system

Abstract

We provide the system. [Solution] The generation module analyzes location and environmental information received from mobile devices and peripheral equipment, and provides means for optimizing the workflow and travel route. A means of acquiring real-time movement data and environmental information using communication infrastructure, A means for generating optimized routes and work assignments for mobile equipment based on the analysis results and transmitting instructions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern business environment, due to the inefficiency of mobile devices and their work processes, energy waste and increased mobility costs have become problems. Such inefficiencies also require improvement from the perspective of sustainability, and it is urgent to improve energy efficiency. In addition, it is difficult to optimize business processes and movement routes in real time in conventional systems, and as a result, there are problems such as work stagnation and resource waste.

Means for Solving the Problems

[0005] This invention provides a system that uses a generation module to analyze location and environmental information received in real time from mobile devices and peripheral equipment, and optimizes work flows and travel routes based on this analysis. By using next-generation communication technology as the communication infrastructure, rapid data acquisition is possible, and the optimized route and work allocation are instructed to the mobile devices based on the analysis results. Furthermore, by including the battery level and obstacle information of the mobile devices in the analysis, more efficient and sustainable business operations are realized.

[0006] A "generation module" is a software or hardware component that analyzes received data and optimizes business workflows and travel routes.

[0007] "Mobile equipment" refers to robots and automated transport systems used to move goods within warehouses and factories.

[0008] "Peripheral devices" refer to devices such as sensors and cameras that assist the operation of mobile devices and provide environmental information.

[0009] "Location information" refers to coordinate data used by mobile devices to indicate their current location or intermediate stops.

[0010] "Environmental information" refers to data that shows the surrounding conditions and circumstances of a mobile device, including the location of obstacles and dynamic environmental changes.

[0011] A "business process flow" is a series of processes that show the steps involved in work and the flow of goods within a company.

[0012] "Travel route" refers to the path or route that a mobile device takes to reach its destination.

[0013] "Communication infrastructure" refers to the network and devices that enable the transmission and reception of data.

[0014] "Next-generation communication technology" refers to a new communication method that offers significantly improved data transmission speed and communication capacity compared to conventional methods.

[0015] "Battery remaining amount" is numerical data indicating the amount of electric power available for use by a mobile device.

[0016] "Obstacle information" is data regarding physical barriers or elements that impede movement present on the path of a mobile device.

Brief Description of the Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0022] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

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

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention provides a system in which a generation module, communication infrastructure, mobile devices, and peripheral devices work closely together so that mobile devices can efficiently perform business processes.

[0039] server

[0040] The server plays a central role in this system, storing generation modules and acquiring data from mobile devices and peripheral equipment in real time. Specifically, the server uses next-generation communication technology to rapidly collect location and environmental information and analyze it comprehensively. Based on the analysis results, the server calculates the optimal workflow and movement route and transmits these instructions to the mobile devices. For example, in a logistics center where the server is located, the server comprehensively manages the route and work procedures when robots perform the collection and delivery process.

[0041] terminal

[0042] The terminals are installed in the mobile devices and receive optimization instructions sent from the server, which are then displayed on the user interface and robot control screen. The terminals analyze the information, which is updated in real time, and immediately reflect these changes in the operation of the mobile devices. For example, a terminal operated by a warehouse manager displays obstacles and alternative routes for the robot in real time, allowing the manager to make immediate decisions.

[0043] User

[0044] The user monitors and manages the entire system. The user operates terminals to check the work progress and energy consumption of each mobile device, and adjusts processes as needed. The overall efficiency of the system improves based on the user's judgment, and user feedback is used as training data for the generation module. For example, based on the results observed by the user, the work allocation during certain time periods can be readjusted to further improve operational efficiency.

[0045] In this way, the system, centered around the generation module, facilitates the collaboration of servers, terminals, and users to achieve efficient operation of mobile devices. This optimizes business processes and enables sustainable operation with reduced energy waste.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The server receives location and environmental information in real time from mobile devices and peripheral equipment. This includes the robot's current position, the location of obstacles, and environmental data such as ambient temperature and humidity.

[0049] Step 2:

[0050] The server passes the received information to the generation module, which then integrates and analyzes the data. This analysis allows for an understanding of the overall workflow and optimizes the robot's routes and work allocation.

[0051] Step 3:

[0052] The server creates instructions for the mobile device based on the optimization results obtained from the generated module. These instructions include the route to be taken and the tasks to be performed.

[0053] Step 4:

[0054] The terminal receives instructions from the server and displays them on the mobile device. At this stage, the robot begins moving along the instructed path and performs the necessary tasks sequentially.

[0055] Step 5:

[0056] The terminal monitors the operating status of mobile equipment and changes in the surrounding environment, and sends feedback to the server as needed. This feedback information is used for reanalysis, resulting in more efficient business operations.

[0057] Step 6:

[0058] Users view the status and analysis results of mobile devices through their terminals. This allows them to monitor the progress of business processes and make adjustments to the system as needed.

[0059] Step 7:

[0060] When users identify specific problems or areas for improvement, they review the system settings and processes. This information is fed back into the generation module to improve the accuracy of subsequent analyses.

[0061] Through these steps, servers, terminals, and users collaborate to maximize the overall efficiency and sustainability of the system.

[0062] (Example 1)

[0063] 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."

[0064] For mobile devices to efficiently perform business processes, real-time collection of location and environmental data, and optimization based on that data, are necessary. However, conventional systems can experience delays in data collection and analysis, resulting in inefficient work execution and wasted energy. Furthermore, there is a lack of effective means to utilize user feedback to evolve the system.

[0065] 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.

[0066] In this invention, the server includes means for analyzing location data and environmental data using a generation module to optimize work procedures and travel routes, means for acquiring data in real time using a communication infrastructure, and means for transmitting optimized routes and work assignments based on the analysis results. This enables efficient operation of mobile devices, optimization of work processes, and sustainable operation with reduced energy waste.

[0067] A "generation module" refers to a program or algorithm that analyzes data received from mobile devices and peripheral equipment to optimize work procedures and travel routes.

[0068] "Mobile devices" refer to equipment used to perform tasks in logistics, warehousing, and other on-site locations, which provide location data and information on work progress.

[0069] "Peripheral devices" refer to devices and sensors used in conjunction with mobile equipment that assist in acquiring environmental and location information.

[0070] "Communication infrastructure" refers to communication technologies and network infrastructure used to transfer location data and environmental data from mobile devices and peripheral equipment to servers in real time.

[0071] "Analysis results" refer to the output of data processed by the generation module, which is used to optimize business procedures and travel routes.

[0072] A "user interface" refers to the screens and mechanisms that allow a user to interact with a system and exchange information.

[0073] "Feedback" refers to data collected from users regarding their user experience and usability, for the purpose of improving and optimizing the system.

[0074] A "generative AI model" refers to an artificial intelligence model that optimizes based on collected data, and is a technology used to evolutionarily improve the efficiency of business processes.

[0075] In this embodiment of the invention, a central server runs a generative AI model and analyzes data from mobile devices and peripheral equipment. The server uses next-generation communication technology to acquire and process location and environmental information in real time, and optimizes work procedures and travel routes. The server is equipped with a high-performance processor and large-capacity storage device, and has an AI model installed as a generative module. This enhances the accuracy and speed of data processing.

[0076] The terminal is mounted on the mobile device and receives optimized instructions sent from the server, displaying them through a user interface. This terminal assists in the operation of the mobile device and features a screen that displays specific work procedures and routes. The terminal performs the calculations necessary for the operation of each mobile device and reflects them in real time.

[0077] Users monitor and manage the entire system using a terminal. They track energy consumption and work progress, and provide feedback to the system as needed. This feedback is used as training data for the generation module, contributing to further system optimization.

[0078] As a concrete example, in a logistics center, a server collects information, analyzes the data, and calculates the optimal route. This instruction is sent to a terminal, and the route is displayed on the robot's control screen. The user makes immediate decisions based on this information and sends feedback to the server, allowing the generated AI model to further learn and improve its problem-solving capabilities.

[0079] As an example of a prompt statement, giving the AI ​​model the instruction to "concentrate collection operations between 9 AM and 11 AM" enables optimization based on a specific time period. This prompt statement allows the server to calculate the optimal timing and route for collection operations and send instructions to mobile devices via the terminal.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server receives location and environmental data transmitted from mobile devices and peripheral equipment via next-generation communication technology. The input data is aggregated on the server and stored in a database at high speed. This process allows the server to understand the situation in real time and prepare for subsequent data analysis.

[0083] Step 2:

[0084] The server analyzes the aggregated data using a generating AI model. It executes algorithms to optimize work procedures and travel routes using the input location and environmental data. Data processing here includes noise filtering, feature extraction, and pattern recognition, resulting in optimized work procedures and routes as output.

[0085] Step 3:

[0086] The server generates instructions for mobile devices based on the optimization results produced. The server then outputs these instructions to the terminals. These instructions include the operating timing and routing information for each device.

[0087] Step 4:

[0088] The terminal displays optimized instructions received from the server. It receives instruction data as input and displays it appropriately on the robot control screen and user interface, visually managing the operation of the mobile device via the terminal screen. Specifically, it displays obstacle information and guides the robot along its path.

[0089] Step 5:

[0090] The user monitors the entire system using information from their device and makes decisions based on the situation. The input is status information from the device, which is used to check energy consumption and work progress, generate prompts as needed, and provide feedback to the server. As a result of this output, the training data of the generated AI model is updated, further optimizing the system.

[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] In modern logistics operations, maximizing the operational efficiency of mobile equipment is crucial, but in practice, real-time information gathering and optimization are difficult. In particular, there are no systems with an intuitive user interface that takes dynamic factors such as work prioritization and energy management into account. Therefore, there is a need for systems that can build efficient workflows and minimize energy consumption.

[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] In this invention, the server includes means for analyzing location and environmental information received from mobile devices and peripheral equipment using a generation module to optimize work procedures and travel routes; means for acquiring travel data and environmental information in real time using a communication infrastructure; means for generating optimized routes and work priorities for mobile devices and transmitting instructions based on the analysis results; means for providing an intuitively operable user interface via a terminal and dynamically adjusting work priorities and routes; and means for proposing an optimal work flow based on information learned from past data and feedback using a generation artificial intelligence model. This enables more efficient logistics operations and sustainable energy management.

[0096] A "generation module" is a program function that analyzes location and environmental information received from mobile devices and peripheral equipment to optimize work procedures and travel routes.

[0097] "Communication infrastructure" refers to information network technology that acquires movement data and environmental information in real time and transmits the analysis results to mobile devices.

[0098] A "mobile device" is an autonomous or semi-autonomous machine used for transporting and organizing goods in logistics centers and warehouses.

