system

Abstract

We provide the system. [Solution] An information processing means that inputs task information and evaluates the difficulty and value of the task based on that information, A reward calculation means that calculates rewards based on evaluated task information and issues rewards as digital currency, A database management system for managing transactions of issued digital currencies, A transaction facilitation mechanism that enables users to conduct transactions with other users using digital currency issued by the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern corporate environment, there are problems such as difficulty in accurately evaluating the contribution and performance of individual employees due to uneven distribution of work and mismatches between employees' skills and job contents. In addition, while some employees are overburdened with work, the productivity decline caused by the underutilization of other employees' skills has become a problem. Furthermore, the lack of transparency and fairness in rewards and evaluations is also one of the factors leading to a decline in employees' motivation.

Means for Solving the Problems

[0005] This invention provides an information processing means that takes task information as input and uses artificial intelligence to evaluate the difficulty and value of the task, thereby objectively quantifying each employee's contribution. Furthermore, by using a reward calculation means that issues rewards as digital currency based on that evaluation, it establishes a fair reward system based on work performance. In addition, by introducing a database management means that manages transactions of the issued digital currency and providing a transaction promotion means that enables users to trade with other users using the issued digital currency, it realizes the activation of work requests and skill sharing, and promotes cooperation among employees.

[0006] "Task information" refers to detailed data about the tasks and projects that employees perform in the course of their work.

[0007] "Information processing means" refers to a series of devices or systems that analyze input task information and perform the necessary processing.

[0008] "Difficulty and value" refers to indicators that describe the effort and expertise required to complete a task, as well as the benefits and importance of the outcome for the company.

[0009] Artificial intelligence refers to computational models and algorithms designed to enable computer systems to perform specific tasks efficiently.

[0010] A "reward calculation system" is a mechanism that calculates the rewards given to employees based on the numerical values ​​assigned to their tasks and provides them in digital format.

[0011] "Digital currency" refers to an electronic alternative currency that is awarded to employees based on their performance evaluations and used for internal company transactions.

[0012] A "database management system" is a system for storing and managing data related to the issuance and consumption of digital currency.

[0013] A "transaction facilitation tool" is a platform that functions as a marketplace to support the exchange of work and skills among employees. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention provides a system that enables employees within a company to perform their duties efficiently and fairly and receive compensation. The system begins with the user inputting task information using a terminal. At that time, detailed information such as the task name, required skills, and deadline is entered.

[0036] The server processes the received task information and runs an artificial intelligence model to calculate the difficulty and value of the task. During this process, it refers to past data and the history of similar tasks, and assigns a score using a predetermined algorithm. The assigned score is determined based on the degree of task completion and its importance.

[0037] Next, the server issues rewards to users as digital currency based on their evaluated scores. This digital currency is treated as internal company currency, and users can manage it in their own accounts. The digital currency issued to each user's account is recorded in a database and monitored in real time by the server.

[0038] Users can access the virtual marketplace using their own devices and browse the services and skills offered by other users. Users can initiate transactions as needed, requesting services from other users or receiving support using designated digital currency.

[0039] As a concrete example, when user A completes the task of "designing a new website," artificial intelligence evaluates their contribution and issues 10 units of digital currency to user A. Meanwhile, user B offers the skill of "digital marketing strategy," and when user A seeks this assistance, they pay B with the digital currency they have earned to complete the transaction.

[0040] This entire process consists of elements such as quantifying tasks via information processing tools, issuing digital currency through reward calculation tools, monitoring transaction history through database management tools, and exchanging tasks through transaction promotion tools. This system aims to improve employees' skills and work efficiency, and contributes to fostering a fair corporate culture.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user enters task information from their device. They enter data including task details such as the name, required skills, and deadline into a form and submit it.

[0044] Step 2:

[0045] The terminal sends task information entered by the user to the server. Security protocols are used to ensure data integrity, and the task information is stored in the server's database.

[0046] Step 3:

[0047] When the server receives task information, it invokes an artificial intelligence model to begin evaluating the task. It refers to similar past data to calculate the difficulty and value of the task.

[0048] Step 4:

[0049] The server uses a reward calculation mechanism to determine the appropriate digital currency based on the evaluation score returned by the artificial intelligence model. It then issues this currency to the user's account.

[0050] Step 5:

[0051] The server updates the database with records of issued digital currencies via a database management system. This ensures that currency balances and transaction history are kept up-to-date.

[0052] Step 6:

[0053] Users access a virtual marketplace on their devices and search for publicly available job requests and skill offerings. They then find the tasks and skills they need.

[0054] Step 7:

[0055] The user selects a specific task or skill and initiates the transaction. They then confirm the amount of digital currency required for the transaction and approve it.

[0056] Step 8:

[0057] The server acknowledges the transaction and records in the database that the digital currency has been transferred between users. The account balances are then updated accordingly.

[0058] Step 9:

[0059] Users check their dashboards on their devices to see the digital currency they've earned, their transaction history, and their own contributions. This allows employees to visualize their own performance.

[0060] (Example 1)

[0061] 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."

[0062] Traditional corporate business management systems made it difficult to accurately and efficiently evaluate employees' contributions to work performance, hindering fair compensation distribution based on these evaluations. Furthermore, the lack of transparency in transactions using digital currency made it challenging to foster trust among employees.

[0063] 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.

[0064] In this invention, the server includes an information processing means for receiving and analyzing task information based on user input, a means for quantifying the difficulty and value of the task using a generating AI model based on the analyzed task information, and a reward calculation means for calculating and issuing digital currency as a reward based on the assigned score. This makes it possible to accurately evaluate the work contribution of employees within a company and automatically distribute appropriate rewards. Furthermore, by transparently and efficiently managing digital currency transactions, trust among employees can be improved.

[0065] "Task information" refers to work-related information entered by users using work terminals, and specifically includes a collection of data such as task name, required skills, and deadline.

[0066] A "generative AI model" is an artificial intelligence model used for data analysis and quantifying the difficulty and value of tasks, and is typically learned by utilizing historical databases.

[0067] A "digital currency" is a virtual currency used within a system, and is a unit of value used for reward payments and transactions between users.

[0068] A "transaction facilitation tool" is a tool that has the function of supporting users in trading services or skills with other users using digital currency.

[0069] A "database management system" is a means of recording and maintaining digital currency transactions that occur within a system, and making the history accessible as needed.

[0070] A "reward calculation means" is a means that has the function of calculating and issuing rewards to users as digital currency based on task information analyzed via an information processing means.

[0071] This invention is a system for improving operational efficiency within a company and realizing a fair compensation system. The system includes a terminal operated by the user, a server for data processing, and a database for managing digital currency transactions.

[0072] Users input task information via their terminal. Specifically, users enter the task name, required skills, deadline, etc., and send it to the server. This communication is implemented using a web-based application, and the UI (user interface) is created using programming languages ​​such as Python and JavaScript (registered trademark).

[0073] The server runs a generative AI model using TENSORFLOW® or PyTorch to process the received task information. This model references historical data stored in a database to quantify the difficulty and value of the input task. This quantified information is used to calculate rewards.

[0074] Reward calculation is performed by the server issuing digital currency based on quantified task evaluations. This digital currency can be used for transactions within the company, and the server monitors and manages each transaction via a database.

[0075] Users access a virtual marketplace provided by the system using their devices. Here, users can search for tasks and skills offered by other employees and select transactions that interest them. This virtual marketplace provides a platform for transactions for users and facilitates skill exchange within the company.

[0076] For example, if user A completes the task of "planning a new project," the server uses an AI model to evaluate their contribution and awards user A 20 units of digital currency. If another user B is skilled at "creating marketing strategies," user A can use the acquired digital currency to pay B for their cooperation in order to utilize that skill.

[0077] An example of a prompt might be, "Propose ways to improve project scoring criteria and efficiently operate the company's digital currency system." Following this prompt, the system processes relevant data and provides useful information to the user.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users enter task information into an input form via their terminal. Specifically, they fill in details such as the task name, required skills, and deadline, and then press the submit button. This action sends the entered information to the server. The input data includes task details in text format, which is then directly entered into the server.

[0081] Step 2:

[0082] The server analyzes the received task information. First, it refers to past task data stored in the database and uses a generative AI model to evaluate the difficulty and value of the task. The AI ​​model used here is based on TensorFlow or PyTorch. The server converts the task information into a numerical score through the AI ​​model. As output, a numerical evaluation representing the difficulty and value of the task is obtained.

[0083] Step 3:

[0084] The server calculates the reward based on the score obtained in step 2. Based on the calculated value, it issues digital currency to the user's account. Here, a simple calculation algorithm is used to directly convert the score into units of digital currency and assign it to the user's account. The output is the updated digital currency balance information.

[0085] Step 4:

[0086] Users access a virtual marketplace using a terminal to search for jobs and skills offered by other employees. They utilize search functions for specific skills and price ranges to select deals that interest them. The terminal filters the results based on the user's input and displays the results.

[0087] Step 5:

[0088] The server monitors transactions between users and records digital currency transactions. The database maintains the transaction history and updates it in real time. It notifies users and administrators of the transaction status as needed. The output is the updated transaction history.

[0089] Step 6:

[0090] Users can view their digital currency balance and transaction history on their device. This allows them to understand the overall picture of their activity in the virtual market and helps them develop future trading strategies. The data is displayed in a user-friendly dashboard format.

[0091] (Application Example 1)

[0092] 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."

[0093] Traditional business management systems have often presented problems with opacity and unfairness in ensuring employees perform tasks efficiently and fairly and receive adequate compensation. Furthermore, a lack of effective methods for exchanging and sharing skills and work among employees hindered overall organizational productivity improvements. To address these issues, there is a need to implement a transparent and efficient business management and compensation system using digital currency.

[0094] 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.

[0095] This invention includes a server comprising: an information processing means for inputting task information and evaluating the difficulty and value of the task based thereon; a reward calculation means for calculating a reward based on the evaluated task information and issuing the reward as digital currency; a database management means for managing transactions of the issued digital currency; a terminal means for performing task management using a smartphone application; and a virtual market means for supporting the smooth exchange of skills and work between users. As a result, employees can have their skills and tasks evaluated fairly and transparently, and can receive rewards and exchange work efficiently via digital currency.

[0096] "Task information" refers to detailed data related to a specific task that a user is performing, including the task name, required skills, and deadline.

[0097] "Difficulty level" is a quantitative indicator that represents the degree of technical or laborious challenge a task involves.

[0098] "Value" is a numerical value or standard used to evaluate the extent to which a task contributes to or benefits an organization when it is completed.

[0099] An "information processing means" is a system that automatically determines and analyzes the difficulty and value of a task based on task information using a computer.

[0100] "Reward calculation method" refers to the process or configuration for calculating the amount of digital currency to be awarded to a user based on evaluated task information.

[0101] "Digital currency" is a form of currency that is issued and managed electronically and traded between users based on the evaluation of tasks.

[0102] A "database management system" is a system or mechanism for recording and maintaining the circulation status and transaction history of issued digital currencies.

[0103] A "transaction facilitation tool" is an environment or platform that allows users to exchange tasks and skills with other users using digital currency.

[0104] A "smartphone application" is a program that allows users to manage tasks and trade digital currencies via their mobile devices.

[0105] "Terminal means" refers to computer equipment or devices used for task management or reward verification.

[0106] A "virtual marketplace" is a virtual marketplace where users can exchange or trade skills and services online.

[0107] This invention provides a system that allows company employees to perform their duties efficiently and fairly using a smartphone application. The server receives task information entered by the user and uses artificial intelligence technology to evaluate the difficulty and value of the task based on that information. Specifically, generative AI models such as TensorFlow support this evaluation.

[0108] Once task evaluation is complete, the server uses a reward calculation mechanism to determine the amount of digital currency based on the evaluation results and issues it to the user's account. This digital currency is for internal use only, and users can use it to trade with other users and exchange skills in the application's virtual marketplace via their smartphones. Transaction details and currency flow are managed and stored / updated in a database using MongoDB.

[0109] The device allows users to check task status and earned rewards in real time. Furthermore, the smartphone application provides users with an interface and an environment for sharing work and responsibilities in the market as a means of facilitating transactions.

[0110] As a concrete example, if an employee is assigned a design task for a new project, they input information about the task into an application, which allows the AI ​​to evaluate its importance and difficulty, and then issues a digital currency tailored to the employee. This currency can then be used to purchase digital marketing support from other employees.