[0099] "Operational procedures" refer to the process of optimizing a series of tasks related to logistics and the efficient transportation of goods.

[0100] A "generative artificial intelligence model" is a machine learning algorithm model that learns from past data and user feedback to propose the optimal workflow.

[0101] A "user interface" is a user-friendly system that provides a screen and operating system that workers can intuitively operate via a terminal, allowing them to adjust work priorities and routes.

[0102] An "optimized route" is a travel path calculated to reach a designated destination in the shortest possible time while minimizing energy consumption.

[0103] The system of the present invention operates in cooperation with a server, terminal, and user to achieve efficient operation of mobile devices and optimization of business processes.

[0104] The server uses a generation module to collect and analyze location and environmental information from mobile devices and surrounding equipment in real time. This analysis includes data processing using Node.js and a generative artificial intelligence model utilizing TENSORFLOW®.js. Based on information learned from past data and feedback, the server is responsible for generating optimal workflows and travel routes. The server transmits these results to terminals in real time.

[0105] The terminal features a user interface developed using React Native, designed for intuitive operation by the user. It displays optimized routes and task priorities sent from the server, allowing users to dynamically adjust task priorities and routes as needed.

[0106] Users can monitor the entire system via their terminals, checking work progress and energy consumption. In particular, in logistics centers, it's possible to understand the operating status of mobile equipment in real time and make appropriate decisions to improve operational efficiency. Furthermore, user feedback is used as further training data for the generation modules.

[0107] For example, a robot responsible for delivering fresh produce might anticipate peak congestion in the warehouse during a specific time period. In this case, the server automatically reroutes the robot's route to ensure timely delivery while maintaining the quality of the produce. An example of a prompt to the generating artificial intelligence model is: "Based on the current congestion status of the logistics center, please suggest the optimal delivery route to transport the fresh produce without damage."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server collects location and environmental information in real time from mobile devices and peripheral equipment using next-generation communication technology. It then analyzes this data to understand the current state of the mobile devices. The inputs are location and environmental information, and the output is state data based on this information.

[0111] Step 2:

[0112] The server optimizes the workflow and travel route using a generation module based on the collected data. It utilizes a generational artificial intelligence model to formulate the optimal plan based on learning from past data and feedback. The input is the state data obtained in step 1, and the output is the optimized workflow and travel route.

[0113] Step 3:

[0114] The server transmits optimized workflows and routes to the mobile devices. It sends instructions in real time via the communication infrastructure to ensure the mobile devices operate efficiently. The input is the workflow and route generated in step 2, and the output is the operation instructions for the mobile devices.

[0115] Step 4:

[0116] The terminal intuitively displays the workflow and route transmitted from the server through the user's interface. Workers review this information and modify priorities and routes as needed. Input is instruction data from the server, and output is visual information for the user.

[0117] Step 5:

[0118] The user monitors the progress of the mobile device and tasks through the terminal screen, making efficient decisions while considering energy consumption and obstacles. It can also generate prompt messages to aid in training the generation AI model. Input is visual information from the terminal, and output is the user's judgment and feedback.

[0119] 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.

[0120] This invention combines a system that optimizes business flows and travel routes using a generation module with an emotion engine that recognizes user emotions. By considering the user's emotional state, it enables more flexible and user-friendly system operation.

[0121] server

[0122] The server is the central hub of the system, receiving location and environmental information transmitted from mobile devices and peripherals via the generation module. In addition, the server integrates user emotion data analyzed by the emotion engine. The server adapts the workflow according to the user's emotional state, dynamically adjusting the mobile device's route and work schedule as needed. For example, if the server determines that the user is experiencing stress, it reduces the frequency of robot movements and decreases the number of notifications to alleviate the user's burden.

[0123] terminal

[0124] The terminal functions as an interface that provides the user with optimization instructions received from the server, both visually and audibly. It displays the user's emotional state in real time and shows work instructions that reflect feedback from the emotion engine. The terminal adjusts the interface design and the speed of information presentation to ensure the user can perform their tasks with confidence.

[0125] User

[0126] Users interact with the system via their devices during their daily work. The emotion engine recognizes emotions from the user's facial expressions and tone of voice, and provides this data to the generation module. Feedback based on the user's emotions is used to optimize work progress and maintain a bright and productive work environment. For example, if a user shows signs of anxiety or impatience, the system reduces the frequency of notifications, creating a more relaxed work environment.

[0127] In this way, by utilizing the emotion engine, servers, terminals, and users work closely together, improving the overall system performance. This enables efficient and environmentally friendly business operations, while also reducing the psychological burden on users.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server receives location and environmental information in real time from mobile devices and peripheral equipment using next-generation communication technology. This allows the server to understand the current work status and the location of robots.

[0131] Step 2:

[0132] The server analyzes emotional data collected from users by the emotion engine. This emotional data includes the user's emotional state obtained using facial recognition technology and voice analysis. This data is integrated into the business process optimization algorithm.

[0133] Step 3:

[0134] A generation module that takes emotional states into account calculates efficient workflows and travel routes. This can lead to route adjustments being made to reduce user stress. For example, it might slow down robot movements to avoid sudden actions or suggest increasing break times.

[0135] Step 4:

[0136] The terminal receives instructions from the server and provides information to the user through visual and auditory means. This is designed to make it easy for the user to understand appropriate work instructions. The terminal provides an interface that reduces the burden on the user and supports more intuitive operation.

[0137] Step 5:

[0138] Users monitor business processes through their terminals and provide feedback as needed. This feedback is analyzed by an emotion engine and used for further system optimization. Users also record their stress reduction through questionnaires, which helps improve the system.

[0139] Step 6:

[0140] The server utilizes collected user feedback to evolve the algorithms of its generated modules. Through this process, the system continuously learns how to balance user emotions with operational efficiency, thereby improving the quality of business operations.

[0141] Through these steps, the roles of the server, terminal, and user are integrated, maximizing system efficiency and user comfort.

[0142] (Example 2)

[0143] 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".

[0144] Traditional business management systems optimize workflows and travel routes without considering the user's emotional state, potentially placing an excessive burden on users. Furthermore, limitations in the energy efficiency and obstacle avoidance capabilities of mobile devices can reduce the efficiency of business operations. Additionally, insufficient real-time information updates can lead to a lack of rapid adaptability.

[0145] 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.

[0146] In this invention, the server includes means for analyzing location information and environmental information received from mobile devices and peripheral devices using a generation module to optimize work activities and travel routes; means for estimating the user's emotional state using an emotion analysis device and reflecting it in work activities; and means for generating optimized routes and work assignments adapted to the user's emotional state using a generation AI model and transmitting instructions. This enables flexible work operations that respond to the user's emotions, resulting in efficient and less burdensome system operation.

[0147] A "generation module" is a program or device that optimizes business activities and travel routes based on location information and environmental information acquired from mobile devices and peripheral equipment.

[0148] A "mobile device" is a device that possesses location information and is used to move through space along a specific route, and that has the function of performing normal tasks.

[0149] "Peripheral devices" are devices or tools that operate in conjunction with mobile devices and are used to provide or collect information necessary for business operations.

[0150] "Location information" refers to data indicating the current geographical location of a mobile device or peripheral equipment, which the system uses to optimize its travel path.

[0151] "Environmental information" refers to data that describes the physical or measurable surrounding conditions, and is necessary for mobile devices and peripheral equipment to function properly.

[0152] An "emotion analysis device" is a device or algorithm used to analyze a user's voice, facial expressions, and behavioral data to estimate the user's emotional state.

[0153] A "generative AI model" is a program or mathematical model that uses artificial intelligence technology to generate optimized workflows and routes based on user emotions and environment.

[0154] A "communication infrastructure" is a network infrastructure used to exchange location information, environmental information, instructions, and other data with mobile devices and peripheral equipment in real time.

[0155] "Advanced communication technology" refers to cutting-edge technologies aimed at improving the efficiency and speed of data communication, and typically includes next-generation communication protocols and hardware.

[0156] This invention is a system that optimizes business activities based on information acquired from mobile devices and peripheral equipment. The system consists of components including a generation module, an emotion analysis device, a generation AI model, and a communication infrastructure.

[0157] server

[0158] The server first utilizes a generation module to collect location and environmental information acquired from mobile devices and peripheral equipment. This includes periodically acquiring information via a data reception API. Next, the server uses an emotion analysis device to estimate the user's emotional state in real time. The analysis takes voice and facial expression data as input and performs analysis using machine learning algorithms. The server integrates these analysis results into a generation AI model to optimize the workflow and travel route according to the user's emotional state. This process makes conventional business operations more flexible and user-friendly.

[0159] terminal

[0160] The terminal provides users with optimization instructions sent from the server visually and audibly. Specifically, it displays work instructions in real time through the user interface and presents important information based on feedback from the emotion analysis device. This allows users to perform their tasks with confidence through the system. The terminal also adjusts the interface design and the speed of information presentation to provide appropriate information according to the user's emotional state.

[0161] User

[0162] Users interact with the system via terminals during their daily work. An emotion analysis device analyzes the user's facial expressions and voice tone to recognize emotions and passes this data to a generation module. Based on this feedback, work processes are optimized, maintaining a brighter and more productive work environment.

[0163] Specific example

[0164] For example, if an emotion analysis device determines that a user working in an office is experiencing stress, the server will reduce the frequency of the robot's actions and decrease the number of notifications. This allows the user to work in a more relaxed environment.

[0165] Example of a prompt

[0166] For example, it's possible to input a prompt like, "If a user shows signs of anxiety, please suggest how to adjust the notification frequency," into a generating AI model and have it derive the optimal workflow.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server receives location and environmental information from mobile devices and peripheral equipment. This information is retrieved periodically using an API. The input data includes location information and environmental data such as temperature and humidity. The server preprocesses this data and performs data cleansing, such as imputing missing values. It generates a clean dataset as output.

[0170] Step 2:

[0171] The server collects user voice and facial expression data transmitted from the terminal. Voice data is converted to text via a speech recognition system, and facial expression data is analyzed using an image processing algorithm. Input is user voice and image data. Data analysis provides a numerical representation of the user's emotional state. Output is a set of emotion scores.

[0172] Step 3:

[0173] The server uses a generative AI model to integrate clean location and environmental data with sentiment scores. First, the AI ​​model receives the dynamic environment and sentiment state as input and calculates the optimal workflow and travel route. A multi-layer neural network is used for data processing, resulting in an optimized work plan being output.

[0174] Step 4:

[0175] The server generates specific instructions based on the output of the generated AI model and sends them to the terminal. The input is a work plan with optimized instructions already set. The server pushes this information to the terminal in real time and provides the user with actionable task instructions as output.

[0176] Step 5:

[0177] The terminal displays received instructions through a user interface. The user receives work instructions sent from the server as visual and auditory information. Input consists of work flow instructions from previous stages. The terminal displays these clearly to the user and collects feedback. Output is the presentation of information to the user.

[0178] Step 6:

[0179] The user performs tasks according to instructions from the terminal. As feedback, they provide newly generated voice and motion data. This data is returned to the server and incorporated again into the processing from step 1. The input is the user's feedback information to the server. The output is a continuous data improvement cycle.

[0180] (Application Example 2)

[0181] 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".

[0182] In today's work environment, there is a demand for both increased efficiency in work processes and reduced emotional burden on workers. In particular, the current situation lacks the optimal operation of mobile equipment and dynamic adjustment of work flows that take into account the emotional state of workers. Therefore, technologies are needed that can improve work efficiency while reducing worker stress and fatigue.

[0183] 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.

[0184] In this invention, the server includes means for analyzing location information and environmental information received from mobile devices and peripheral devices by a generation module to optimize work processes and travel routes; means for acquiring travel data and environmental information in real time using communication equipment; means for generating optimized routes and work assignments for mobile devices based on the analysis results and transmitting instructions; means for recognizing emotional states using an emotion engine and flexibly adjusting work processes based on the recognition results; and means for providing visual and auditory feedback through worker devices and providing relaxation guidance tailored to the worker's emotions. This makes it possible to improve work efficiency and reduce the emotional burden on workers.

[0185] A "generation module" is a device or software that analyzes data from mobile devices and peripheral devices to optimize work processes and travel routes.

[0186] A "mobile device" is a machine or device that operates based on location information and performs a specific task or transport.

[0187] A "peripheral device" is a device that operates in conjunction with a mobile device and provides additional information or functions.

[0188] "Location information" refers to data that indicates the geographical location of mobile devices and peripheral devices.

[0189] "Environmental information" refers to data that shows the conditions of the mobile device and its surroundings, and includes temperature, humidity, and other factors.

[0190] A "business process" refers to the procedures and steps for efficiently executing a specific task or process.

[0191] "Travel path" refers to information that indicates the route or course that a mobile device follows when it moves.

[0192] "Communication equipment" refers to physical or virtual infrastructure or technology for sending and receiving data.

[0193] "Real-time" refers to the ability to instantly check or manipulate the situation currently unfolding.

[0194] "Analysis results" refer to the information and conclusions obtained after analyzing data.

[0195] "Work allocation" refers to the allocation of resources and time to individual tasks within a business process.

[0196] "Instructions" are commands or guidelines given to perform a task or action.

[0197] An "emotion engine" is a technology that analyzes a user's emotions and psychological state and outputs the results.

[0198] "Emotional state" is a temporary expression that describes an individual's psychological feelings.

[0199] A "worker device" is a device used by a worker that functions as an information receiver and interface.

[0200] "Feedback" refers to information about reactions or responses given in response to actions or situations.

[0201] "Relaxation guidance" refers to advice and suggestions for reducing stress and stabilizing the mind and body.

[0202] The system of this invention is composed of three main components: a server, a terminal, and a user.

[0203] First, the server functions as the central processing unit, utilizing a generation module, communication equipment, and an emotion engine. The generation module analyzes location and environmental information acquired from mobile devices and peripheral devices to dynamically optimize work processes and travel routes. The server further acquires this information in real time and, based on the analysis results, instructs the mobile devices on optimized routes and work allocation. The emotion engine recognizes the user's emotional state, such as facial expressions and voice, and sends this data to the coordinating module. This allows for flexible adaptation of work processes according to the user's emotional state. Specifically, if a user experiences stress, the system adjusts the route and notification frequency to reduce the workload.

[0204] Next, the terminal functions as an interface with the user, providing instructions from the server visually and audibly. The terminal includes worker devices that provide emotionally responsive feedback and guidance to promote relaxation. This allows users to understand their own emotional state and make appropriate decisions to perform their tasks smoothly.

[0205] Finally, users engage in their daily tasks through this system and receive information from their terminals. When the emotion engine recognizes the user's emotions, that information is reflected in the work process via the server, allowing users to maintain a more comfortable and efficient work environment.

[0206] As a concrete example, factory workers use smart devices to record their emotional state, and a server analyzes this data to automatically adjust work processes and break times. In this way, system operation that takes the user's emotional state into consideration is realized. An example of a prompt sentence to be input into the generating AI model is, "What are some ways to reduce the fatigue you are feeling in your current work situation?"

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The server acquires location and environmental information from mobile devices and peripheral devices in real time. It processes this data as input and performs data manipulation. Specifically, information from GPS sensors and environmental sensors is stored in a database, and preparations for analysis are made.

[0210] Step 2:

[0211] The server analyzes location and environmental information acquired using a generation module. Based on the input data, it calculates the optimal work process and travel route. This analysis utilizes historical data and predictive algorithms to generate an optimized operation plan. The output is the optimized route and work allocation.

[0212] Step 3:

[0213] The server uses an emotion engine to analyze the user's facial recognition and voice data to recognize their emotional state. This emotional data is then input, and the server uses data calculations to determine the user's emotional state. The output is a determination of whether the user is relaxed, stressed, or otherwise unsettled.

[0214] Step 4:

[0215] The server flexibly adjusts tasks based on the generated task processes and emotion assessment results. Using task process data and emotion assessment results as input, it processes the data to create an action plan that includes optimized notification frequency and route changes. The adjusted action plan is obtained as output.

[0216] Step 5:

[0217] The terminal receives an action plan sent from the server and provides it to the user visually and audibly. It takes the action plan as input and presents the information in a user-friendly format as output. In this step, it displays appropriate guidance to help the user relax when they are feeling stressed.

[0218] Step 6:

[0219] Users perform tasks based on information from their devices and provide feedback to the system. Specifically, changes in the user's facial expressions and voice are fed back to the system and used as input data for the next analysis. This cycle allows the system to continuously provide the user with an optimal environment.

[0220] 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.

[0221] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] This invention provides a system in which a generation module, communication infrastructure, mobile devices, and peripheral devices work closely together so that mobile devices can efficiently perform business processes.

[0237] server

[0238] The server plays a central role in this system, storing generation modules and acquiring data from mobile devices and peripheral equipment in real time. Specifically, the server uses next-generation communication technology to rapidly collect location and environmental information and analyze it comprehensively. Based on the analysis results, the server calculates the optimal workflow and movement route and transmits these instructions to the mobile devices. For example, in a logistics center where the server is located, the server comprehensively manages the route and work procedures when robots perform the collection and delivery process.

[0239] terminal

[0240] The terminals are installed in the mobile devices and receive optimization instructions sent from the server, which are then displayed on the user interface and robot control screen. The terminals analyze the information, which is updated in real time, and immediately reflect these changes in the operation of the mobile devices. For example, a terminal operated by a warehouse manager displays obstacles and alternative routes for the robot in real time, allowing the manager to make immediate decisions.

[0241] User

[0242] The user monitors and manages the entire system. The user operates terminals to check the work progress and energy consumption of each mobile device, and adjusts processes as needed. The overall efficiency of the system improves based on the user's judgment, and user feedback is used as training data for the generation module. For example, based on the results observed by the user, the work allocation during certain time periods can be readjusted to further improve operational efficiency.

[0243] In this way, the system, centered around the generation module, facilitates the collaboration of servers, terminals, and users to achieve efficient operation of mobile devices. This optimizes business processes and enables sustainable operation with reduced energy waste.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The server receives location and environmental information in real time from mobile devices and peripheral equipment. This includes the robot's current position, the location of obstacles, and environmental data such as ambient temperature and humidity.

[0247] Step 2:

[0248] The server passes the received information to the generation module, which then integrates and analyzes the data. This analysis allows for an understanding of the overall workflow and optimizes the robot's routes and work allocation.

[0249] Step 3:

[0250] The server creates instructions for the mobile device based on the optimization results obtained from the generated module. These instructions include the route to be taken and the tasks to be performed.

[0251] Step 4:

[0252] The terminal receives instructions from the server and displays them on the mobile device. At this stage, the robot begins moving along the instructed path and performs the necessary tasks sequentially.

[0253] Step 5:

[0254] The terminal monitors the operating status of mobile equipment and changes in the surrounding environment, and sends feedback to the server as needed. This feedback information is used for reanalysis, resulting in more efficient business operations.

[0255] Step 6:

[0256] Users view the status and analysis results of mobile devices through their terminals. This allows them to monitor the progress of business processes and make adjustments to the system as needed.

[0257] Step 7:

[0258] When users identify specific problems or areas for improvement, they review the system settings and processes. This information is fed back into the generation module to improve the accuracy of subsequent analyses.

[0259] Through these steps, servers, terminals, and users collaborate to maximize the overall efficiency and sustainability of the system.

[0260] (Example 1)

[0261] 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."

[0262] For mobile devices to efficiently perform business processes, real-time collection of location and environmental data, and optimization based on that data, are necessary. However, conventional systems can experience delays in data collection and analysis, resulting in inefficient work execution and wasted energy. Furthermore, there is a lack of effective means to utilize user feedback to evolve the system.

[0263] 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.

[0264] In this invention, the server includes means for analyzing location data and environmental data using a generation module to optimize work procedures and travel routes, means for acquiring data in real time using a communication infrastructure, and means for transmitting optimized routes and work assignments based on the analysis results. This enables efficient operation of mobile devices, optimization of work processes, and sustainable operation with reduced energy waste.

[0265] A "generation module" refers to a program or algorithm that analyzes data received from mobile devices and peripheral equipment to optimize work procedures and travel routes.

[0266] "Mobile devices" refer to equipment used to perform tasks in logistics, warehousing, and other on-site locations, which provide location data and information on work progress.

[0267] "Peripheral devices" refer to devices and sensors used in conjunction with mobile equipment that assist in acquiring environmental and location information.

[0268] "Communication infrastructure" refers to communication technologies and network infrastructure used to transfer location data and environmental data from mobile devices and peripheral equipment to servers in real time.

[0269] "Analysis results" refer to the output of data processed by the generation module, which is used to optimize business procedures and travel routes.

[0270] A "user interface" refers to the screens and mechanisms that allow a user to interact with a system and exchange information.

[0271] "Feedback" refers to data collected from users regarding their user experience and usability, for the purpose of improving and optimizing the system.

[0272] A "generative AI model" refers to an artificial intelligence model that optimizes based on collected data, and is a technology used to evolutionarily improve the efficiency of business processes.