[0111] A concrete example of a prompt statement is as follows:

[0112] "Please describe the process of designing a task management app using a cloud platform and issuing a custom currency as a reward. Also, please include a case study demonstrating a user-friendly UI in that process."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user opens the application on their smartphone and enters task information. A form is displayed for entering detailed information such as the task name, required skills, and deadline, which the user fills out. The entered information is then sent to the server.

[0116] Step 2:

[0117] The server analyzes the received task information. Specifically, a generative AI model using TensorFlow calculates the task difficulty and value based on past data and the history of similar tasks. This calculation is performed by vectorizing the input task information and feeding it to the model, resulting in the output of a task difficulty score and value score.

[0118] Step 3:

[0119] The server issues digital currency to users using a reward calculation mechanism based on their evaluated score. Here, the reward amount is determined according to the difficulty and value, and this is credited to the user's account as digital currency. Organizational policies may be reflected in the setting of reward ratios.

[0120] Step 4:

[0121] The issued digital currency and task evaluation results are stored in a database management system using MongoDB. The database updates transaction history and currency circulation in real time, and securely stores and manages the data.

[0122] Step 5:

[0123] The device displays an interface that allows the user to check their digital currency balance and task evaluation status. Here, users can monitor their progress and rewards. The interface is designed using React Native and is intended to be intuitive to use.

[0124] Step 6:

[0125] Users can use the digital currency they earn to trade skills and services with other users in the application's virtual marketplace. Here, a list of skills offered by users is displayed, and users can select the necessary support and pay with digital currency.

[0126] 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.

[0127] This invention aims to improve employee productivity and the work environment by combining a task management system with an emotion engine that recognizes user emotions. In this system, the user inputs task information using a terminal, and based on that information, the server uses artificial intelligence to evaluate the difficulty and value of the task. In addition, the emotion engine analyzes the user's emotions in real time and transmits that data to the server.

[0128] The server calculates rewards based on acquired emotional data, incorporating it into traditional evaluations. These rewards are issued as digital currency to the user's account, which the user can then use for internal company transactions. By taking into account the user's psychological state and motivation as indicated by the emotional data, the fairness of rewards increases, leading to improved employee motivation.

[0129] For example, if User A completes the task of "creating a project report" and a higher-than-usual stress level is detected, the emotion engine analyzes this information and transmits it to the server. Based on this data, the server adjusts the evaluation of User A's contribution more appropriately by issuing additional digital currency in addition to the usual reward.

[0130] The feedback users receive through the emotion engine on their devices provides insights into their contributions and areas for improvement based on their own feelings, helping them adjust their approach to future tasks. Furthermore, by analyzing the team's overall emotion data, the server suggests optimal task redistributions, and users can receive this information through their devices.

[0131] In this way, by integrating an emotion engine into the system, the aim is to improve the working environment for employees by taking into account psychological factors that were lacking in conventional performance evaluation systems. This invention provides an implementation model that achieves effective task evaluation and reward distribution after selecting complex social factors.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user enters task information from their device. They input the task name, required skills, deadline, etc., through the device's interface and send it to the system.

[0135] Step 2:

[0136] The device collects user emotional data. Using sensors and software built into the device, it analyzes facial expressions, voice tone, input speed, and other factors in real time.

[0137] Step 3:

[0138] The server receives task information and activates the artificial intelligence model. It references past task data to quantify the difficulty and value of the input task.

[0139] Step 4:

[0140] The server analyzes emotional data transmitted from the terminal. From the collected data, it calculates the user's stress level and motivation indicators, and uses these factors to evaluate tasks.

[0141] Step 5:

[0142] The server integrates task evaluation scores and sentiment data, and uses a reward calculation mechanism to determine the digital currency. The reward is issued to the user's account.

[0143] Step 6:

[0144] The server records digital currency issued via a database management system into the database and updates the account balance. This information is used to monitor user transactions.

[0145] Step 7:

[0146] Users access a virtual marketplace via their devices. They search for job requests and skills offered by other users and conduct transactions using their own digital currency.

[0147] Step 8:

[0148] The server records transaction details and adjusts digital currency between users based on the transaction results. The transaction history in the database is updated.

[0149] Step 9:

[0150] Users can view their device dashboard to visualize their transaction history and performance, including their own sentiment data. This information can then be used to improve how they approach future tasks.

[0151] (Example 2)

[0152] 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".

[0153] In modern workplaces, task evaluation and compensation are often based on simple productivity metrics, resulting in a problem where psychological factors are not adequately considered. This leads to a lack of reflection of employee stress and motivation, making it difficult to improve the work environment and increase employee productivity.

[0154] 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.

[0155] In this invention, the server includes information processing means that input user behavior information and evaluate the difficulty and value of the behavior based on that information; emotion recognition means that analyze the user's emotions and transmit them in real time; and reward adjustment means that incorporate the acquired emotion data into the conventional evaluation. This makes it possible to determine rewards more fairly and effectively by taking into account psychological factors such as emotions.

[0156] "User" refers to an individual or organization that operates the system, inputs information, or uses information.

[0157] "Behavioral information" refers to data related to tasks and activities performed by the user, specifically including the content, deadline, and importance of the tasks.

[0158] "Information processing means" refers to a device or program that has the function of evaluating, analyzing, and quantifying the content of input behavioral information.

[0159] "Digital format" refers to a method of representing evaluation and reward calculation results as electronic data.

[0160] "Reward calculation means" refers to a device or program that has the function of calculating the amount of reward to be earned by the user based on data obtained from information processing means.

[0161] "Record management means" refers to equipment or programs that have the function of saving and managing the exchange history in digital format that has been issued.

[0162] "Exchange facilitator" refers to a device or program that has the function of enabling a user to conduct transactions with other users using a digital format.

[0163] "Emotion recognition means" refers to a device or program that has the function of analyzing the user's psychological state and transmitting the results as data in real time.

[0164] "Reward adjustment mechanism" refers to a device or program that has the function of recalculating fair rewards by modifying conventional evaluation criteria in consideration of emotional data.

[0165] This invention is a system that integrates task management and reward systems in an office environment, providing improvement measures that also take into account the feelings of employees.

[0166] The user first enters information about the task they are working on via a terminal. The terminal has task management software installed, allowing the user to input information such as the task name, deadline, and importance level. This information is then transmitted digitally to the server.

[0167] The server processes the received task information. Specifically, it uses data analysis software such as Python or R, and generative AI models utilizing TensorFlow or PyTorch, to quantify and evaluate the difficulty and value of the input task information. Based on this evaluation, the server prepares to perform the subsequent reward calculation.

[0168] The device uses its built-in camera and microphone to capture the user's facial expressions and voice information into an emotion engine in order to analyze the user's emotions. The emotion engine is designed to analyze the user's emotional state in real time and immediately transmits the obtained data to the server.

[0169] The server uses not only task information but also user sentiment data to ensure fair reward adjustments. This process incorporates reward adjustment mechanisms that appropriately consider the impact of sentiment data on reward calculations. Finally, the calculated reward is issued digitally to the user's account.

[0170] As a concrete example, consider a scenario where a user enters "Create an important project report" as a task, and upon completion, a high stress level is detected. In this case, the server considers not only the task evaluation but also the emotional data, and provides additional digital rewards to appropriately evaluate the user's efforts.

[0171] An example of a prompt for a generative AI model is: "If a user experiences high stress while creating a project report, how does the emotion engine analyze this information and notify the server? Furthermore, how does it adjust the reward based on this information?"

[0172] This system makes it possible to create a better working environment that takes psychological factors into consideration.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] Users enter task information (e.g., task name, due date, importance) using a task management app on their device. This information is converted into digital data by the device and sent to the server. The input data reflects the user's current work status and serves as the foundation for starting the entire system's processing.

[0176] Step 2:

[0177] The server receives task information submitted by the user and analyzes its contents using a generative AI model. Specifically, it analyzes the input data using an information processing system and quantifies the difficulty and value of the task. In this process, an AI model using TensorFlow evaluates the task information and outputs it as numerical data. This numerical data is used as the basis for calculating rewards.

[0178] Step 3:

[0179] The device records the user's facial expressions and voice through its built-in camera and microphone to analyze the user's emotions in real time, and sends this data to an emotion recognition engine. The input biometric data is rapidly processed to calculate the user's current emotional state (e.g., stress level, happiness level), and the results are sent to a server. This data is used in the reward determination process.

[0180] Step 4:

[0181] The server integrates task evaluation results and user sentiment data to calculate the final reward. The reward calculation mechanism adjusts the reward based on factors such as stress reduction. For example, completing a high-difficulty task under high stress levels will result in additional digital rewards. This output is reflected in the user's account as a digital reward.

[0182] Step 5:

[0183] Users receive feedback sent from the server via their device. This feedback includes insights generated based on the user's emotional data and is displayed visually. This allows users to see their own contributions and gain clues to improve their approach to future tasks. This output provides an opportunity for emotionally-based self-assessment.

[0184] (Application Example 2)

[0185] 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".

[0186] In today's work environment, the impact of employees' emotional states on work efficiency and results cannot be ignored. However, existing task management systems lack mechanisms to reflect individual emotional states in evaluations, making it difficult to achieve fair and effective task evaluation and reward distribution. Therefore, there is a growing need for systems that redistribute tasks and rewards while taking emotional states into consideration.

[0187] 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.

[0188] In this invention, the server includes an information processing means that inputs task information and evaluates the difficulty and value of the task based on that information; a reward calculation means that calculates a reward based on the evaluated task information and issues it as digital currency; and an emotion analysis means that acquires the user's emotional state and optimizes the task execution order based on that data. This enables efficient and fair task evaluation and reward distribution that takes the emotional state into account.

[0189] "Task information" refers to detailed data related to a specific task or activity, and is used to evaluate its difficulty and value.

[0190] An "information processing means" is a system component that analyzes input task information and determines its difficulty level and value based on evaluation criteria.

[0191] A "reward calculation device" is a system that has the function of appropriately calculating the reward for the user based on the evaluated task information and issuing it as digital currency.

[0192] A "database management system" is a system element for recording and managing the transaction history of issued digital currencies and transactions between users.

[0193] A "transaction facilitation mechanism" is a system that supports users in conducting transactions with other users using the digital currency that has been issued.

[0194] "Emotional state" refers to the emotional and psychological status that an individual is experiencing at a particular point in time.

[0195] An "emotion analysis tool" is a system component that acquires and analyzes a user's emotional state in real time and uses the results to optimize tasks.

[0196] The system used to realize this application optimizes the tasks of factory robots by taking into account the emotional state of the operator, in order to improve work efficiency in the factory.

[0197] The server collects task information and operator emotion data acquired within the factory, and uses this data to evaluate tasks and perform emotion analysis. Information processing tools are used to process task information, and an AI model quantifies the difficulty and value of tasks. Emotion analysis tools analyze operator emotion data transmitted from devices such as smart glasses in real time to evaluate the operator's stress level and fatigue.

[0198] The terminal collects data from devices worn by the user, such as smart glasses, and transfers it to the server. This utilizes facial recognition sensors and heart rate sensors. Based on the obtained emotional data, the server uses emotion analysis tools to optimally redistribute tasks and adjusts the robot's tasks in a way that reduces the user's physical and mental burden.

[0199] For example, if it is detected that an operator is experiencing high stress while assembling parts, this information is immediately transmitted to the server. The server then takes this emotional state into consideration and makes adjustments, such as assigning the factory robot a different, less demanding task. This helps maintain the operator's work efficiency and motivation.

[0200] A generative AI model is used, and an example of a prompt in this context is as follows:

[0201] "If an operator's emotional data indicates high stress levels, please explain how you would optimally redistribute tasks."

[0202] This enables the system to create an efficient and fair work environment that takes emotional states into consideration.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The device uses sensors in smart glasses to acquire physiological data such as the operator's facial expressions and heart rate. This data is used as input to evaluate their emotional state.

[0206] Step 2:

[0207] The device sends acquired physiological data to a server for emotion analysis. The server receives this data and uses a generative AI model to analyze the emotional state in real time. The output here is the specific emotional state and its intensity.

[0208] Step 3:

[0209] The server uses the results of the emotional state analysis to compare them with information about the tasks currently being performed. This task information includes data previously entered by the user, such as the task content and difficulty level. This provides the data needed to determine the optimal task execution order.

[0210] Step 4:

[0211] The server redistributes and adjusts tasks based on task difficulty, emotional state, and other relevant data. The output of this process is optimized task assignment information for each worker.