[0273] In this embodiment of the invention, a central server runs a generative AI model and analyzes data from mobile devices and peripheral equipment. The server uses next-generation communication technology to acquire and process location and environmental information in real time, and optimizes work procedures and travel routes. The server is equipped with a high-performance processor and large-capacity storage device, and has an AI model installed as a generative module. This enhances the accuracy and speed of data processing.

[0274] The terminal is mounted on the mobile device and receives optimized instructions sent from the server, displaying them through a user interface. This terminal assists in the operation of the mobile device and features a screen that displays specific work procedures and routes. The terminal performs the calculations necessary for the operation of each mobile device and reflects them in real time.

[0275] Users monitor and manage the entire system using a terminal. They track energy consumption and work progress, and provide feedback to the system as needed. This feedback is used as training data for the generation module, contributing to further system optimization.

[0276] As a concrete example, in a logistics center, a server collects information, analyzes the data, and calculates the optimal route. This instruction is sent to a terminal, and the route is displayed on the robot's control screen. The user makes immediate decisions based on this information and sends feedback to the server, allowing the generated AI model to further learn and improve its problem-solving capabilities.

[0277] As an example of a prompt statement, giving the AI ​​model the instruction to "concentrate collection operations between 9 AM and 11 AM" enables optimization based on a specific time period. This prompt statement allows the server to calculate the optimal timing and route for collection operations and send instructions to mobile devices via the terminal.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The server receives location and environmental data transmitted from mobile devices and peripheral equipment via next-generation communication technology. The input data is aggregated on the server and stored in a database at high speed. This process allows the server to understand the situation in real time and prepare for subsequent data analysis.

[0281] Step 2:

[0282] The server analyzes the aggregated data using a generative AI model. It executes an algorithm to optimize business procedures and movement routes using the input position data and environmental data. The data processing here includes noise filtering, feature extraction, and pattern recognition, and optimized business procedures and routes are generated as output.

[0283] Step 3:

[0284] Based on the generated optimization results, the server generates instructions for the mobile device. The server outputs by sending the instructions to the terminal. These instructions include the operation timing and route information of each device.

[0285] Step 4:

[0286] The terminal displays the optimized instructions received from the server. By receiving the instruction data as input and appropriately displaying it on the robot control screen or user interface, the operation of the mobile device is visually managed through the terminal screen. Specific operations include displaying obstacle information and guiding the route.

[0287] Step 5:

[0288] The user monitors the entire system using the terminal information and makes judgments according to the situation. The input is the situation information from the terminal. Based on this, the user checks the energy consumption and work progress, generates prompts as needed, and provides feedback to the server. As a result of this output, the learning data of the generative AI model is updated, and further optimization of the system is achieved.

[0289] (Application Example 1)

[0290] 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".

[0291] In modern logistics operations, maximizing the operational efficiency of mobile equipment is crucial, but in practice, real-time information gathering and optimization are difficult. In particular, there are no systems with an intuitive user interface that takes dynamic factors such as work prioritization and energy management into account. Therefore, there is a need for systems that can build efficient workflows and minimize energy consumption.

[0292] 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.

[0293] In this invention, the server includes means for analyzing location and environmental information received from mobile devices and peripheral equipment using a generation module to optimize work procedures and travel routes; means for acquiring travel data and environmental information in real time using a communication infrastructure; means for generating optimized routes and work priorities for mobile devices and transmitting instructions based on the analysis results; means for providing an intuitively operable user interface via a terminal and dynamically adjusting work priorities and routes; and means for proposing an optimal work flow based on information learned from past data and feedback using a generation artificial intelligence model. This enables more efficient logistics operations and sustainable energy management.

[0294] A "generation module" is a program function that analyzes location and environmental information received from mobile devices and peripheral equipment to optimize work procedures and travel routes.

[0295] "Communication infrastructure" refers to information network technology that acquires movement data and environmental information in real time and transmits the analysis results to mobile devices.

[0296] A "mobile device" is an autonomous or semi-autonomous machine used for transporting and organizing goods in logistics centers and warehouses.

[0297] "Operational procedures" refer to the process of optimizing a series of tasks related to logistics and the efficient transportation of goods.

[0298] A "generative artificial intelligence model" is a machine learning algorithm model that learns from past data and user feedback to propose the optimal workflow.

[0299] A "user interface" is a user-friendly system that provides a screen and operating system that workers can intuitively operate via a terminal, allowing them to adjust work priorities and routes.

[0300] An "optimized route" is a travel path calculated to reach a designated destination in the shortest possible time while minimizing energy consumption.

[0301] The system of the present invention operates in cooperation with a server, terminal, and user to achieve efficient operation of mobile devices and optimization of business processes.

[0302] The server uses a generation module to collect and analyze location and environmental information from mobile devices and surrounding equipment in real time. This analysis includes data processing using Node.js and a generative artificial intelligence model utilizing TensorFlow.js. Based on information learned from past data and feedback, the server is responsible for generating optimal workflows and travel routes. The server then transmits these results to the terminal in real time.

[0303] The terminal features a user interface developed using React Native, designed for intuitive operation by the user. It displays optimized routes and task priorities sent from the server, allowing users to dynamically adjust task priorities and routes as needed.

[0304] The user can monitor the entire system via the terminal and check the work progress and energy consumption. Especially in a logistics center, it is possible to grasp the operating status of mobile devices in real time and make appropriate decisions to improve the operation efficiency. Also, the feedback from the user is utilized as additional learning data for the generation module.

[0305] As a specific example, there may be a case where a robot responsible for delivering fresh produce predicts that the congestion in the warehouse will peak during a specific time period. At this time, the server automatically detours the route of the mobile device to enable timely delivery while maintaining the quality of the fresh produce. An example of a prompt sentence for the generation artificial intelligence model is, "Please propose the optimal delivery route for transporting fresh produce without damage based on the current congestion situation in the logistics center."

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The server collects the position information and environmental information in real time from the mobile device and peripheral equipment using the next-generation communication technology. Analyze the data obtained thereby to grasp the current state of the mobile device. The input is the position information and environmental information, and the output is the state data based on these information.

[0309] Step 2:

[0310] The server optimizes the business process and movement route using the generation module based on the collected data. Utilize the generation artificial intelligence model to formulate an optimal plan from the results of learning past data and feedback. The input is the state data obtained in Step 1, and the output is the optimized business process and movement route.

[0311] Step 3:

[0312] The server transmits optimized workflows and routes to the mobile devices. It sends instructions in real time via the communication infrastructure to ensure the mobile devices operate efficiently. The input is the workflow and route generated in step 2, and the output is the operation instructions for the mobile devices.

[0313] Step 4:

[0314] The terminal intuitively displays the workflow and route transmitted from the server through the user's interface. Workers review this information and modify priorities and routes as needed. Input is instruction data from the server, and output is visual information for the user.

[0315] Step 5:

[0316] The user monitors the progress of the mobile device and tasks through the terminal screen, making efficient decisions while considering energy consumption and obstacles. It can also generate prompt messages to aid in training the generation AI model. Input is visual information from the terminal, and output is the user's judgment and feedback.

[0317] 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.

[0318] This invention combines a system that optimizes business flows and travel routes using a generation module with an emotion engine that recognizes user emotions. By considering the user's emotional state, it enables more flexible and user-friendly system operation.

[0319] server

[0320] The server is the central hub of the system, receiving location and environmental information transmitted from mobile devices and peripherals via the generation module. In addition, the server integrates user emotion data analyzed by the emotion engine. The server adapts the workflow according to the user's emotional state, dynamically adjusting the mobile device's route and work schedule as needed. For example, if the server determines that the user is experiencing stress, it reduces the frequency of robot movements and decreases the number of notifications to alleviate the user's burden.

[0321] terminal

[0322] The terminal functions as an interface that provides the user with optimization instructions received from the server, both visually and audibly. It displays the user's emotional state in real time and shows work instructions that reflect feedback from the emotion engine. The terminal adjusts the interface design and the speed of information presentation to ensure the user can perform their tasks with confidence.

[0323] User

[0324] Users interact with the system via their devices during their daily work. The emotion engine recognizes emotions from the user's facial expressions and tone of voice, and provides this data to the generation module. Feedback based on the user's emotions is used to optimize work progress and maintain a bright and productive work environment. For example, if a user shows signs of anxiety or impatience, the system reduces the frequency of notifications, creating a more relaxed work environment.

[0325] In this way, by utilizing the emotion engine, servers, terminals, and users work closely together, improving the overall system performance. This enables efficient and environmentally friendly business operations, while also reducing the psychological burden on users.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The server receives location and environmental information in real time from mobile devices and peripheral equipment using next-generation communication technology. This allows the server to understand the current work status and the location of robots.

[0329] Step 2:

[0330] The server analyzes emotional data collected from users by the emotion engine. This emotional data includes the user's emotional state obtained using facial recognition technology and voice analysis. This data is integrated into the business process optimization algorithm.

[0331] Step 3:

[0332] A generation module that takes emotional states into account calculates efficient workflows and travel routes. This can lead to route adjustments being made to reduce user stress. For example, it might slow down robot movements to avoid sudden actions or suggest increasing break times.

[0333] Step 4:

[0334] The terminal receives instructions from the server and provides information to the user through visual and auditory means. This is designed to make it easy for the user to understand appropriate work instructions. The terminal provides an interface that reduces the burden on the user and supports more intuitive operation.

[0335] Step 5:

[0336] Users monitor business processes through their terminals and provide feedback as needed. This feedback is analyzed by an emotion engine and used for further system optimization. Users also record their stress reduction through questionnaires, which helps improve the system.

[0337] Step 6:

[0338] The server utilizes collected user feedback to evolve the algorithms of its generated modules. Through this process, the system continuously learns how to balance user emotions with operational efficiency, thereby improving the quality of business operations.

[0339] Through these steps, the roles of the server, terminal, and user are integrated, maximizing system efficiency and user comfort.

[0340] (Example 2)

[0341] 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".

[0342] Traditional business management systems optimize workflows and travel routes without considering the user's emotional state, potentially placing an excessive burden on users. Furthermore, limitations in the energy efficiency and obstacle avoidance capabilities of mobile devices can reduce the efficiency of business operations. Additionally, insufficient real-time information updates can lead to a lack of rapid adaptability.

[0343] 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.

[0344] In this invention, the server includes means for analyzing location information and environmental information received from mobile devices and peripheral devices using a generation module to optimize work activities and travel routes; means for estimating the user's emotional state using an emotion analysis device and reflecting it in work activities; and means for generating optimized routes and work assignments adapted to the user's emotional state using a generation AI model and transmitting instructions. This enables flexible work operations that respond to the user's emotions, resulting in efficient and less burdensome system operation.

[0345] A "generation module" is a program or device that optimizes business activities and travel routes based on location information and environmental information acquired from mobile devices and peripheral equipment.

[0346] A "mobile device" is a device that possesses location information and is used to move through space along a specific route, and that has the function of performing normal tasks.

[0347] "Peripheral devices" are devices or tools that operate in conjunction with mobile devices and are used to provide or collect information necessary for business operations.

[0348] "Location information" refers to data indicating the current geographical location of a mobile device or peripheral equipment, which the system uses to optimize its travel path.

[0349] "Environmental information" refers to data that describes the physical or measurable surrounding conditions, and is necessary for mobile devices and peripheral equipment to function properly.

[0350] An "emotion analysis device" is a device or algorithm used to analyze a user's voice, facial expressions, and behavioral data to estimate the user's emotional state.

[0351] A "generative AI model" is a program or mathematical model that uses artificial intelligence technology to generate optimized workflows and routes based on user emotions and environment.

[0352] A "communication infrastructure" is a network infrastructure used to exchange location information, environmental information, instructions, and other data with mobile devices and peripheral equipment in real time.

[0353] "Advanced communication technology" refers to cutting-edge technologies aimed at improving the efficiency and speed of data communication, and typically includes next-generation communication protocols and hardware.

[0354] This invention is a system that optimizes business activities based on information acquired from mobile devices and peripheral equipment. The system consists of components including a generation module, an emotion analysis device, a generation AI model, and a communication infrastructure.

[0355] server

[0356] The server first utilizes a generation module to collect location and environmental information acquired from mobile devices and peripheral equipment. This includes periodically acquiring information via a data reception API. Next, the server uses an emotion analysis device to estimate the user's emotional state in real time. The analysis takes voice and facial expression data as input and performs analysis using machine learning algorithms. The server integrates these analysis results into a generation AI model to optimize the workflow and travel route according to the user's emotional state. This process makes conventional business operations more flexible and user-friendly.

[0357] terminal

[0358] The terminal provides users with optimization instructions sent from the server visually and audibly. Specifically, it displays work instructions in real time through the user interface and presents important information based on feedback from the emotion analysis device. This allows users to perform their tasks with confidence through the system. The terminal also adjusts the interface design and the speed of information presentation to provide appropriate information according to the user's emotional state.

[0359] User

[0360] Users interact with the system via terminals during their daily work. An emotion analysis device analyzes the user's facial expressions and voice tone to recognize emotions and passes this data to a generation module. Based on this feedback, work processes are optimized, maintaining a brighter and more productive work environment.

[0361] Specific example

[0362] For example, if an emotion analysis device determines that a user working in an office is experiencing stress, the server will reduce the frequency of the robot's actions and decrease the number of notifications. This allows the user to work in a more relaxed environment.

[0363] Example of a prompt

[0364] For example, it's possible to input a prompt like, "If a user shows signs of anxiety, please suggest how to adjust the notification frequency," into a generating AI model and have it derive the optimal workflow.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The server receives location and environmental information from mobile devices and peripheral equipment. This information is retrieved periodically using an API. The input data includes location information and environmental data such as temperature and humidity. The server preprocesses this data and performs data cleansing, such as imputing missing values. It generates a clean dataset as output.

[0368] Step 2:

[0369] The server collects user voice and facial expression data transmitted from the terminal. Voice data is converted to text via a speech recognition system, and facial expression data is analyzed using an image processing algorithm. Input is user voice and image data. Data analysis provides a numerical representation of the user's emotional state. Output is a set of emotion scores.

[0370] Step 3:

[0371] The server uses a generative AI model to integrate clean location and environmental data with sentiment scores. First, the AI ​​model receives the dynamic environment and sentiment state as input and calculates the optimal workflow and travel route. A multi-layer neural network is used for data processing, resulting in an optimized work plan being output.

[0372] Step 4:

[0373] The server generates specific instructions based on the output of the generated AI model and sends them to the terminal. The input is a work plan with optimized instructions already set. The server pushes this information to the terminal in real time and provides the user with actionable task instructions as output.

[0374] Step 5:

[0375] The terminal displays received instructions through a user interface. The user receives work instructions sent from the server as visual and auditory information. Input consists of work flow instructions from previous stages. The terminal displays these clearly to the user and collects feedback. Output is the presentation of information to the user.

[0376] Step 6:

[0377] The user performs tasks according to instructions from the terminal. As feedback, they provide newly generated voice and motion data. This data is returned to the server and incorporated again into the processing from step 1. The input is the user's feedback information to the server. The output is a continuous data improvement cycle.

[0378] (Application Example 2)

[0379] 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."

[0380] In today's work environment, there is a demand for both increased efficiency in work processes and reduced emotional burden on workers. In particular, the current situation lacks the optimal operation of mobile equipment and dynamic adjustment of work flows that take into account the emotional state of workers. Therefore, technologies are needed that can improve work efficiency while reducing worker stress and fatigue.

[0381] 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.

[0382] In this invention, the server includes means for analyzing location information and environmental information received from mobile devices and peripheral devices by a generation module to optimize work processes and travel routes; means for acquiring travel data and environmental information in real time using communication equipment; means for generating optimized routes and work assignments for mobile devices based on the analysis results and transmitting instructions; means for recognizing emotional states using an emotion engine and flexibly adjusting work processes based on the recognition results; and means for providing visual and auditory feedback through worker devices and providing relaxation guidance tailored to the worker's emotions. This makes it possible to improve work efficiency and reduce the emotional burden on workers.

[0383] A "generation module" is a device or software that analyzes data from mobile devices and peripheral devices to optimize work processes and travel routes.

[0384] A "mobile device" is a machine or device that operates based on location information and performs a specific task or transport.

[0385] A "peripheral device" is a device that operates in conjunction with a mobile device and provides additional information or functions.

[0386] "Location information" refers to data that indicates the geographical location of mobile devices and peripheral devices.

[0387] "Environmental information" refers to data that shows the conditions of the mobile device and its surroundings, and includes temperature, humidity, and other factors.

[0388] A "business process" refers to the procedures and steps for efficiently executing a specific task or process.

[0389] "Travel path" refers to information that indicates the route or course that a mobile device follows when it moves.

[0390] "Communication equipment" refers to physical or virtual infrastructure or technology for sending and receiving data.

[0391] "Real-time" refers to the ability to instantly check or manipulate the situation currently unfolding.

[0392] "Analysis results" refer to the information and conclusions obtained after analyzing data.

[0393] "Work allocation" refers to the allocation of resources and time to individual tasks within a business process.

[0394] "Instructions" are commands or guidelines given to perform a task or action.

[0395] An "emotion engine" is a technology that analyzes a user's emotions and psychological state and outputs the results.

[0396] "Emotional state" is a temporary expression that describes an individual's psychological feelings.

[0397] A "worker device" is a device used by a worker that functions as an information receiver and interface.

[0398] "Feedback" refers to information about reactions or responses given in response to actions or situations.

[0399] "Relaxation guidance" refers to advice and suggestions for reducing stress and stabilizing the mind and body.

[0400] The system of this invention is composed of three main components: a server, a terminal, and a user.

[0401] First, the server functions as the central processing unit, utilizing a generation module, communication equipment, and an emotion engine. The generation module analyzes location and environmental information acquired from mobile devices and peripheral devices to dynamically optimize work processes and travel routes. The server further acquires this information in real time and, based on the analysis results, instructs the mobile devices on optimized routes and work allocation. The emotion engine recognizes the user's emotional state, such as facial expressions and voice, and sends this data to the coordinating module. This allows for flexible adaptation of work processes according to the user's emotional state. Specifically, if a user experiences stress, the system adjusts the route and notification frequency to reduce the workload.

[0402] Next, the terminal functions as an interface with the user, providing instructions from the server visually and audibly. The terminal includes worker devices that provide emotionally responsive feedback and guidance to promote relaxation. This allows users to understand their own emotional state and make appropriate decisions to perform their tasks smoothly.

[0403] Finally, users engage in their daily tasks through this system and receive information from their terminals. When the emotion engine recognizes the user's emotions, that information is reflected in the work process via the server, allowing users to maintain a more comfortable and efficient work environment.

[0404] As a concrete example, factory workers use smart devices to record their emotional state, and a server analyzes this data to automatically adjust work processes and break times. In this way, system operation that takes the user's emotional state into consideration is realized. An example of a prompt sentence to be input into the generating AI model is, "What are some ways to reduce the fatigue you are feeling in your current work situation?"

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] The server acquires location and environmental information from mobile devices and peripheral devices in real time. It processes this data as input and performs data manipulation. Specifically, information from GPS sensors and environmental sensors is stored in a database, and preparations for analysis are made.

[0408] Step 2:

[0409] The server analyzes location and environmental information acquired using a generation module. Based on the input data, it calculates the optimal work process and travel route. This analysis utilizes historical data and predictive algorithms to generate an optimized operation plan. The output is the optimized route and work allocation.

[0410] Step 3:

[0411] The server uses an emotion engine to analyze the user's facial recognition and voice data to recognize their emotional state. This emotional data is then input, and the server uses data calculations to determine the user's emotional state. The output is a determination of whether the user is relaxed, stressed, or otherwise unsettled.

[0412] Step 4:

[0413] The server flexibly adjusts tasks based on the generated task processes and emotion assessment results. Using task process data and emotion assessment results as input, it processes the data to create an action plan that includes optimized notification frequency and route changes. The adjusted action plan is obtained as output.

[0414] Step 5:

[0415] The terminal receives an action plan sent from the server and provides it to the user visually and audibly. It takes the action plan as input and presents the information in a user-friendly format as output. In this step, it displays appropriate guidance to help the user relax when they are feeling stressed.

[0416] Step 6:

[0417] Users perform tasks based on information from their devices and provide feedback to the system. Specifically, changes in the user's facial expressions and voice are fed back to the system and used as input data for the next analysis. This cycle allows the system to continuously provide the user with an optimal environment.

[0418] 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.

[0419] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0420] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] 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.

[0424] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0425] The 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.

[0426] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0427] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0428] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0429] Figure 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.

[0430] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0431] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0432] In the 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.

[0433] 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".

[0434] This invention provides a system in which a generation module, communication infrastructure, mobile devices, and peripheral devices work closely together so that mobile devices can efficiently perform business processes.

[0435] server

[0436] The server plays a central role in this system, storing generation modules and acquiring data from mobile devices and peripheral equipment in real time. Specifically, the server uses next-generation communication technology to rapidly collect location and environmental information and analyze it comprehensively. Based on the analysis results, the server calculates the optimal workflow and movement route and transmits these instructions to the mobile devices. For example, in a logistics center where the server is located, the server comprehensively manages the route and work procedures when robots perform the collection and delivery process.