[0212] Step 5:

[0213] The server transmits information about the reassigned tasks to the factory robots, which then operate based on those instructions. The factory robots execute the instructions and perform the tasks appropriately. This is expected to reduce operator stress and improve work efficiency.

[0214] Step 6:

[0215] Users receive feedback from the server through smart glasses, gaining information about task progress and their own emotional state. This makes it easier for individual workers to manage their psychological and physical burdens.

[0216] 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.

[0217] 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 those described above. 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 shown 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This invention provides a system that enables employees within a company to perform their duties efficiently and fairly and receive compensation. The system begins with the user inputting task information using a terminal. At that time, detailed information such as the task name, required skills, and deadline is entered.

[0233] The server processes the received task information and runs an artificial intelligence model to calculate the difficulty and value of the task. During this process, it refers to past data and the history of similar tasks, and assigns a score using a predetermined algorithm. The assigned score is determined based on the degree of task completion and its importance.

[0234] Next, the server issues rewards to users as digital currency based on their evaluated scores. This digital currency is treated as internal company currency, and users can manage it in their own accounts. The digital currency issued to each user's account is recorded in a database and monitored in real time by the server.

[0235] Users can access the virtual marketplace using their own devices and browse the services and skills offered by other users. Users can initiate transactions as needed, requesting services from other users or receiving support using designated digital currency.

[0236] As a concrete example, when user A completes the task of "designing a new website," artificial intelligence evaluates their contribution and issues 10 units of digital currency to user A. Meanwhile, user B offers the skill of "digital marketing strategy," and when user A seeks this assistance, they pay B with the digital currency they have earned to complete the transaction.

[0237] This entire process consists of elements such as quantifying tasks via information processing tools, issuing digital currency through reward calculation tools, monitoring transaction history through database management tools, and exchanging tasks through transaction promotion tools. This system aims to improve employees' skills and work efficiency, and contributes to fostering a fair corporate culture.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user enters task information from their device. They enter data including task details such as the name, required skills, and deadline into a form and submit it.

[0241] Step 2:

[0242] The terminal sends task information entered by the user to the server. Security protocols are used to ensure data integrity, and the task information is stored in the server's database.

[0243] Step 3:

[0244] When the server receives task information, it invokes an artificial intelligence model to begin evaluating the task. It refers to similar past data to calculate the difficulty and value of the task.

[0245] Step 4:

[0246] The server uses a reward calculation mechanism to determine the appropriate digital currency based on the evaluation score returned by the artificial intelligence model. It then issues this currency to the user's account.

[0247] Step 5:

[0248] The server updates the database with records of issued digital currencies via a database management system. This ensures that currency balances and transaction history are kept up-to-date.

[0249] Step 6:

[0250] Users access a virtual marketplace on their devices and search for publicly available job requests and skill offerings. They then find the tasks and skills they need.

[0251] Step 7:

[0252] The user selects a specific task or skill and initiates the transaction. They then confirm the amount of digital currency required for the transaction and approve it.

[0253] Step 8:

[0254] The server acknowledges the transaction and records in the database that the digital currency has been transferred between users. The account balances are then updated accordingly.

[0255] Step 9:

[0256] Users check their dashboards on their devices to see the digital currency they've earned, their transaction history, and their own contributions. This allows employees to visualize their own performance.

[0257] (Example 1)

[0258] 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."

[0259] Traditional corporate business management systems made it difficult to accurately and efficiently evaluate employees' contributions to work performance, hindering fair compensation distribution based on these evaluations. Furthermore, the lack of transparency in transactions using digital currency made it challenging to foster trust among employees.

[0260] 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.

[0261] In this invention, the server includes an information processing means for receiving and analyzing task information based on user input, a means for quantifying the difficulty and value of the task using a generating AI model based on the analyzed task information, and a reward calculation means for calculating and issuing digital currency as a reward based on the assigned score. This makes it possible to accurately evaluate the work contribution of employees within a company and automatically distribute appropriate rewards. Furthermore, by transparently and efficiently managing digital currency transactions, trust among employees can be improved.

[0262] "Task information" refers to work-related information entered by users using work terminals, and specifically includes a collection of data such as task name, required skills, and deadline.

[0263] A "generative AI model" is an artificial intelligence model used for data analysis and quantifying the difficulty and value of tasks, and is typically learned by utilizing historical databases.

[0264] A "digital currency" is a virtual currency used within a system, and is a unit of value used for reward payments and transactions between users.

[0265] A "transaction facilitation tool" is a tool that has the function of supporting users in trading services or skills with other users using digital currency.

[0266] A "database management system" is a means of recording and maintaining digital currency transactions that occur within a system, and making the history accessible as needed.

[0267] A "reward calculation means" is a means that has the function of calculating and issuing rewards to users as digital currency based on task information analyzed via an information processing means.

[0268] This invention is a system for improving operational efficiency within a company and realizing a fair compensation system. The system includes a terminal operated by the user, a server for data processing, and a database for managing digital currency transactions.

[0269] Users input task information via their terminal. Specifically, users enter the task name, required skills, deadline, etc., and send it to the server. This communication is implemented using a web-based application, and the UI (user interface) is created using programming languages ​​such as Python and JavaScript.

[0270] The server runs a generative AI model using TensorFlow or PyTorch to process the received task information. This model references historical data stored in a database to quantify the difficulty and value of the input task. This quantified information is used to calculate the reward.

[0271] Reward calculation is performed by the server issuing digital currency based on quantified task evaluations. This digital currency can be used for transactions within the company, and the server monitors and manages each transaction via a database.

[0272] Users access a virtual marketplace provided by the system using their devices. Here, users can search for tasks and skills offered by other employees and select transactions that interest them. This virtual marketplace provides a platform for transactions for users and facilitates skill exchange within the company.

[0273] For example, if user A completes the task of "planning a new project," the server uses an AI model to evaluate their contribution and awards user A 20 units of digital currency. If another user B is skilled at "creating marketing strategies," user A can use the acquired digital currency to pay B for their cooperation in order to utilize that skill.

[0274] An example of a prompt might be, "Propose ways to improve project scoring criteria and efficiently operate the company's digital currency system." Following this prompt, the system processes relevant data and provides useful information to the user.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The user inputs task information into the business-related input form through the terminal. Specifically, the user fills in details such as the task name, required skills, due date, etc., and presses the send button. By this operation, the input information is sent to the server. The input data includes task details in text format, which directly serves as the input to the server.

[0278] Step 2:

[0279] The server analyzes the received task information. First, it refers to the past task data stored in the database and uses the generated AI model to evaluate the difficulty and value of the task. The AI model used here is based on TensorFlow or PyTorch. The server converts the task information into numerical scores through the AI model. As output, a numerical evaluation representing the difficulty and value of the task is obtained.

[0280] Step 3:

[0281] The server calculates the reward based on the score obtained in Step 2. Based on the calculated numerical value, digital currency is issued to the user's account. Here, a simple calculation algorithm is used to directly convert the score into the unit of digital currency and grant it to the user's account. The output is the updated balance information of the digital currency.

[0282] Step 4:

[0283] The user uses the terminal to access the virtual market and search for the services and skills provided by other employees. Here, in order to select transactions that the user is interested in, the search function for specific skills and price ranges is utilized. The terminal performs filtering based on the user's input and displays the results.

[0284] Step 5:

[0285] The server monitors transactions conducted among users and records digital currency transactions. The database holds the transaction history and updates it in real time. It notifies users and administrators of the transaction status as needed. The output is the updated transaction history.

[0286] Step 6:

[0287] Users can check their digital currency balances and transaction histories on their terminals. This enables them to grasp the overall picture of their activities in the virtual market and helps them formulate future trading strategies. The data is presented in a user-friendly dashboard format.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] In conventional business management systems, there were sometimes problems of opacity and injustice when employees performed tasks efficiently and fairly and received rewards. Also, there was a lack of effective ways to exchange and share skills and work among employees, which hindered the improvement of productivity across the organization. It is necessary to solve these problems and realize a transparent and efficient business management and reward system using digital currency.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0292] This invention includes a server comprising: an information processing means for inputting task information and evaluating the difficulty and value of the task based thereon; a reward calculation means for calculating a reward based on the evaluated task information and issuing the reward as digital currency; a database management means for managing transactions of the issued digital currency; a terminal means for performing task management using a smartphone application; and a virtual market means for supporting the smooth exchange of skills and work between users. As a result, employees can have their skills and tasks evaluated fairly and transparently, and can receive rewards and exchange work efficiently via digital currency.

[0293] "Task information" refers to detailed data related to a specific task that a user is performing, including the task name, required skills, and deadline.

[0294] "Difficulty level" is a quantitative indicator that represents the degree of technical or laborious challenge a task involves.

[0295] "Value" is a numerical value or standard used to evaluate the extent to which a task contributes to or benefits an organization when it is completed.

[0296] An "information processing means" is a system that automatically determines and analyzes the difficulty and value of a task based on task information using a computer.

[0297] "Reward calculation method" refers to the process or configuration for calculating the amount of digital currency to be awarded to a user based on evaluated task information.

[0298] "Digital currency" is a form of currency that is issued and managed electronically and traded between users based on the evaluation of tasks.

[0299] A "database management system" is a system or mechanism for recording and maintaining the circulation status and transaction history of issued digital currencies.

[0300] A "transaction facilitation tool" is an environment or platform that allows users to exchange tasks and skills with other users using digital currency.

[0301] A "smartphone application" is a program that allows users to manage tasks and trade digital currencies via their mobile devices.

[0302] "Terminal means" refers to computer equipment or devices used for task management or reward verification.

[0303] A "virtual marketplace" is a virtual marketplace where users can exchange or trade skills and services online.

[0304] This invention provides a system that allows company employees to perform their duties efficiently and fairly using a smartphone application. The server receives task information entered by the user and uses artificial intelligence technology to evaluate the difficulty and value of the task based on that information. Specifically, generative AI models such as TensorFlow support this evaluation.

[0305] Once task evaluation is complete, the server uses a reward calculation mechanism to determine the amount of digital currency based on the evaluation results and issues it to the user's account. This digital currency is for internal use only, and users can use it to trade with other users and exchange skills in the application's virtual marketplace via their smartphones. Transaction details and currency flow are managed and stored / updated in a database using MongoDB.

[0306] The device allows users to check task status and earned rewards in real time. Furthermore, the smartphone application provides users with an interface and an environment for sharing work and responsibilities in the market as a means of facilitating transactions.

[0307] As a specific example, when a certain employee is in charge of the design task of a new project, by inputting the information of that task into the application, the AI evaluates its importance and difficulty level, and issues digital currency according to the employee. Using this, other employees can purchase digital marketing support from each other.

[0308] Specific examples of the prompt text are as follows.

[0309] "Please teach me the process of designing a task management application using a cloud platform and issuing original currency as a reward. Also, please include a case study with a user-friendly UI in that process."

[0310] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0311] Step 1:

[0312] The user opens the application on the smartphone and inputs task information. At this time, a form for inputting detailed information such as the task name, required technology, deadline, etc. is displayed, and the user fills it in. The input information is sent to the server.

[0313] Step 2:

[0314] The server analyzes the received task information. Specifically, using a generative AI model based on TensorFlow, the difficulty level and value of the task are calculated based on past data and the history of similar tasks. This calculation is performed by vectorizing the input task information and providing it as an input to the model, and as a result, the difficulty score and value score of the task are output.

[0315] Step 3:

[0316] The server issues digital currency to users using a reward calculation mechanism based on their evaluated score. Here, the reward amount is determined according to the difficulty and value, and this is credited to the user's account as digital currency. Organizational policies may be reflected in the setting of reward ratios.

[0317] Step 4:

[0318] The issued digital currency and task evaluation results are stored in a database management system using MongoDB. The database updates transaction history and currency circulation in real time, and securely stores and manages the data.

[0319] Step 5:

[0320] The device displays an interface that allows the user to check their digital currency balance and task evaluation status. Here, users can monitor their progress and rewards. The interface is designed using React Native and is intended to be intuitive to use.

[0321] Step 6:

[0322] Users can use the digital currency they earn to trade skills and services with other users in the application's virtual marketplace. Here, a list of skills offered by users is displayed, and users can select the necessary support and pay with digital currency.

[0323] 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.

[0324] This invention aims to improve employee productivity and the work environment by combining a task management system with an emotion engine that recognizes user emotions. In this system, the user inputs task information using a terminal, and based on that information, the server uses artificial intelligence to evaluate the difficulty and value of the task. In addition, the emotion engine analyzes the user's emotions in real time and transmits that data to the server.

[0325] The server calculates rewards based on acquired emotional data, incorporating it into traditional evaluations. These rewards are issued as digital currency to the user's account, which the user can then use for internal company transactions. By taking into account the user's psychological state and motivation as indicated by the emotional data, the fairness of rewards increases, leading to improved employee motivation.

[0326] For example, if User A completes the task of "creating a project report" and a higher-than-usual stress level is detected, the emotion engine analyzes this information and transmits it to the server. Based on this data, the server adjusts the evaluation of User A's contribution more appropriately by issuing additional digital currency in addition to the usual reward.

[0327] The feedback users receive through the emotion engine on their devices provides insights into their contributions and areas for improvement based on their own feelings, helping them adjust their approach to future tasks. Furthermore, by analyzing the team's overall emotion data, the server suggests optimal task redistributions, and users can receive this information through their devices.

[0328] In this way, by integrating an emotion engine into the system, the aim is to improve the working environment for employees by taking into account psychological factors that were lacking in conventional performance evaluation systems. This invention provides an implementation model that achieves effective task evaluation and reward distribution after selecting complex social factors.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The user enters task information from their device. They input the task name, required skills, deadline, etc., through the device's interface and send it to the system.

[0332] Step 2:

[0333] The device collects user emotional data. Using sensors and software built into the device, it analyzes facial expressions, voice tone, input speed, and other factors in real time.

[0334] Step 3:

[0335] The server receives task information and activates the artificial intelligence model. It references past task data to quantify the difficulty and value of the input task.

[0336] Step 4:

[0337] The server analyzes emotional data transmitted from the terminal. From the collected data, it calculates the user's stress level and motivation indicators, and uses these factors to evaluate tasks.

[0338] Step 5:

[0339] The server integrates task evaluation scores and sentiment data, and uses a reward calculation mechanism to determine the digital currency. The reward is issued to the user's account.

[0340] Step 6:

[0341] The server records digital currency issued via a database management system into the database and updates the account balance. This information is used to monitor user transactions.

[0342] Step 7:

[0343] Users access a virtual marketplace via their devices. They search for job requests and skills offered by other users and conduct transactions using their own digital currency.

[0344] Step 8:

[0345] The server records transaction details and adjusts digital currency between users based on the transaction results. The transaction history in the database is updated.

[0346] Step 9:

[0347] Users can view their device dashboard to visualize their transaction history and performance, including their own sentiment data. This information can then be used to improve how they approach future tasks.

[0348] (Example 2)

[0349] 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".

[0350] In modern workplaces, task evaluation and compensation are often based on simple productivity metrics, resulting in a problem where psychological factors are not adequately considered. This leads to a lack of reflection of employee stress and motivation, making it difficult to improve the work environment and increase employee productivity.

[0351] 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.

[0352] In this invention, the server includes information processing means that input user behavior information and evaluate the difficulty and value of the behavior based on that information; emotion recognition means that analyze the user's emotions and transmit them in real time; and reward adjustment means that incorporate the acquired emotion data into the conventional evaluation. This makes it possible to determine rewards more fairly and effectively by taking into account psychological factors such as emotions.

[0353] "User" refers to an individual or organization that operates the system, inputs information, or uses information.

[0354] "Behavioral information" refers to data related to tasks and activities performed by the user, specifically including the content, deadline, and importance of the tasks.

[0355] "Information processing means" refers to a device or program that has the function of evaluating, analyzing, and quantifying the content of input behavioral information.

[0356] "Digital format" refers to a method of representing evaluation and reward calculation results as electronic data.

[0357] "Reward calculation means" refers to a device or program that has the function of calculating the amount of reward to be earned by the user based on data obtained from information processing means.

[0358] "Record management means" refers to equipment or programs that have the function of saving and managing the exchange history in digital format that has been issued.

[0359] "Exchange facilitator" refers to a device or program that has the function of enabling a user to conduct transactions with other users using a digital format.

[0360] "Emotion recognition means" refers to a device or program that has the function of analyzing the user's psychological state and transmitting the results as data in real time.

[0361] "Reward adjustment mechanism" refers to a device or program that has the function of recalculating fair rewards by modifying conventional evaluation criteria in consideration of emotional data.

[0362] This invention is a system that integrates task management and reward systems in an office environment, providing improvement measures that also take into account the feelings of employees.

[0363] The user first enters information about the task they are working on via a terminal. The terminal has task management software installed, allowing the user to input information such as the task name, deadline, and importance level. This information is then transmitted digitally to the server.

[0364] The server processes the received task information. Specifically, it uses data analysis software such as Python or R, and generative AI models utilizing TensorFlow or PyTorch, to quantify and evaluate the difficulty and value of the input task information. Based on this evaluation, the server prepares to perform the subsequent reward calculation.

[0365] The device uses its built-in camera and microphone to capture the user's facial expressions and voice information into an emotion engine in order to analyze the user's emotions. The emotion engine is designed to analyze the user's emotional state in real time and immediately transmits the obtained data to the server.

[0366] The server uses not only task information but also user sentiment data to ensure fair reward adjustments. This process incorporates reward adjustment mechanisms that appropriately consider the impact of sentiment data on reward calculations. Finally, the calculated reward is issued digitally to the user's account.

[0367] As a concrete example, consider a scenario where a user enters "Create an important project report" as a task, and upon completion, a high stress level is detected. In this case, the server considers not only the task evaluation but also the emotional data, and provides additional digital rewards to appropriately evaluate the user's efforts.

[0368] An example of a prompt for a generative AI model is: "If a user experiences high stress while creating a project report, how does the emotion engine analyze this information and notify the server? Furthermore, how does it adjust the reward based on this information?"

[0369] This system makes it possible to create a better working environment that takes psychological factors into consideration.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] Users enter task information (e.g., task name, due date, importance) using a task management app on their device. This information is converted into digital data by the device and sent to the server. The input data reflects the user's current work status and serves as the foundation for starting the entire system's processing.

[0373] Step 2:

[0374] The server receives task information submitted by the user and analyzes its contents using a generative AI model. Specifically, it analyzes the input data using an information processing system and quantifies the difficulty and value of the task. In this process, an AI model using TensorFlow evaluates the task information and outputs it as numerical data. This numerical data is used as the basis for calculating rewards.

[0375] Step 3:

[0376] The device records the user's facial expressions and voice through its built-in camera and microphone to analyze the user's emotions in real time, and sends this data to an emotion recognition engine. The input biometric data is rapidly processed to calculate the user's current emotional state (e.g., stress level, happiness level), and the results are sent to a server. This data is used in the reward determination process.

[0377] Step 4:

[0378] The server integrates task evaluation results and user sentiment data to calculate the final reward. The reward calculation mechanism adjusts the reward based on factors such as stress reduction. For example, completing a high-difficulty task under high stress levels will result in additional digital rewards. This output is reflected in the user's account as a digital reward.

[0379] Step 5:

[0380] Users receive feedback sent from the server via their device. This feedback includes insights generated based on the user's emotional data and is displayed visually. This allows users to see their own contributions and gain clues to improve their approach to future tasks. This output provides an opportunity for emotionally-based self-assessment.

[0381] (Application Example 2)

[0382] 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."

[0383] In today's work environment, the impact of employees' emotional states on work efficiency and results cannot be ignored. However, existing task management systems lack mechanisms to reflect individual emotional states in evaluations, making it difficult to achieve fair and effective task evaluation and reward distribution. Therefore, there is a growing need for systems that redistribute tasks and rewards while taking emotional states into consideration.

[0384] 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.

[0385] In this invention, the server includes an information processing means that inputs task information and evaluates the difficulty and value of the task based on that information; a reward calculation means that calculates a reward based on the evaluated task information and issues it as digital currency; and an emotion analysis means that acquires the user's emotional state and optimizes the task execution order based on that data. This enables efficient and fair task evaluation and reward distribution that takes the emotional state into account.

[0386] "Task information" refers to detailed data related to a specific task or activity, and is used to evaluate its difficulty and value.

[0387] An "information processing means" is a system component that analyzes input task information and determines its difficulty level and value based on evaluation criteria.

[0388] A "reward calculation device" is a system that has the function of appropriately calculating the reward for the user based on the evaluated task information and issuing it as digital currency.

[0389] A "database management system" is a system element for recording and managing the transaction history of issued digital currencies and transactions between users.

[0390] A "transaction facilitation mechanism" is a system that supports users in conducting transactions with other users using the digital currency that has been issued.

[0391] "Emotional state" refers to the emotional and psychological status that an individual is experiencing at a particular point in time.

[0392] An "emotion analysis tool" is a system component that acquires and analyzes a user's emotional state in real time and uses the results to optimize tasks.

[0393] The system used to realize this application optimizes the tasks of factory robots by taking into account the emotional state of the operator, in order to improve work efficiency in the factory.

[0394] The server collects task information and operator emotion data acquired within the factory, and uses this data to evaluate tasks and perform emotion analysis. Information processing tools are used to process task information, and an AI model quantifies the difficulty and value of tasks. Emotion analysis tools analyze operator emotion data transmitted from devices such as smart glasses in real time to evaluate the operator's stress level and fatigue.

[0395] The terminal collects data from devices worn by the user, such as smart glasses, and transfers it to the server. This utilizes facial recognition sensors and heart rate sensors. Based on the obtained emotional data, the server uses emotion analysis tools to optimally redistribute tasks and adjusts the robot's tasks in a way that reduces the user's physical and mental burden.

[0396] For example, if it is detected that an operator is experiencing high stress while assembling parts, this information is immediately transmitted to the server. The server then takes this emotional state into consideration and makes adjustments, such as assigning the factory robot a different, less demanding task. This helps maintain the operator's work efficiency and motivation.

[0397] A generative AI model is used, and an example of a prompt in this context is as follows:

[0398] "If an operator's emotional data indicates high stress levels, please explain how you would optimally redistribute tasks."

[0399] This enables the system to create an efficient and fair work environment that takes emotional states into consideration.

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The device uses sensors in smart glasses to acquire physiological data such as the operator's facial expressions and heart rate. This data is used as input to evaluate their emotional state.

[0403] Step 2:

[0404] The device sends acquired physiological data to a server for emotion analysis. The server receives this data and uses a generative AI model to analyze the emotional state in real time. The output here is the specific emotional state and its intensity.

[0405] Step 3:

[0406] The server uses the results of the emotional state analysis to compare them with information about the tasks currently being performed. This task information includes data previously entered by the user, such as the task content and difficulty level. This provides the data needed to determine the optimal task execution order.

[0407] Step 4:

[0408] The server redistributes and adjusts tasks based on task difficulty, emotional state, and other relevant data. The output of this process is optimized task assignment information for each worker.

[0409] Step 5:

[0410] The server transmits information about the reassigned tasks to the factory robots, which then operate based on those instructions. The factory robots execute the instructions and perform the tasks appropriately. This is expected to reduce operator stress and improve work efficiency.

[0411] Step 6:

[0412] Users receive feedback from the server through smart glasses, gaining information about task progress and their own emotional state. This makes it easier for individual workers to manage their psychological and physical burdens.

[0413] 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.

[0414] 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 those described above. 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 shown 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.

[0415] 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.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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".

[0429] This invention provides a system that enables employees within a company to perform their duties efficiently and fairly and receive compensation. The system begins with the user inputting task information using a terminal. At that time, detailed information such as the task name, required skills, and deadline is entered.

[0430] The server processes the received task information and runs an artificial intelligence model to calculate the difficulty and value of the task. During this process, it refers to past data and the history of similar tasks, and assigns a score using a predetermined algorithm. The assigned score is determined based on the degree of task completion and its importance.

[0431] Next, the server issues rewards to users as digital currency based on their evaluated scores. This digital currency is treated as internal company currency, and users can manage it in their own accounts. The digital currency issued to each user's account is recorded in a database and monitored in real time by the server.

[0432] Users can access the virtual marketplace using their own devices and browse the services and skills offered by other users. Users can initiate transactions as needed, requesting services from other users or receiving support using designated digital currency.

[0433] As a concrete example, when user A completes the task of "designing a new website," artificial intelligence evaluates their contribution and issues 10 units of digital currency to user A. Meanwhile, user B offers the skill of "digital marketing strategy," and when user A seeks this assistance, they pay B with the digital currency they have earned to complete the transaction.