[0437] terminal

[0438] The terminals are installed in the mobile devices and receive optimization instructions sent from the server, which are then displayed on the user interface and robot control screen. The terminals analyze the information, which is updated in real time, and immediately reflect these changes in the operation of the mobile devices. For example, a terminal operated by a warehouse manager displays obstacles and alternative routes for the robot in real time, allowing the manager to make immediate decisions.

[0439] User

[0440] The user monitors and manages the entire system. The user operates terminals to check the work progress and energy consumption of each mobile device, and adjusts processes as needed. The overall efficiency of the system improves based on the user's judgment, and user feedback is used as training data for the generation module. For example, based on the results observed by the user, the work allocation during certain time periods can be readjusted to further improve operational efficiency.

[0441] In this way, the system, centered around the generation module, facilitates the collaboration of servers, terminals, and users to achieve efficient operation of mobile devices. This optimizes business processes and enables sustainable operation with reduced energy waste.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] The server receives location and environmental information in real time from mobile devices and peripheral equipment. This includes the robot's current position, the location of obstacles, and environmental data such as ambient temperature and humidity.

[0445] Step 2:

[0446] The server passes the received information to the generation module, which then integrates and analyzes the data. This analysis allows for an understanding of the overall workflow and optimizes the robot's routes and work allocation.

[0447] Step 3:

[0448] The server creates instructions for the mobile device based on the optimization results obtained from the generated module. These instructions include the route to be taken and the tasks to be performed.

[0449] Step 4:

[0450] The terminal receives instructions from the server and displays them on the mobile device. At this stage, the robot begins moving along the instructed path and performs the necessary tasks sequentially.

[0451] Step 5:

[0452] The terminal monitors the operating status of mobile equipment and changes in the surrounding environment, and sends feedback to the server as needed. This feedback information is used for reanalysis, resulting in more efficient business operations.

[0453] Step 6:

[0454] Users view the status and analysis results of mobile devices through their terminals. This allows them to monitor the progress of business processes and make adjustments to the system as needed.

[0455] Step 7:

[0456] When users identify specific problems or areas for improvement, they review the system settings and processes. This information is fed back into the generation module to improve the accuracy of subsequent analyses.

[0457] Through these steps, servers, terminals, and users collaborate to maximize the overall efficiency and sustainability of the system.

[0458] (Example 1)

[0459] 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."

[0460] For mobile devices to efficiently perform business processes, real-time collection of location and environmental data, and optimization based on that data, are necessary. However, conventional systems can experience delays in data collection and analysis, resulting in inefficient work execution and wasted energy. Furthermore, there is a lack of effective means to utilize user feedback to evolve the system.

[0461] 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.

[0462] In this invention, the server includes means for analyzing location data and environmental data using a generation module to optimize work procedures and travel routes, means for acquiring data in real time using a communication infrastructure, and means for transmitting optimized routes and work assignments based on the analysis results. This enables efficient operation of mobile devices, optimization of work processes, and sustainable operation with reduced energy waste.

[0463] A "generation module" refers to a program or algorithm that analyzes data received from mobile devices and peripheral equipment to optimize work procedures and travel routes.

[0464] "Mobile devices" refer to equipment used to perform tasks in logistics, warehousing, and other on-site locations, which provide location data and information on work progress.

[0465] "Peripheral devices" refer to devices and sensors used in conjunction with mobile equipment that assist in acquiring environmental and location information.

[0466] "Communication infrastructure" refers to communication technologies and network infrastructure used to transfer location data and environmental data from mobile devices and peripheral equipment to servers in real time.

[0467] "Analysis results" refer to the output of data processed by the generation module, which is used to optimize business procedures and travel routes.

[0468] A "user interface" refers to the screens and mechanisms that allow a user to interact with a system and exchange information.

[0469] "Feedback" refers to data collected from users regarding their user experience and usability, for the purpose of improving and optimizing the system.

[0470] A "generative AI model" refers to an artificial intelligence model that optimizes based on collected data, and is a technology used to evolutionarily improve the efficiency of business processes.

[0471] In this embodiment of the invention, a central server runs a generative AI model and analyzes data from mobile devices and peripheral equipment. The server uses next-generation communication technology to acquire and process location and environmental information in real time, and optimizes work procedures and travel routes. The server is equipped with a high-performance processor and large-capacity storage device, and has an AI model installed as a generative module. This enhances the accuracy and speed of data processing.

[0472] The terminal is mounted on the mobile device and receives optimized instructions sent from the server, displaying them through a user interface. This terminal assists in the operation of the mobile device and features a screen that displays specific work procedures and routes. The terminal performs the calculations necessary for the operation of each mobile device and reflects them in real time.

[0473] Users monitor and manage the entire system using a terminal. They track energy consumption and work progress, and provide feedback to the system as needed. This feedback is used as training data for the generation module, contributing to further system optimization.

[0474] As a concrete example, in a logistics center, a server collects information, analyzes the data, and calculates the optimal route. This instruction is sent to a terminal, and the route is displayed on the robot's control screen. The user makes immediate decisions based on this information and sends feedback to the server, allowing the generated AI model to further learn and improve its problem-solving capabilities.

[0475] As an example of a prompt statement, giving the AI ​​model the instruction to "concentrate collection operations between 9 AM and 11 AM" enables optimization based on a specific time period. This prompt statement allows the server to calculate the optimal timing and route for collection operations and send instructions to mobile devices via the terminal.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The server receives location and environmental data transmitted from mobile devices and peripheral equipment via next-generation communication technology. The input data is aggregated on the server and stored in a database at high speed. This process allows the server to understand the situation in real time and prepare for subsequent data analysis.

[0479] Step 2:

[0480] The server analyzes the aggregated data using a generating AI model. It executes algorithms to optimize work procedures and travel routes using the input location and environmental data. Data processing here includes noise filtering, feature extraction, and pattern recognition, resulting in optimized work procedures and routes as output.

[0481] Step 3:

[0482] The server generates instructions for mobile devices based on the optimization results produced. The server then outputs these instructions to the terminals. These instructions include the operating timing and routing information for each device.

[0483] Step 4:

[0484] The terminal displays optimized instructions received from the server. It receives instruction data as input and displays it appropriately on the robot control screen and user interface, visually managing the operation of the mobile device via the terminal screen. Specifically, it displays obstacle information and guides the robot along its path.

[0485] Step 5:

[0486] The user monitors the entire system using information from their device and makes decisions based on the situation. The input is status information from the device, which is used to check energy consumption and work progress, generate prompts as needed, and provide feedback to the server. As a result of this output, the training data of the generated AI model is updated, further optimizing the system.

[0487] (Application Example 1)

[0488] 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."

[0489] In modern logistics operations, maximizing the operational efficiency of mobile equipment is crucial, but in practice, real-time information gathering and optimization are difficult. In particular, there are no systems with an intuitive user interface that takes dynamic factors such as work prioritization and energy management into account. Therefore, there is a need for systems that can build efficient workflows and minimize energy consumption.

[0490] 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.

[0491] In this invention, the server includes means for analyzing location and environmental information received from mobile devices and peripheral equipment using a generation module to optimize work procedures and travel routes; means for acquiring travel data and environmental information in real time using a communication infrastructure; means for generating optimized routes and work priorities for mobile devices and transmitting instructions based on the analysis results; means for providing an intuitively operable user interface via a terminal and dynamically adjusting work priorities and routes; and means for proposing an optimal work flow based on information learned from past data and feedback using a generation artificial intelligence model. This enables more efficient logistics operations and sustainable energy management.

[0492] A "generation module" is a program function that analyzes location and environmental information received from mobile devices and peripheral equipment to optimize work procedures and travel routes.

[0493] "Communication infrastructure" refers to information network technology that acquires movement data and environmental information in real time and transmits the analysis results to mobile devices.

[0494] A "mobile device" is an autonomous or semi-autonomous machine used for transporting and organizing goods in logistics centers and warehouses.

[0495] "Operational procedures" refer to the process of optimizing a series of tasks related to logistics and the efficient transportation of goods.

[0496] A "generative artificial intelligence model" is a machine learning algorithm model that learns from past data and user feedback to propose the optimal workflow.

[0497] A "user interface" is a user-friendly system that provides a screen and operating system that workers can intuitively operate via a terminal, allowing them to adjust work priorities and routes.

[0498] An "optimized route" is a travel path calculated to reach a designated destination in the shortest possible time while minimizing energy consumption.

[0499] The system of the present invention operates in cooperation with a server, terminal, and user to achieve efficient operation of mobile devices and optimization of business processes.

[0500] The server uses a generation module to collect and analyze location and environmental information from mobile devices and surrounding equipment in real time. This analysis includes data processing using Node.js and a generative artificial intelligence model utilizing TensorFlow.js. Based on information learned from past data and feedback, the server is responsible for generating optimal workflows and travel routes. The server then transmits these results to the terminal in real time.

[0501] The terminal features a user interface developed using React Native, designed for intuitive operation by the user. It displays optimized routes and task priorities sent from the server, allowing users to dynamically adjust task priorities and routes as needed.

[0502] Users can monitor the entire system via their terminals, checking work progress and energy consumption. In particular, in logistics centers, it's possible to understand the operating status of mobile equipment in real time and make appropriate decisions to improve operational efficiency. Furthermore, user feedback is used as further training data for the generation modules.

[0503] For example, a robot responsible for delivering fresh produce might anticipate peak congestion in the warehouse during a specific time period. In this case, the server automatically reroutes the robot's route to ensure timely delivery while maintaining the quality of the produce. An example of a prompt to the generating artificial intelligence model is: "Based on the current congestion status of the logistics center, please suggest the optimal delivery route to transport the fresh produce without damage."

[0504] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0505] Step 1:

[0506] The server collects location and environmental information in real time from mobile devices and peripheral equipment using next-generation communication technology. It then analyzes this data to understand the current state of the mobile devices. The inputs are location and environmental information, and the output is state data based on this information.

[0507] Step 2:

[0508] The server optimizes the workflow and travel route using a generation module based on the collected data. It utilizes a generational artificial intelligence model to formulate the optimal plan based on learning from past data and feedback. The input is the state data obtained in step 1, and the output is the optimized workflow and travel route.

[0509] Step 3:

[0510] The server transmits optimized workflows and routes to the mobile devices. It sends instructions in real time via the communication infrastructure to ensure the mobile devices operate efficiently. The input is the workflow and route generated in step 2, and the output is the operation instructions for the mobile devices.

[0511] Step 4:

[0512] The terminal intuitively displays the workflow and route transmitted from the server through the user's interface. Workers review this information and modify priorities and routes as needed. Input is instruction data from the server, and output is visual information for the user.

[0513] Step 5:

[0514] The user monitors the progress of the mobile device and tasks through the terminal screen, making efficient decisions while considering energy consumption and obstacles. It can also generate prompt messages to aid in training the generation AI model. Input is visual information from the terminal, and output is the user's judgment and feedback.

[0515] 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.

[0516] This invention combines a system that optimizes business flows and travel routes using a generation module with an emotion engine that recognizes user emotions. By considering the user's emotional state, it enables more flexible and user-friendly system operation.

[0517] server

[0518] The server is the central hub of the system, receiving location and environmental information transmitted from mobile devices and peripherals via the generation module. In addition, the server integrates user emotion data analyzed by the emotion engine. The server adapts the workflow according to the user's emotional state, dynamically adjusting the mobile device's route and work schedule as needed. For example, if the server determines that the user is experiencing stress, it reduces the frequency of robot movements and decreases the number of notifications to alleviate the user's burden.

[0519] terminal

[0520] The terminal functions as an interface that provides the user with optimization instructions received from the server, both visually and audibly. It displays the user's emotional state in real time and shows work instructions that reflect feedback from the emotion engine. The terminal adjusts the interface design and the speed of information presentation to ensure the user can perform their tasks with confidence.

[0521] User

[0522] Users interact with the system via their devices during their daily work. The emotion engine recognizes emotions from the user's facial expressions and tone of voice, and provides this data to the generation module. Feedback based on the user's emotions is used to optimize work progress and maintain a bright and productive work environment. For example, if a user shows signs of anxiety or impatience, the system reduces the frequency of notifications, creating a more relaxed work environment.

[0523] In this way, by utilizing the emotion engine, servers, terminals, and users work closely together, improving the overall system performance. This enables efficient and environmentally friendly business operations, while also reducing the psychological burden on users.

[0524] The following describes the processing flow.

[0525] Step 1:

[0526] The server receives location and environmental information in real time from mobile devices and peripheral equipment using next-generation communication technology. This allows the server to understand the current work status and the location of robots.

[0527] Step 2:

[0528] The server analyzes emotional data collected from users by the emotion engine. This emotional data includes the user's emotional state obtained using facial recognition technology and voice analysis. This data is integrated into the business process optimization algorithm.

[0529] Step 3:

[0530] A generation module that takes emotional states into account calculates efficient workflows and travel routes. This can lead to route adjustments being made to reduce user stress. For example, it might slow down robot movements to avoid sudden actions or suggest increasing break times.

[0531] Step 4:

[0532] The terminal receives instructions from the server and provides information to the user through visual and auditory means. This is designed to make it easy for the user to understand appropriate work instructions. The terminal provides an interface that reduces the burden on the user and supports more intuitive operation.

[0533] Step 5:

[0534] Users monitor business processes through their terminals and provide feedback as needed. This feedback is analyzed by an emotion engine and used for further system optimization. Users also record their stress reduction through questionnaires, which helps improve the system.

[0535] Step 6:

[0536] The server utilizes collected user feedback to evolve the algorithms of its generated modules. Through this process, the system continuously learns how to balance user emotions with operational efficiency, thereby improving the quality of business operations.

[0537] Through these steps, the roles of the server, terminal, and user are integrated, maximizing system efficiency and user comfort.

[0538] (Example 2)

[0539] 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."

[0540] Traditional business management systems optimize workflows and travel routes without considering the user's emotional state, potentially placing an excessive burden on users. Furthermore, limitations in the energy efficiency and obstacle avoidance capabilities of mobile devices can reduce the efficiency of business operations. Additionally, insufficient real-time information updates can lead to a lack of rapid adaptability.

[0541] 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.

[0542] In this invention, the server includes means for analyzing location information and environmental information received from mobile devices and peripheral devices using a generation module to optimize work activities and travel routes; means for estimating the user's emotional state using an emotion analysis device and reflecting it in work activities; and means for generating optimized routes and work assignments adapted to the user's emotional state using a generation AI model and transmitting instructions. This enables flexible work operations that respond to the user's emotions, resulting in efficient and less burdensome system operation.

[0543] A "generation module" is a program or device that optimizes business activities and travel routes based on location information and environmental information acquired from mobile devices and peripheral equipment.

[0544] A "mobile device" is a device that possesses location information and is used to move through space along a specific route, and that has the function of performing normal tasks.

[0545] "Peripheral devices" are devices or tools that operate in conjunction with mobile devices and are used to provide or collect information necessary for business operations.

[0546] "Location information" refers to data indicating the current geographical location of a mobile device or peripheral equipment, which the system uses to optimize its travel path.

[0547] "Environmental information" refers to data that describes the physical or measurable surrounding conditions, and is necessary for mobile devices and peripheral equipment to function properly.

[0548] An "emotion analysis device" is a device or algorithm used to analyze a user's voice, facial expressions, and behavioral data to estimate the user's emotional state.

[0549] A "generative AI model" is a program or mathematical model that uses artificial intelligence technology to generate optimized workflows and routes based on user emotions and environment.

[0550] A "communication infrastructure" is a network infrastructure used to exchange location information, environmental information, instructions, and other data with mobile devices and peripheral equipment in real time.

[0551] "Advanced communication technology" refers to cutting-edge technologies aimed at improving the efficiency and speed of data communication, and typically includes next-generation communication protocols and hardware.

[0552] This invention is a system that optimizes business activities based on information acquired from mobile devices and peripheral equipment. The system consists of components including a generation module, an emotion analysis device, a generation AI model, and a communication infrastructure.

[0553] server

[0554] The server first utilizes a generation module to collect location and environmental information acquired from mobile devices and peripheral equipment. This includes periodically acquiring information via a data reception API. Next, the server uses an emotion analysis device to estimate the user's emotional state in real time. The analysis takes voice and facial expression data as input and performs analysis using machine learning algorithms. The server integrates these analysis results into a generation AI model to optimize the workflow and travel route according to the user's emotional state. This process makes conventional business operations more flexible and user-friendly.

[0555] terminal

[0556] The terminal provides users with optimization instructions sent from the server visually and audibly. Specifically, it displays work instructions in real time through the user interface and presents important information based on feedback from the emotion analysis device. This allows users to perform their tasks with confidence through the system. The terminal also adjusts the interface design and the speed of information presentation to provide appropriate information according to the user's emotional state.

[0557] User

[0558] Users interact with the system via terminals during their daily work. An emotion analysis device analyzes the user's facial expressions and voice tone to recognize emotions and passes this data to a generation module. Based on this feedback, work processes are optimized, maintaining a brighter and more productive work environment.

[0559] Specific example

[0560] For example, if an emotion analysis device determines that a user working in an office is experiencing stress, the server will reduce the frequency of the robot's actions and decrease the number of notifications. This allows the user to work in a more relaxed environment.

[0561] Example of a prompt

[0562] For example, it's possible to input a prompt like, "If a user shows signs of anxiety, please suggest how to adjust the notification frequency," into a generating AI model and have it derive the optimal workflow.

[0563] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0564] Step 1:

[0565] The server receives location and environmental information from mobile devices and peripheral equipment. This information is retrieved periodically using an API. The input data includes location information and environmental data such as temperature and humidity. The server preprocesses this data and performs data cleansing, such as imputing missing values. It generates a clean dataset as output.

[0566] Step 2:

[0567] The server collects user voice and facial expression data transmitted from the terminal. Voice data is converted to text via a speech recognition system, and facial expression data is analyzed using an image processing algorithm. Input is user voice and image data. Data analysis provides a numerical representation of the user's emotional state. Output is a set of emotion scores.

[0568] Step 3:

[0569] The server uses a generative AI model to integrate clean location and environmental data with sentiment scores. First, the AI ​​model receives the dynamic environment and sentiment state as input and calculates the optimal workflow and travel route. A multi-layer neural network is used for data processing, resulting in an optimized work plan being output.

[0570] Step 4:

[0571] The server generates specific instructions based on the output of the generated AI model and sends them to the terminal. The input is a work plan with optimized instructions already set. The server pushes this information to the terminal in real time and provides the user with actionable task instructions as output.

[0572] Step 5:

[0573] The terminal displays received instructions through a user interface. The user receives work instructions sent from the server as visual and auditory information. Input consists of work flow instructions from previous stages. The terminal displays these clearly to the user and collects feedback. Output is the presentation of information to the user.

[0574] Step 6:

[0575] The user performs tasks according to instructions from the terminal. As feedback, they provide newly generated voice and motion data. This data is returned to the server and incorporated again into the processing from step 1. The input is the user's feedback information to the server. The output is a continuous data improvement cycle.

[0576] (Application Example 2)

[0577] 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."

[0578] In today's work environment, there is a demand for both increased efficiency in work processes and reduced emotional burden on workers. In particular, the current situation lacks the optimal operation of mobile equipment and dynamic adjustment of work flows that take into account the emotional state of workers. Therefore, technologies are needed that can improve work efficiency while reducing worker stress and fatigue.

[0579] 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.

[0580] In this invention, the server includes means for analyzing location information and environmental information received from mobile devices and peripheral devices by a generation module to optimize work processes and travel routes; means for acquiring travel data and environmental information in real time using communication equipment; means for generating optimized routes and work assignments for mobile devices based on the analysis results and transmitting instructions; means for recognizing emotional states using an emotion engine and flexibly adjusting work processes based on the recognition results; and means for providing visual and auditory feedback through worker devices and providing relaxation guidance tailored to the worker's emotions. This makes it possible to improve work efficiency and reduce the emotional burden on workers.

[0581] A "generation module" is a device or software that analyzes data from mobile devices and peripheral devices to optimize work processes and travel routes.

[0582] A "mobile device" is a machine or device that operates based on location information and performs a specific task or transport.

[0583] A "peripheral device" is a device that operates in conjunction with a mobile device and provides additional information or functions.

[0584] "Location information" refers to data that indicates the geographical location of mobile devices and peripheral devices.

[0585] "Environmental information" refers to data that shows the conditions of the mobile device and its surroundings, and includes temperature, humidity, and other factors.

[0586] A "business process" refers to the procedures and steps for efficiently executing a specific task or process.

[0587] "Travel path" refers to information that indicates the route or course that a mobile device follows when it moves.

[0588] "Communication equipment" refers to physical or virtual infrastructure or technology for sending and receiving data.

[0589] "Real-time" refers to the ability to instantly check or manipulate the situation currently unfolding.

[0590] "Analysis results" refer to the information and conclusions obtained after analyzing data.

[0591] "Work allocation" refers to the allocation of resources and time to individual tasks within a business process.