[0434] This entire process consists of elements such as quantifying tasks via information processing tools, issuing digital currency through reward calculation tools, monitoring transaction history through database management tools, and exchanging tasks through transaction promotion tools. This system aims to improve employees' skills and work efficiency, and contributes to fostering a fair corporate culture.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] The user enters task information from their device. They enter data including task details such as the name, required skills, and deadline into a form and submit it.

[0438] Step 2:

[0439] The terminal sends task information entered by the user to the server. Security protocols are used to ensure data integrity, and the task information is stored in the server's database.

[0440] Step 3:

[0441] When the server receives task information, it invokes an artificial intelligence model to begin evaluating the task. It refers to similar past data to calculate the difficulty and value of the task.

[0442] Step 4:

[0443] The server uses a reward calculation mechanism to determine the appropriate digital currency based on the evaluation score returned by the artificial intelligence model. It then issues this currency to the user's account.

[0444] Step 5:

[0445] The server updates the database with records of issued digital currencies via a database management system. This ensures that currency balances and transaction history are kept up-to-date.

[0446] Step 6:

[0447] Users access a virtual marketplace on their devices and search for publicly available job requests and skill offerings. They then find the tasks and skills they need.

[0448] Step 7:

[0449] The user selects a specific task or skill and initiates the transaction. They then confirm the amount of digital currency required for the transaction and approve it.

[0450] Step 8:

[0451] The server acknowledges the transaction and records in the database that the digital currency has been transferred between users. The account balances are then updated accordingly.

[0452] Step 9:

[0453] Users check their dashboards on their devices to see the digital currency they've earned, their transaction history, and their own contributions. This allows employees to visualize their own performance.

[0454] (Example 1)

[0455] 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."

[0456] Traditional corporate business management systems made it difficult to accurately and efficiently evaluate employees' contributions to work performance, hindering fair compensation distribution based on these evaluations. Furthermore, the lack of transparency in transactions using digital currency made it challenging to foster trust among employees.

[0457] 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.

[0458] In this invention, the server includes an information processing means for receiving and analyzing task information based on user input, a means for quantifying the difficulty and value of the task using a generating AI model based on the analyzed task information, and a reward calculation means for calculating and issuing digital currency as a reward based on the assigned score. This makes it possible to accurately evaluate the work contribution of employees within a company and automatically distribute appropriate rewards. Furthermore, by transparently and efficiently managing digital currency transactions, trust among employees can be improved.

[0459] "Task information" refers to work-related information entered by users using work terminals, and specifically includes a collection of data such as task name, required skills, and deadline.

[0460] A "generative AI model" is an artificial intelligence model used for data analysis and quantifying the difficulty and value of tasks, and is typically learned by utilizing historical databases.

[0461] A "digital currency" is a virtual currency used within a system, and is a unit of value used for reward payments and transactions between users.

[0462] A "transaction facilitation tool" is a tool that has the function of supporting users in trading services or skills with other users using digital currency.

[0463] A "database management system" is a means of recording and maintaining digital currency transactions that occur within a system, and making the history accessible as needed.

[0464] A "reward calculation means" is a means that has the function of calculating and issuing rewards to users as digital currency based on task information analyzed via an information processing means.

[0465] This invention is a system for improving operational efficiency within a company and realizing a fair compensation system. The system includes a terminal operated by the user, a server for data processing, and a database for managing digital currency transactions.

[0466] Users input task information via their terminal. Specifically, users enter the task name, required skills, deadline, etc., and send it to the server. This communication is implemented using a web-based application, and the UI (user interface) is created using programming languages ​​such as Python and JavaScript.

[0467] The server runs a generative AI model using TensorFlow or PyTorch to process the received task information. This model references historical data stored in a database to quantify the difficulty and value of the input task. This quantified information is used to calculate the reward.

[0468] Reward calculation is performed by the server issuing digital currency based on quantified task evaluations. This digital currency can be used for transactions within the company, and the server monitors and manages each transaction via a database.

[0469] Users access a virtual marketplace provided by the system using their devices. Here, users can search for tasks and skills offered by other employees and select transactions that interest them. This virtual marketplace provides a platform for transactions for users and facilitates skill exchange within the company.

[0470] For example, if user A completes the task of "planning a new project," the server uses an AI model to evaluate their contribution and awards user A 20 units of digital currency. If another user B is skilled at "creating marketing strategies," user A can use the acquired digital currency to pay B for their cooperation in order to utilize that skill.

[0471] An example of a prompt might be, "Propose ways to improve project scoring criteria and efficiently operate the company's digital currency system." Following this prompt, the system processes relevant data and provides useful information to the user.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] Users enter task information into an input form via their terminal. Specifically, they fill in details such as the task name, required skills, and deadline, and then press the submit button. This action sends the entered information to the server. The input data includes task details in text format, which is then directly entered into the server.

[0475] Step 2:

[0476] The server analyzes the received task information. First, it refers to past task data stored in the database and uses a generative AI model to evaluate the difficulty and value of the task. The AI ​​model used here is based on TensorFlow or PyTorch. The server converts the task information into a numerical score through the AI ​​model. As output, a numerical evaluation representing the difficulty and value of the task is obtained.

[0477] Step 3:

[0478] The server calculates the reward based on the score obtained in step 2. Based on the calculated value, it issues digital currency to the user's account. Here, a simple calculation algorithm is used to directly convert the score into units of digital currency and assign it to the user's account. The output is the updated digital currency balance information.

[0479] Step 4:

[0480] Users access a virtual marketplace using a terminal to search for jobs and skills offered by other employees. They utilize search functions for specific skills and price ranges to select deals that interest them. The terminal filters the results based on the user's input and displays the results.

[0481] Step 5:

[0482] The server monitors transactions between users and records digital currency transactions. The database maintains the transaction history and updates it in real time. It notifies users and administrators of the transaction status as needed. The output is the updated transaction history.

[0483] Step 6:

[0484] Users can view their digital currency balance and transaction history on their device. This allows them to understand the overall picture of their activity in the virtual market and helps them develop future trading strategies. The data is displayed in a user-friendly dashboard format.

[0485] (Application Example 1)

[0486] 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."

[0487] Traditional business management systems have often presented problems with opacity and unfairness in ensuring employees perform tasks efficiently and fairly and receive adequate compensation. Furthermore, a lack of effective methods for exchanging and sharing skills and work among employees hindered overall organizational productivity improvements. To address these issues, there is a need to implement a transparent and efficient business management and compensation system using digital currency.

[0488] 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.

[0489] This invention includes a server comprising: an information processing means for inputting task information and evaluating the difficulty and value of the task based thereon; a reward calculation means for calculating a reward based on the evaluated task information and issuing the reward as digital currency; a database management means for managing transactions of the issued digital currency; a terminal means for performing task management using a smartphone application; and a virtual market means for supporting the smooth exchange of skills and work between users. As a result, employees can have their skills and tasks evaluated fairly and transparently, and can receive rewards and exchange work efficiently via digital currency.

[0490] "Task information" refers to detailed data related to a specific task that a user is performing, including the task name, required skills, and deadline.

[0491] "Difficulty level" is a quantitative indicator that represents the degree of technical or laborious challenge a task involves.

[0492] "Value" is a numerical value or standard used to evaluate the extent to which a task contributes to or benefits an organization when it is completed.

[0493] An "information processing means" is a system that automatically determines and analyzes the difficulty and value of a task based on task information using a computer.

[0494] "Reward calculation method" refers to the process or configuration for calculating the amount of digital currency to be awarded to a user based on evaluated task information.

[0495] "Digital currency" is a form of currency that is issued and managed electronically and traded between users based on the evaluation of tasks.

[0496] A "database management system" is a system or mechanism for recording and maintaining the circulation status and transaction history of issued digital currencies.

[0497] A "transaction facilitation tool" is an environment or platform that allows users to exchange tasks and skills with other users using digital currency.

[0498] A "smartphone application" is a program that allows users to manage tasks and trade digital currencies via their mobile devices.

[0499] "Terminal means" refers to computer equipment or devices used for task management or reward verification.

[0500] A "virtual marketplace" is a virtual marketplace where users can exchange or trade skills and services online.

[0501] This invention provides a system that allows company employees to perform their duties efficiently and fairly using a smartphone application. The server receives task information entered by the user and uses artificial intelligence technology to evaluate the difficulty and value of the task based on that information. Specifically, generative AI models such as TensorFlow support this evaluation.

[0502] Once task evaluation is complete, the server uses a reward calculation mechanism to determine the amount of digital currency based on the evaluation results and issues it to the user's account. This digital currency is for internal use only, and users can use it to trade with other users and exchange skills in the application's virtual marketplace via their smartphones. Transaction details and currency flow are managed and stored / updated in a database using MongoDB.

[0503] The device allows users to check task status and earned rewards in real time. Furthermore, the smartphone application provides users with an interface and an environment for sharing work and responsibilities in the market as a means of facilitating transactions.

[0504] As a concrete example, if an employee is assigned a design task for a new project, they input information about the task into an application, which allows the AI ​​to evaluate its importance and difficulty, and then issues a digital currency tailored to the employee. This currency can then be used to purchase digital marketing support from other employees.

[0505] A concrete example of a prompt statement is as follows:

[0506] "Please describe the process of designing a task management app using a cloud platform and issuing a custom currency as a reward. Also, please include a case study demonstrating a user-friendly UI in that process."

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The user opens the application on their smartphone and enters task information. A form is displayed for entering detailed information such as the task name, required skills, and deadline, which the user fills out. The entered information is then sent to the server.

[0510] Step 2:

[0511] The server analyzes the received task information. Specifically, a generative AI model using TensorFlow calculates the task difficulty and value based on past data and the history of similar tasks. This calculation is performed by vectorizing the input task information and feeding it to the model, resulting in the output of a task difficulty score and value score.

[0512] Step 3:

[0513] The server issues digital currency to users using a reward calculation mechanism based on their evaluated score. Here, the reward amount is determined according to the difficulty and value, and this is credited to the user's account as digital currency. Organizational policies may be reflected in the setting of reward ratios.

[0514] Step 4:

[0515] The issued digital currency and task evaluation results are stored in a database management system using MongoDB. The database updates transaction history and currency circulation in real time, and securely stores and manages the data.

[0516] Step 5:

[0517] The device displays an interface that allows the user to check their digital currency balance and task evaluation status. Here, users can monitor their progress and rewards. The interface is designed using React Native and is intended to be intuitive to use.

[0518] Step 6:

[0519] Users can use the digital currency they earn to trade skills and services with other users in the application's virtual marketplace. Here, a list of skills offered by users is displayed, and users can select the necessary support and pay with digital currency.

[0520] 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.

[0521] This invention aims to improve employee productivity and the work environment by combining a task management system with an emotion engine that recognizes user emotions. In this system, the user inputs task information using a terminal, and based on that information, the server uses artificial intelligence to evaluate the difficulty and value of the task. In addition, the emotion engine analyzes the user's emotions in real time and transmits that data to the server.

[0522] The server calculates rewards based on acquired emotional data, incorporating it into traditional evaluations. These rewards are issued as digital currency to the user's account, which the user can then use for internal company transactions. By taking into account the user's psychological state and motivation as indicated by the emotional data, the fairness of rewards increases, leading to improved employee motivation.

[0523] For example, if User A completes the task of "creating a project report" and a higher-than-usual stress level is detected, the emotion engine analyzes this information and transmits it to the server. Based on this data, the server adjusts the evaluation of User A's contribution more appropriately by issuing additional digital currency in addition to the usual reward.

[0524] The feedback users receive through the emotion engine on their devices provides insights into their contributions and areas for improvement based on their own feelings, helping them adjust their approach to future tasks. Furthermore, by analyzing the team's overall emotion data, the server suggests optimal task redistributions, and users can receive this information through their devices.

[0525] In this way, by integrating an emotion engine into the system, the aim is to improve the working environment for employees by taking into account psychological factors that were lacking in conventional performance evaluation systems. This invention provides an implementation model that achieves effective task evaluation and reward distribution after selecting complex social factors.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The user enters task information from their device. They input the task name, required skills, deadline, etc., through the device's interface and send it to the system.

[0529] Step 2:

[0530] The device collects user emotional data. Using sensors and software built into the device, it analyzes facial expressions, voice tone, input speed, and other factors in real time.

[0531] Step 3:

[0532] The server receives task information and activates the artificial intelligence model. It references past task data to quantify the difficulty and value of the input task.

[0533] Step 4:

[0534] The server analyzes emotional data transmitted from the terminal. From the collected data, it calculates the user's stress level and motivation indicators, and uses these factors to evaluate tasks.

[0535] Step 5:

[0536] The server integrates task evaluation scores and sentiment data, and uses a reward calculation mechanism to determine the digital currency. The reward is issued to the user's account.

[0537] Step 6:

[0538] The server records digital currency issued via a database management system into the database and updates the account balance. This information is used to monitor user transactions.

[0539] Step 7:

[0540] Users access a virtual marketplace via their devices. They search for job requests and skills offered by other users and conduct transactions using their own digital currency.

[0541] Step 8:

[0542] The server records transaction details and adjusts digital currency between users based on the transaction results. The transaction history in the database is updated.

[0543] Step 9:

[0544] Users can view their device dashboard to visualize their transaction history and performance, including their own sentiment data. This information can then be used to improve how they approach future tasks.

[0545] (Example 2)

[0546] 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."

[0547] In modern workplaces, task evaluation and compensation are often based on simple productivity metrics, resulting in a problem where psychological factors are not adequately considered. This leads to a lack of reflection of employee stress and motivation, making it difficult to improve the work environment and increase employee productivity.

[0548] 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.

[0549] In this invention, the server includes information processing means that input user behavior information and evaluate the difficulty and value of the behavior based on that information; emotion recognition means that analyze the user's emotions and transmit them in real time; and reward adjustment means that incorporate the acquired emotion data into the conventional evaluation. This makes it possible to determine rewards more fairly and effectively by taking into account psychological factors such as emotions.

[0550] "User" refers to an individual or organization that operates the system, inputs information, or uses information.

[0551] "Behavioral information" refers to data related to tasks and activities performed by the user, specifically including the content, deadline, and importance of the tasks.

[0552] "Information processing means" refers to a device or program that has the function of evaluating, analyzing, and quantifying the content of input behavioral information.

[0553] "Digital format" refers to a method of representing evaluation and reward calculation results as electronic data.

[0554] "Reward calculation means" refers to a device or program that has the function of calculating the amount of reward to be earned by the user based on data obtained from information processing means.

[0555] "Record management means" refers to equipment or programs that have the function of saving and managing the exchange history in digital format that has been issued.

[0556] "Exchange facilitator" refers to a device or program that has the function of enabling a user to conduct transactions with other users using a digital format.

[0557] "Emotion recognition means" refers to a device or program that has the function of analyzing the user's psychological state and transmitting the results as data in real time.

[0558] "Reward adjustment mechanism" refers to a device or program that has the function of recalculating fair rewards by modifying conventional evaluation criteria in consideration of emotional data.

[0559] This invention is a system that integrates task management and reward systems in an office environment, providing improvement measures that also take into account the feelings of employees.

[0560] The user first enters information about the task they are working on via a terminal. The terminal has task management software installed, allowing the user to input information such as the task name, deadline, and importance level. This information is then transmitted digitally to the server.

[0561] The server processes the received task information. Specifically, it uses data analysis software such as Python or R, and generative AI models utilizing TensorFlow or PyTorch, to quantify and evaluate the difficulty and value of the input task information. Based on this evaluation, the server prepares to perform the subsequent reward calculation.

[0562] The device uses its built-in camera and microphone to capture the user's facial expressions and voice information into an emotion engine in order to analyze the user's emotions. The emotion engine is designed to analyze the user's emotional state in real time and immediately transmits the obtained data to the server.

[0563] The server uses not only task information but also user sentiment data to ensure fair reward adjustments. This process incorporates reward adjustment mechanisms that appropriately consider the impact of sentiment data on reward calculations. Finally, the calculated reward is issued digitally to the user's account.

[0564] As a concrete example, consider a scenario where a user enters "Create an important project report" as a task, and upon completion, a high stress level is detected. In this case, the server considers not only the task evaluation but also the emotional data, and provides additional digital rewards to appropriately evaluate the user's efforts.

[0565] An example of a prompt for a generative AI model is: "If a user experiences high stress while creating a project report, how does the emotion engine analyze this information and notify the server? Furthermore, how does it adjust the reward based on this information?"

[0566] This system makes it possible to create a better working environment that takes psychological factors into consideration.

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] Users enter task information (e.g., task name, due date, importance) using a task management app on their device. This information is converted into digital data by the device and sent to the server. The input data reflects the user's current work status and serves as the foundation for starting the entire system's processing.

[0570] Step 2:

[0571] The server receives task information submitted by the user and analyzes its contents using a generative AI model. Specifically, it analyzes the input data using an information processing system and quantifies the difficulty and value of the task. In this process, an AI model using TensorFlow evaluates the task information and outputs it as numerical data. This numerical data is used as the basis for calculating rewards.

[0572] Step 3:

[0573] The device records the user's facial expressions and voice through its built-in camera and microphone to analyze the user's emotions in real time, and sends this data to an emotion recognition engine. The input biometric data is rapidly processed to calculate the user's current emotional state (e.g., stress level, happiness level), and the results are sent to a server. This data is used in the reward determination process.

[0574] Step 4:

[0575] The server integrates task evaluation results and user sentiment data to calculate the final reward. The reward calculation mechanism adjusts the reward based on factors such as stress reduction. For example, completing a high-difficulty task under high stress levels will result in additional digital rewards. This output is reflected in the user's account as a digital reward.

[0576] Step 5:

[0577] Users receive feedback sent from the server via their device. This feedback includes insights generated based on the user's emotional data and is displayed visually. This allows users to see their own contributions and gain clues to improve their approach to future tasks. This output provides an opportunity for emotionally-based self-assessment.

[0578] (Application Example 2)

[0579] 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."

[0580] In today's work environment, the impact of employees' emotional states on work efficiency and results cannot be ignored. However, existing task management systems lack mechanisms to reflect individual emotional states in evaluations, making it difficult to achieve fair and effective task evaluation and reward distribution. Therefore, there is a growing need for systems that redistribute tasks and rewards while taking emotional states into consideration.

[0581] 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.

[0582] In this invention, the server includes an information processing means that inputs task information and evaluates the difficulty and value of the task based on that information; a reward calculation means that calculates a reward based on the evaluated task information and issues it as digital currency; and an emotion analysis means that acquires the user's emotional state and optimizes the task execution order based on that data. This enables efficient and fair task evaluation and reward distribution that takes the emotional state into account.

[0583] "Task information" refers to detailed data related to a specific task or activity, and is used to evaluate its difficulty and value.

[0584] An "information processing means" is a system component that analyzes input task information and determines its difficulty level and value based on evaluation criteria.

[0585] A "reward calculation device" is a system that has the function of appropriately calculating the reward for the user based on the evaluated task information and issuing it as digital currency.

[0586] A "database management system" is a system element for recording and managing the transaction history of issued digital currencies and transactions between users.

[0587] A "transaction facilitation mechanism" is a system that supports users in conducting transactions with other users using the digital currency that has been issued.

[0588] "Emotional state" refers to the emotional and psychological status that an individual is experiencing at a particular point in time.

[0589] An "emotion analysis tool" is a system component that acquires and analyzes a user's emotional state in real time and uses the results to optimize tasks.

[0590] The system used to realize this application optimizes the tasks of factory robots by taking into account the emotional state of the operator, in order to improve work efficiency in the factory.

[0591] The server collects task information and operator emotion data acquired within the factory, and uses this data to evaluate tasks and perform emotion analysis. Information processing tools are used to process task information, and an AI model quantifies the difficulty and value of tasks. Emotion analysis tools analyze operator emotion data transmitted from devices such as smart glasses in real time to evaluate the operator's stress level and fatigue.

[0592] The terminal collects data from devices worn by the user, such as smart glasses, and transfers it to the server. This utilizes facial recognition sensors and heart rate sensors. Based on the obtained emotional data, the server uses emotion analysis tools to optimally redistribute tasks and adjusts the robot's tasks in a way that reduces the user's physical and mental burden.

[0593] For example, if it is detected that an operator is experiencing high stress while assembling parts, this information is immediately transmitted to the server. The server then takes this emotional state into consideration and makes adjustments, such as assigning the factory robot a different, less demanding task. This helps maintain the operator's work efficiency and motivation.

[0594] A generative AI model is used, and an example of a prompt in this context is as follows:

[0595] "If an operator's emotional data indicates high stress levels, please explain how you would optimally redistribute tasks."

[0596] This enables the system to create an efficient and fair work environment that takes emotional states into consideration.

[0597] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0598] Step 1:

[0599] The device uses sensors in smart glasses to acquire physiological data such as the operator's facial expressions and heart rate. This data is used as input to evaluate their emotional state.

[0600] Step 2:

[0601] The device sends acquired physiological data to a server for emotion analysis. The server receives this data and uses a generative AI model to analyze the emotional state in real time. The output here is the specific emotional state and its intensity.

[0602] Step 3:

[0603] The server uses the results of the emotional state analysis to compare them with information about the tasks currently being performed. This task information includes data previously entered by the user, such as the task content and difficulty level. This provides the data needed to determine the optimal task execution order.

[0604] Step 4:

[0605] The server redistributes and adjusts tasks based on task difficulty, emotional state, and other relevant data. The output of this process is optimized task assignment information for each worker.

[0606] Step 5:

[0607] The server transmits information about the reassigned tasks to the factory robots, which then operate based on those instructions. The factory robots execute the instructions and perform the tasks appropriately. This is expected to reduce operator stress and improve work efficiency.

[0608] Step 6:

[0609] Users receive feedback from the server through smart glasses, gaining information about task progress and their own emotional state. This makes it easier for individual workers to manage their psychological and physical burdens.

[0610] 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.

[0611] 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 those described above. 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 shown 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.

[0612] 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.

[0613] [Fourth Embodiment]

[0614] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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".

[0627] This invention provides a system that enables employees within a company to perform their duties efficiently and fairly and receive compensation. The system begins with the user inputting task information using a terminal. At that time, detailed information such as the task name, required skills, and deadline is entered.

[0628] The server processes the received task information and runs an artificial intelligence model to calculate the difficulty and value of the task. During this process, it refers to past data and the history of similar tasks, and assigns a score using a predetermined algorithm. The assigned score is determined based on the degree of task completion and its importance.

[0629] Next, the server issues rewards to users as digital currency based on their evaluated scores. This digital currency is treated as internal company currency, and users can manage it in their own accounts. The digital currency issued to each user's account is recorded in a database and monitored in real time by the server.

[0630] Users can access the virtual marketplace using their own devices and browse the services and skills offered by other users. Users can initiate transactions as needed, requesting services from other users or receiving support using designated digital currency.

[0631] As a concrete example, when user A completes the task of "designing a new website," artificial intelligence evaluates their contribution and issues 10 units of digital currency to user A. Meanwhile, user B offers the skill of "digital marketing strategy," and when user A seeks this assistance, they pay B with the digital currency they have earned to complete the transaction.

[0632] This entire process consists of elements such as quantifying tasks via information processing tools, issuing digital currency through reward calculation tools, monitoring transaction history through database management tools, and exchanging tasks through transaction promotion tools. This system aims to improve employees' skills and work efficiency, and contributes to fostering a fair corporate culture.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The user enters task information from their device. They enter data including task details such as the name, required skills, and deadline into a form and submit it.

[0636] Step 2:

[0637] The terminal sends task information entered by the user to the server. Security protocols are used to ensure data integrity, and the task information is stored in the server's database.

[0638] Step 3:

[0639] When the server receives task information, it invokes an artificial intelligence model to begin evaluating the task. It refers to similar past data to calculate the difficulty and value of the task.

[0640] Step 4:

[0641] The server uses a reward calculation mechanism to determine the appropriate digital currency based on the evaluation score returned by the artificial intelligence model. It then issues this currency to the user's account.

[0642] Step 5:

[0643] The server updates the database with records of issued digital currencies via a database management system. This ensures that currency balances and transaction history are kept up-to-date.

[0644] Step 6:

[0645] Users access a virtual marketplace on their devices and search for publicly available job requests and skill offerings. They then find the tasks and skills they need.

[0646] Step 7:

[0647] The user selects a specific task or skill and initiates the transaction. They then confirm the amount of digital currency required for the transaction and approve it.

[0648] Step 8:

[0649] The server acknowledges the transaction and records in the database that the digital currency has been transferred between users. The account balances are then updated accordingly.

[0650] Step 9:

[0651] Users check their dashboards on their devices to see the digital currency they've earned, their transaction history, and their own contributions. This allows employees to visualize their own performance.

[0652] (Example 1)

[0653] 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".

[0654] Traditional corporate business management systems made it difficult to accurately and efficiently evaluate employees' contributions to work performance, hindering fair compensation distribution based on these evaluations. Furthermore, the lack of transparency in transactions using digital currency made it challenging to foster trust among employees.

[0655] 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.

[0656] In this invention, the server includes an information processing means for receiving and analyzing task information based on user input, a means for quantifying the difficulty and value of the task using a generating AI model based on the analyzed task information, and a reward calculation means for calculating and issuing digital currency as a reward based on the assigned score. This makes it possible to accurately evaluate the work contribution of employees within a company and automatically distribute appropriate rewards. Furthermore, by transparently and efficiently managing digital currency transactions, trust among employees can be improved.

[0657] "Task information" refers to work-related information entered by users using work terminals, and specifically includes a collection of data such as task name, required skills, and deadline.

[0658] A "generative AI model" is an artificial intelligence model used for data analysis and quantifying the difficulty and value of tasks, and is typically learned by utilizing historical databases.

[0659] A "digital currency" is a virtual currency used within a system, and is a unit of value used for reward payments and transactions between users.

[0660] A "transaction facilitation tool" is a tool that has the function of supporting users in trading services or skills with other users using digital currency.

[0661] A "database management system" is a means of recording and maintaining digital currency transactions that occur within a system, and making the history accessible as needed.

[0662] A "reward calculation means" is a means that has the function of calculating and issuing rewards to users as digital currency based on task information analyzed via an information processing means.

[0663] This invention is a system for improving operational efficiency within a company and realizing a fair compensation system. The system includes a terminal operated by the user, a server for data processing, and a database for managing digital currency transactions.

[0664] Users input task information via their terminal. Specifically, users enter the task name, required skills, deadline, etc., and send it to the server. This communication is implemented using a web-based application, and the UI (user interface) is created using programming languages ​​such as Python and JavaScript.

[0665] The server runs a generative AI model using TensorFlow or PyTorch to process the received task information. This model references historical data stored in a database to quantify the difficulty and value of the input task. This quantified information is used to calculate the reward.

[0666] Reward calculation is performed by the server issuing digital currency based on quantified task evaluations. This digital currency can be used for transactions within the company, and the server monitors and manages each transaction via a database.

[0667] Users access a virtual marketplace provided by the system using their devices. Here, users can search for tasks and skills offered by other employees and select transactions that interest them. This virtual marketplace provides a platform for transactions for users and facilitates skill exchange within the company.

[0668] For example, if user A completes the task of "planning a new project," the server uses an AI model to evaluate their contribution and awards user A 20 units of digital currency. If another user B is skilled at "creating marketing strategies," user A can use the acquired digital currency to pay B for their cooperation in order to utilize that skill.

[0669] An example of a prompt might be, "Propose ways to improve project scoring criteria and efficiently operate the company's digital currency system." Following this prompt, the system processes relevant data and provides useful information to the user.

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] Users enter task information into an input form via their terminal. Specifically, they fill in details such as the task name, required skills, and deadline, and then press the submit button. This action sends the entered information to the server. The input data includes task details in text format, which is then directly entered into the server.

[0673] Step 2:

[0674] The server analyzes the received task information. First, it refers to past task data stored in the database and uses a generative AI model to evaluate the difficulty and value of the task. The AI ​​model used here is based on TensorFlow or PyTorch. The server converts the task information into a numerical score through the AI ​​model. As output, a numerical evaluation representing the difficulty and value of the task is obtained.

[0675] Step 3:

[0676] The server calculates the reward based on the score obtained in step 2. Based on the calculated value, it issues digital currency to the user's account. Here, a simple calculation algorithm is used to directly convert the score into units of digital currency and assign it to the user's account. The output is the updated digital currency balance information.

[0677] Step 4:

[0678] Users access a virtual marketplace using a terminal to search for jobs and skills offered by other employees. They utilize search functions for specific skills and price ranges to select deals that interest them. The terminal filters the results based on the user's input and displays the results.

[0679] Step 5:

[0680] The server monitors transactions between users and records digital currency transactions. The database maintains the transaction history and updates it in real time. It notifies users and administrators of the transaction status as needed. The output is the updated transaction history.

[0681] Step 6:

[0682] Users can view their digital currency balance and transaction history on their device. This allows them to understand the overall picture of their activity in the virtual market and helps them develop future trading strategies. The data is displayed in a user-friendly dashboard format.

[0683] (Application Example 1)

[0684] 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".

[0685] Traditional business management systems have often presented problems with opacity and unfairness in ensuring employees perform tasks efficiently and fairly and receive adequate compensation. Furthermore, a lack of effective methods for exchanging and sharing skills and work among employees hindered overall organizational productivity improvements. To address these issues, there is a need to implement a transparent and efficient business management and compensation system using digital currency.

[0686] 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.

[0687] This invention includes a server comprising: an information processing means for inputting task information and evaluating the difficulty and value of the task based thereon; a reward calculation means for calculating a reward based on the evaluated task information and issuing the reward as digital currency; a database management means for managing transactions of the issued digital currency; a terminal means for performing task management using a smartphone application; and a virtual market means for supporting the smooth exchange of skills and work between users. As a result, employees can have their skills and tasks evaluated fairly and transparently, and can receive rewards and exchange work efficiently via digital currency.

[0688] "Task information" refers to detailed data related to a specific task that a user is performing, including the task name, required skills, and deadline.

[0689] "Difficulty level" is a quantitative indicator that represents the degree of technical or laborious challenge a task involves.

[0690] "Value" is a numerical value or standard used to evaluate the extent to which a task contributes to or benefits an organization when it is completed.

[0691] An "information processing means" is a system that automatically determines and analyzes the difficulty and value of a task based on task information using a computer.

[0692] "Reward calculation method" refers to the process or configuration for calculating the amount of digital currency to be awarded to a user based on evaluated task information.

[0693] "Digital currency" is a form of currency that is issued and managed electronically and traded between users based on the evaluation of tasks.

[0694] A "database management system" is a system or mechanism for recording and maintaining the circulation status and transaction history of issued digital currencies.

[0695] A "transaction facilitation tool" is an environment or platform that allows users to exchange tasks and skills with other users using digital currency.

[0696] A "smartphone application" is a program that allows users to manage tasks and trade digital currencies via their mobile devices.

[0697] "Terminal means" refers to computer equipment or devices used for task management or reward verification.

[0698] A "virtual marketplace" is a virtual marketplace where users can exchange or trade skills and services online.

[0699] This invention provides a system that allows company employees to perform their duties efficiently and fairly using a smartphone application. The server receives task information entered by the user and uses artificial intelligence technology to evaluate the difficulty and value of the task based on that information. Specifically, generative AI models such as TensorFlow support this evaluation.

[0700] Once task evaluation is complete, the server uses a reward calculation mechanism to determine the amount of digital currency based on the evaluation results and issues it to the user's account. This digital currency is for internal use only, and users can use it to trade with other users and exchange skills in the application's virtual marketplace via their smartphones. Transaction details and currency flow are managed and stored / updated in a database using MongoDB.

[0701] The device allows users to check task status and earned rewards in real time. Furthermore, the smartphone application provides users with an interface and an environment for sharing work and responsibilities in the market as a means of facilitating transactions.

[0702] As a concrete example, if an employee is assigned a design task for a new project, they input information about the task into an application, which allows the AI ​​to evaluate its importance and difficulty, and then issues a digital currency tailored to the employee. This currency can then be used to purchase digital marketing support from other employees.

[0703] A concrete example of a prompt statement is as follows:

[0704] "Please describe the process of designing a task management app using a cloud platform and issuing a custom currency as a reward. Also, please include a case study demonstrating a user-friendly UI in that process."

[0705] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0706] Step 1:

[0707] The user opens the application on their smartphone and enters task information. A form is displayed for entering detailed information such as the task name, required skills, and deadline, which the user fills out. The entered information is then sent to the server.

[0708] Step 2:

[0709] The server analyzes the received task information. Specifically, a generative AI model using TensorFlow calculates the task difficulty and value based on past data and the history of similar tasks. This calculation is performed by vectorizing the input task information and feeding it to the model, resulting in the output of a task difficulty score and value score.

[0710] Step 3:

[0711] The server issues digital currency to users using a reward calculation mechanism based on their evaluated score. Here, the reward amount is determined according to the difficulty and value, and this is credited to the user's account as digital currency. Organizational policies may be reflected in the setting of reward ratios.

[0712] Step 4:

[0713] The issued digital currency and task evaluation results are stored in a database management system using MongoDB. The database updates transaction history and currency circulation in real time, and securely stores and manages the data.

[0714] Step 5:

[0715] The device displays an interface that allows the user to check their digital currency balance and task evaluation status. Here, users can monitor their progress and rewards. The interface is designed using React Native and is intended to be intuitive to use.

[0716] Step 6:

[0717] Users can use the digital currency they earn to trade skills and services with other users in the application's virtual marketplace. Here, a list of skills offered by users is displayed, and users can select the necessary support and pay with digital currency.

[0718] 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.

[0719] This invention aims to improve employee productivity and the work environment by combining a task management system with an emotion engine that recognizes user emotions. In this system, the user inputs task information using a terminal, and based on that information, the server uses artificial intelligence to evaluate the difficulty and value of the task. In addition, the emotion engine analyzes the user's emotions in real time and transmits that data to the server.

[0720] The server calculates rewards based on acquired emotional data, incorporating it into traditional evaluations. These rewards are issued as digital currency to the user's account, which the user can then use for internal company transactions. By taking into account the user's psychological state and motivation as indicated by the emotional data, the fairness of rewards increases, leading to improved employee motivation.

[0721] For example, if User A completes the task of "creating a project report" and a higher-than-usual stress level is detected, the emotion engine analyzes this information and transmits it to the server. Based on this data, the server adjusts the evaluation of User A's contribution more appropriately by issuing additional digital currency in addition to the usual reward.

[0722] The feedback users receive through the emotion engine on their devices provides insights into their contributions and areas for improvement based on their own feelings, helping them adjust their approach to future tasks. Furthermore, by analyzing the team's overall emotion data, the server suggests optimal task redistributions, and users can receive this information through their devices.

[0723] In this way, by integrating an emotion engine into the system, the aim is to improve the working environment for employees by taking into account psychological factors that were lacking in conventional performance evaluation systems. This invention provides an implementation model that achieves effective task evaluation and reward distribution after selecting complex social factors.

[0724] The following describes the processing flow.

[0725] Step 1:

[0726] The user enters task information from their device. They input the task name, required skills, deadline, etc., through the device's interface and send it to the system.

[0727] Step 2:

[0728] The device collects user emotional data. Using sensors and software built into the device, it analyzes facial expressions, voice tone, input speed, and other factors in real time.

[0729] Step 3:

[0730] The server receives task information and activates the artificial intelligence model. It references past task data to quantify the difficulty and value of the input task.

[0731] Step 4:

[0732] The server analyzes emotional data transmitted from the terminal. From the collected data, it calculates the user's stress level and motivation indicators, and uses these factors to evaluate tasks.

[0733] Step 5:

[0734] The server integrates task evaluation scores and sentiment data, and uses a reward calculation mechanism to determine the digital currency. The reward is issued to the user's account.

[0735] Step 6:

[0736] The server records digital currency issued via a database management system into the database and updates the account balance. This information is used to monitor user transactions.

[0737] Step 7:

[0738] Users access a virtual marketplace via their devices. They search for job requests and skills offered by other users and conduct transactions using their own digital currency.

[0739] Step 8:

[0740] The server records transaction details and adjusts digital currency between users based on the transaction results. The transaction history in the database is updated.

[0741] Step 9:

[0742] Users can view their device dashboard to visualize their transaction history and performance, including their own sentiment data. This information can then be used to improve how they approach future tasks.

[0743] (Example 2)

[0744] 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".

[0745] In modern workplaces, task evaluation and compensation are often based on simple productivity metrics, resulting in a problem where psychological factors are not adequately considered. This leads to a lack of reflection of employee stress and motivation, making it difficult to improve the work environment and increase employee productivity.

[0746] 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.

[0747] In this invention, the server includes information processing means that input user behavior information and evaluate the difficulty and value of the behavior based on that information; emotion recognition means that analyze the user's emotions and transmit them in real time; and reward adjustment means that incorporate the acquired emotion data into the conventional evaluation. This makes it possible to determine rewards more fairly and effectively by taking into account psychological factors such as emotions.

[0748] "User" refers to an individual or organization that operates the system, inputs information, or uses information.

[0749] "Behavioral information" refers to data related to tasks and activities performed by the user, specifically including the content, deadline, and importance of the tasks.

[0750] "Information processing means" refers to a device or program that has the function of evaluating, analyzing, and quantifying the content of input behavioral information.

[0751] "Digital format" refers to a method of representing evaluation and reward calculation results as electronic data.

[0752] "Reward calculation means" refers to a device or program that has the function of calculating the amount of reward to be earned by the user based on data obtained from information processing means.

[0753] "Record management means" refers to equipment or programs that have the function of saving and managing the exchange history in digital format that has been issued.

[0754] "Exchange facilitator" refers to a device or program that has the function of enabling a user to conduct transactions with other users using a digital format.

[0755] "Emotion recognition means" refers to a device or program that has the function of analyzing the user's psychological state and transmitting the results as data in real time.

[0756] "Reward adjustment mechanism" refers to a device or program that has the function of recalculating fair rewards by modifying conventional evaluation criteria in consideration of emotional data.

[0757] This invention is a system that integrates task management and reward systems in an office environment, providing improvement measures that also take into account the feelings of employees.

[0758] The user first enters information about the task they are working on via a terminal. The terminal has task management software installed, allowing the user to input information such as the task name, deadline, and importance level. This information is then transmitted digitally to the server.

[0759] The server processes the received task information. Specifically, it uses data analysis software such as Python or R, and generative AI models utilizing TensorFlow or PyTorch, to quantify and evaluate the difficulty and value of the input task information. Based on this evaluation, the server prepares to perform the subsequent reward calculation.

[0760] The device uses its built-in camera and microphone to capture the user's facial expressions and voice information into an emotion engine in order to analyze the user's emotions. The emotion engine is designed to analyze the user's emotional state in real time and immediately transmits the obtained data to the server.

[0761] The server uses not only task information but also user sentiment data to ensure fair reward adjustments. This process incorporates reward adjustment mechanisms that appropriately consider the impact of sentiment data on reward calculations. Finally, the calculated reward is issued digitally to the user's account.

[0762] As a concrete example, consider a scenario where a user enters "Create an important project report" as a task, and upon completion, a high stress level is detected. In this case, the server considers not only the task evaluation but also the emotional data, and provides additional digital rewards to appropriately evaluate the user's efforts.

[0763] An example of a prompt for a generative AI model is: "If a user experiences high stress while creating a project report, how does the emotion engine analyze this information and notify the server? Furthermore, how does it adjust the reward based on this information?"

[0764] This system makes it possible to create a better working environment that takes psychological factors into consideration.

[0765] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0766] Step 1:

[0767] Users enter task information (e.g., task name, due date, importance) using a task management app on their device. This information is converted into digital data by the device and sent to the server. The input data reflects the user's current work status and serves as the foundation for starting the entire system's processing.

[0768] Step 2:

[0769] The server receives task information submitted by the user and analyzes its contents using a generative AI model. Specifically, it analyzes the input data using an information processing system and quantifies the difficulty and value of the task. In this process, an AI model using TensorFlow evaluates the task information and outputs it as numerical data. This numerical data is used as the basis for calculating rewards.

[0770] Step 3:

[0771] The device records the user's facial expressions and voice through its built-in camera and microphone to analyze the user's emotions in real time, and sends this data to an emotion recognition engine. The input biometric data is rapidly processed to calculate the user's current emotional state (e.g., stress level, happiness level), and the results are sent to a server. This data is used in the reward determination process.

[0772] Step 4:

[0773] The server integrates task evaluation results and user sentiment data to calculate the final reward. The reward calculation mechanism adjusts the reward based on factors such as stress reduction. For example, completing a high-difficulty task under high stress levels will result in additional digital rewards. This output is reflected in the user's account as a digital reward.

[0774] Step 5:

[0775] Users receive feedback sent from the server via their device. This feedback includes insights generated based on the user's emotional data and is displayed visually. This allows users to see their own contributions and gain clues to improve their approach to future tasks. This output provides an opportunity for emotionally-based self-assessment.

[0776] (Application Example 2)

[0777] 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".

[0778] In today's work environment, the impact of employees' emotional states on work efficiency and results cannot be ignored. However, existing task management systems lack mechanisms to reflect individual emotional states in evaluations, making it difficult to achieve fair and effective task evaluation and reward distribution. Therefore, there is a growing need for systems that redistribute tasks and rewards while taking emotional states into consideration.

[0779] 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.

[0780] In this invention, the server includes an information processing means that inputs task information and evaluates the difficulty and value of the task based on that information; a reward calculation means that calculates a reward based on the evaluated task information and issues it as digital currency; and an emotion analysis means that acquires the user's emotional state and optimizes the task execution order based on that data. This enables efficient and fair task evaluation and reward distribution that takes the emotional state into account.

[0781] "Task information" refers to detailed data related to a specific task or activity, and is used to evaluate its difficulty and value.

[0782] An "information processing means" is a system component that analyzes input task information and determines its difficulty level and value based on evaluation criteria.

[0783] A "reward calculation device" is a system that has the function of appropriately calculating the reward for the user based on the evaluated task information and issuing it as digital currency.

[0784] A "database management system" is a system element for recording and managing the transaction history of issued digital currencies and transactions between users.

[0785] A "transaction facilitation mechanism" is a system that supports users in conducting transactions with other users using the digital currency that has been issued.

[0786] "Emotional state" refers to the emotional and psychological status that an individual is experiencing at a particular point in time.

[0787] An "emotion analysis tool" is a system component that acquires and analyzes a user's emotional state in real time and uses the results to optimize tasks.

[0788] The system used to realize this application optimizes the tasks of factory robots by taking into account the emotional state of the operator, in order to improve work efficiency in the factory.

[0789] The server collects task information and operator emotion data acquired within the factory, and uses this data to evaluate tasks and perform emotion analysis. Information processing tools are used to process task information, and an AI model quantifies the difficulty and value of tasks. Emotion analysis tools analyze operator emotion data transmitted from devices such as smart glasses in real time to evaluate the operator's stress level and fatigue.

[0790] The terminal collects data from devices worn by the user, such as smart glasses, and transfers it to the server. This utilizes facial recognition sensors and heart rate sensors. Based on the obtained emotional data, the server uses emotion analysis tools to optimally redistribute tasks and adjusts the robot's tasks in a way that reduces the user's physical and mental burden.

[0791] For example, if it is detected that an operator is experiencing high stress while assembling parts, this information is immediately transmitted to the server. The server then takes this emotional state into consideration and makes adjustments, such as assigning the factory robot a different, less demanding task. This helps maintain the operator's work efficiency and motivation.

[0792] A generative AI model is used, and an example of a prompt in this context is as follows:

[0793] "If an operator's emotional data indicates high stress levels, please explain how you would optimally redistribute tasks."

[0794] This enables the system to create an efficient and fair work environment that takes emotional states into consideration.

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The device uses sensors in smart glasses to acquire physiological data such as the operator's facial expressions and heart rate. This data is used as input to evaluate their emotional state.

[0798] Step 2:

[0799] The device sends acquired physiological data to a server for emotion analysis. The server receives this data and uses a generative AI model to analyze the emotional state in real time. The output here is the specific emotional state and its intensity.

[0800] Step 3:

[0801] The server uses the results of the emotional state analysis to compare them with information about the tasks currently being performed. This task information includes data previously entered by the user, such as the task content and difficulty level. This provides the data needed to determine the optimal task execution order.

[0802] Step 4:

[0803] The server redistributes and adjusts tasks based on task difficulty, emotional state, and other relevant data. The output of this process is optimized task assignment information for each worker.

[0804] Step 5:

[0805] The server transmits information about the reassigned tasks to the factory robots, which then operate based on those instructions. The factory robots execute the instructions and perform the tasks appropriately. This is expected to reduce operator stress and improve work efficiency.

[0806] Step 6:

[0807] Users receive feedback from the server through smart glasses, gaining information about task progress and their own emotional state. This makes it easier for individual workers to manage their psychological and physical burdens.

[0808] 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.

[0809] 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 those described above. 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 shown 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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."

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] The following is further disclosed regarding the embodiments described above.

[0830] (Claim 1)

[0831] An information processing means that inputs task information and evaluates the difficulty and value of the task based on that information,

[0832] A reward calculation means that calculates rewards based on evaluated task information and issues rewards as digital currency,

[0833] A database management system for managing transactions of issued digital currencies,

[0834] A transaction facilitation mechanism that enables users to conduct transactions with other users using digital currency issued by the user,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, which analyzes and quantifies the aforementioned task information using artificial intelligence.

[0838] (Claim 3)

[0839] The system according to claim 1, wherein the digital currency is provided with a dashboard display means for visualizing transaction records between users.

[0840] "Example 1"

[0841] (Claim 1)

[0842] An information processing means that receives task information based on user input and analyzes it,

[0843] A means of quantifying the difficulty and value of a task using a generative AI model based on the analyzed task information,

[0844] A reward calculation method that calculates and issues digital currency as a reward based on an assigned score,

[0845] A means of facilitating transactions that makes issued digital currency available for transactions between users,

[0846] Search and display means to help users access virtual markets and select trading items,

[0847] A means of managing transaction records in a database and monitoring the trends of digital currencies,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, comprising a notification means for immediately notifying the user of a reward based on task information analyzed using a generative AI model.

[0851] (Claim 3)

[0852] The system according to claim 1, further comprising a dashboard that displays the balance and transaction history of the digital currency in real time.

[0853] "Application Example 1"

[0854] (Claim 1)

[0855] An information processing means that inputs task information and evaluates the difficulty and value of the task based on that information,

[0856] A reward calculation means that calculates rewards based on evaluated task information and issues rewards as digital currency,

[0857] A database management system for managing transactions of issued digital currencies,

[0858] A transaction facilitation mechanism that enables users to conduct transactions with other users using digital currency issued by the user,

[0859] A terminal device that uses a smartphone application to manage tasks,

[0860] A virtual marketplace that facilitates the smooth exchange of skills and services between users,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, which analyzes and quantifies the aforementioned task information using artificial intelligence.

[0864] (Claim 3)

[0865] The system according to claim 1, wherein the digital currency is provided with an interface display means for visualizing transaction records between users.

[0866] "Example 2 of combining an emotion engine"

[0867] (Claim 1)

[0868] An information processing means that inputs user behavior information and evaluates the difficulty and value of the behavior based on that information,

[0869] A reward calculation means that calculates rewards based on evaluated behavioral information and issues rewards in digital format,

[0870] A record management system for managing the issued digital exchange history,

[0871] An exchange facilitator that enables users to exchange with other users using the issued digital format,

[0872] An emotion recognition method that analyzes the user's emotions and transmits them in real time,

[0873] Based on the acquired emotional data, a reward adjustment mechanism is incorporated into the conventional evaluation,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, which analyzes and quantifies the aforementioned behavioral information using machine learning.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the digital format includes a display means for visualizing the exchange records between users.

[0879] "Application example 2 when combining with an emotional engine"

[0880] (Claim 1)

[0881] An information processing means that inputs task information and evaluates the difficulty and value of the task based on that information,

[0882] A reward calculation means that calculates rewards based on evaluated task information and issues rewards as digital currency,

[0883] A database management system for managing transactions of issued digital currencies,

[0884] A transaction facilitation mechanism that enables users to conduct transactions with other users using digital currency issued by the user,

[0885] A sentiment analysis means that acquires the user's emotional state and optimizes the task execution order based on the acquired sentiment data,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, which analyzes the task information using artificial intelligence and quantifies it while taking emotional data into consideration.

[0889] (Claim 3)

[0890] The system according to claim 1, wherein the digital currency is provided with a dashboard display means for visualizing transaction records and sentiment data between users. [Explanation of Symbols]

[0891] 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 means that inputs task information and evaluates the difficulty and value of the task based on that information, A reward calculation means that calculates rewards based on evaluated task information and issues rewards as digital currency, A database management system for managing transactions of issued digital currencies, A transaction facilitation mechanism that enables users to conduct transactions with other users using digital currency issued by the user, A system that includes this.

2. The system according to claim 1, which analyzes and quantifies the aforementioned task information using artificial intelligence.

3. The system according to claim 1, wherein the digital currency is provided with a dashboard display means for visualizing transaction records between users.

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