[0592] "Instructions" are commands or guidelines given to perform a task or action.

[0593] An "emotion engine" is a technology that analyzes a user's emotions and psychological state and outputs the results.

[0594] "Emotional state" is a temporary expression that describes an individual's psychological feelings.

[0595] A "worker device" is a device used by a worker that functions as an information receiver and interface.

[0596] "Feedback" refers to information about reactions or responses given in response to actions or situations.

[0597] "Relaxation guidance" refers to advice and suggestions for reducing stress and stabilizing the mind and body.

[0598] The system of this invention is composed of three main components: a server, a terminal, and a user.

[0599] First, the server functions as the central processing unit, utilizing a generation module, communication equipment, and an emotion engine. The generation module analyzes location and environmental information acquired from mobile devices and peripheral devices to dynamically optimize work processes and travel routes. The server further acquires this information in real time and, based on the analysis results, instructs the mobile devices on optimized routes and work allocation. The emotion engine recognizes the user's emotional state, such as facial expressions and voice, and sends this data to the coordinating module. This allows for flexible adaptation of work processes according to the user's emotional state. Specifically, if a user experiences stress, the system adjusts the route and notification frequency to reduce the workload.

[0600] Next, the terminal functions as an interface with the user, providing instructions from the server visually and audibly. The terminal includes worker devices that provide emotionally responsive feedback and guidance to promote relaxation. This allows users to understand their own emotional state and make appropriate decisions to perform their tasks smoothly.

[0601] Finally, users engage in their daily tasks through this system and receive information from their terminals. When the emotion engine recognizes the user's emotions, that information is reflected in the work process via the server, allowing users to maintain a more comfortable and efficient work environment.

[0602] As a concrete example, factory workers use smart devices to record their emotional state, and a server analyzes this data to automatically adjust work processes and break times. In this way, system operation that takes the user's emotional state into consideration is realized. An example of a prompt sentence to be input into the generating AI model is, "What are some ways to reduce the fatigue you are feeling in your current work situation?"

[0603] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0604] Step 1:

[0605] The server acquires location and environmental information from mobile devices and peripheral devices in real time. It processes this data as input and performs data manipulation. Specifically, information from GPS sensors and environmental sensors is stored in a database, and preparations for analysis are made.

[0606] Step 2:

[0607] The server analyzes location and environmental information acquired using a generation module. Based on the input data, it calculates the optimal work process and travel route. This analysis utilizes historical data and predictive algorithms to generate an optimized operation plan. The output is the optimized route and work allocation.

[0608] Step 3:

[0609] The server uses an emotion engine to analyze the user's facial recognition and voice data to recognize their emotional state. This emotional data is then input, and the server uses data calculations to determine the user's emotional state. The output is a determination of whether the user is relaxed, stressed, or otherwise unsettled.

[0610] Step 4:

[0611] The server flexibly adjusts tasks based on the generated task processes and emotion assessment results. Using task process data and emotion assessment results as input, it processes the data to create an action plan that includes optimized notification frequency and route changes. The adjusted action plan is obtained as output.

[0612] Step 5:

[0613] The terminal receives an action plan sent from the server and provides it to the user visually and audibly. It takes the action plan as input and presents the information in a user-friendly format as output. In this step, it displays appropriate guidance to help the user relax when they are feeling stressed.

[0614] Step 6:

[0615] Users perform tasks based on information from their devices and provide feedback to the system. Specifically, changes in the user's facial expressions and voice are fed back to the system and used as input data for the next analysis. This cycle allows the system to continuously provide the user with an optimal environment.

[0616] 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.

[0617] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0618] 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.

[0619] [Fourth Embodiment]

[0620] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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".

[0633] This invention provides a system in which a generation module, communication infrastructure, mobile devices, and peripheral devices work closely together so that mobile devices can efficiently perform business processes.

[0634] server

[0635] The server plays a central role in this system, storing generation modules and acquiring data from mobile devices and peripheral equipment in real time. Specifically, the server uses next-generation communication technology to rapidly collect location and environmental information and analyze it comprehensively. Based on the analysis results, the server calculates the optimal workflow and movement route and transmits these instructions to the mobile devices. For example, in a logistics center where the server is located, the server comprehensively manages the route and work procedures when robots perform the collection and delivery process.

[0636] terminal

[0637] The terminals are installed in the mobile devices and receive optimization instructions sent from the server, which are then displayed on the user interface and robot control screen. The terminals analyze the information, which is updated in real time, and immediately reflect these changes in the operation of the mobile devices. For example, a terminal operated by a warehouse manager displays obstacles and alternative routes for the robot in real time, allowing the manager to make immediate decisions.

[0638] User

[0639] The user monitors and manages the entire system. The user operates terminals to check the work progress and energy consumption of each mobile device, and adjusts processes as needed. The overall efficiency of the system improves based on the user's judgment, and user feedback is used as training data for the generation module. For example, based on the results observed by the user, the work allocation during certain time periods can be readjusted to further improve operational efficiency.

[0640] In this way, the system, centered around the generation module, facilitates the collaboration of servers, terminals, and users to achieve efficient operation of mobile devices. This optimizes business processes and enables sustainable operation with reduced energy waste.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] The server receives location and environmental information in real time from mobile devices and peripheral equipment. This includes the robot's current position, the location of obstacles, and environmental data such as ambient temperature and humidity.

[0644] Step 2:

[0645] The server passes the received information to the generation module, which then integrates and analyzes the data. This analysis allows for an understanding of the overall workflow and optimizes the robot's routes and work allocation.

[0646] Step 3:

[0647] The server creates instructions for the mobile device based on the optimization results obtained from the generated module. These instructions include the route to be taken and the tasks to be performed.

[0648] Step 4:

[0649] The terminal receives instructions from the server and displays them on the mobile device. At this stage, the robot begins moving along the instructed path and performs the necessary tasks sequentially.

[0650] Step 5:

[0651] The terminal monitors the operating status of mobile equipment and changes in the surrounding environment, and sends feedback to the server as needed. This feedback information is used for reanalysis, resulting in more efficient business operations.

[0652] Step 6:

[0653] Users view the status and analysis results of mobile devices through their terminals. This allows them to monitor the progress of business processes and make adjustments to the system as needed.

[0654] Step 7:

[0655] When users identify specific problems or areas for improvement, they review the system settings and processes. This information is fed back into the generation module to improve the accuracy of subsequent analyses.

[0656] Through these steps, servers, terminals, and users collaborate to maximize the overall efficiency and sustainability of the system.

[0657] (Example 1)

[0658] 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".

[0659] For mobile devices to efficiently perform business processes, real-time collection of location and environmental data, and optimization based on that data, are necessary. However, conventional systems can experience delays in data collection and analysis, resulting in inefficient work execution and wasted energy. Furthermore, there is a lack of effective means to utilize user feedback to evolve the system.

[0660] 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.

[0661] In this invention, the server includes means for analyzing location data and environmental data using a generation module to optimize work procedures and travel routes, means for acquiring data in real time using a communication infrastructure, and means for transmitting optimized routes and work assignments based on the analysis results. This enables efficient operation of mobile devices, optimization of work processes, and sustainable operation with reduced energy waste.

[0662] A "generation module" refers to a program or algorithm that analyzes data received from mobile devices and peripheral equipment to optimize work procedures and travel routes.

[0663] "Mobile devices" refer to equipment used to perform tasks in logistics, warehousing, and other on-site locations, which provide location data and information on work progress.

[0664] "Peripheral devices" refer to devices and sensors used in conjunction with mobile equipment that assist in acquiring environmental and location information.

[0665] "Communication infrastructure" refers to communication technologies and network infrastructure used to transfer location data and environmental data from mobile devices and peripheral equipment to servers in real time.

[0666] "Analysis results" refer to the output of data processed by the generation module, which is used to optimize business procedures and travel routes.

[0667] A "user interface" refers to the screens and mechanisms that allow a user to interact with a system and exchange information.

[0668] "Feedback" refers to data collected from users regarding their user experience and usability, for the purpose of improving and optimizing the system.

[0669] A "generative AI model" refers to an artificial intelligence model that optimizes based on collected data, and is a technology used to evolutionarily improve the efficiency of business processes.

[0670] In this embodiment of the invention, a central server runs a generative AI model and analyzes data from mobile devices and peripheral equipment. The server uses next-generation communication technology to acquire and process location and environmental information in real time, and optimizes work procedures and travel routes. The server is equipped with a high-performance processor and large-capacity storage device, and has an AI model installed as a generative module. This enhances the accuracy and speed of data processing.

[0671] The terminal is mounted on the mobile device and receives optimized instructions sent from the server, displaying them through a user interface. This terminal assists in the operation of the mobile device and features a screen that displays specific work procedures and routes. The terminal performs the calculations necessary for the operation of each mobile device and reflects them in real time.

[0672] Users monitor and manage the entire system using a terminal. They track energy consumption and work progress, and provide feedback to the system as needed. This feedback is used as training data for the generation module, contributing to further system optimization.

[0673] As a concrete example, in a logistics center, a server collects information, analyzes the data, and calculates the optimal route. This instruction is sent to a terminal, and the route is displayed on the robot's control screen. The user makes immediate decisions based on this information and sends feedback to the server, allowing the generated AI model to further learn and improve its problem-solving capabilities.

[0674] As an example of a prompt statement, giving the AI ​​model the instruction to "concentrate collection operations between 9 AM and 11 AM" enables optimization based on a specific time period. This prompt statement allows the server to calculate the optimal timing and route for collection operations and send instructions to mobile devices via the terminal.

[0675] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0676] Step 1:

[0677] The server receives location and environmental data transmitted from mobile devices and peripheral equipment via next-generation communication technology. The input data is aggregated on the server and stored in a database at high speed. This process allows the server to understand the situation in real time and prepare for subsequent data analysis.

[0678] Step 2:

[0679] The server analyzes the aggregated data using a generating AI model. It executes algorithms to optimize work procedures and travel routes using the input location and environmental data. Data processing here includes noise filtering, feature extraction, and pattern recognition, resulting in optimized work procedures and routes as output.

[0680] Step 3:

[0681] The server generates instructions for mobile devices based on the optimization results produced. The server then outputs these instructions to the terminals. These instructions include the operating timing and routing information for each device.

[0682] Step 4:

[0683] The terminal displays optimized instructions received from the server. It receives instruction data as input and displays it appropriately on the robot control screen and user interface, visually managing the operation of the mobile device via the terminal screen. Specifically, it displays obstacle information and guides the robot along its path.

[0684] Step 5:

[0685] The user monitors the entire system using information from their device and makes decisions based on the situation. The input is status information from the device, which is used to check energy consumption and work progress, generate prompts as needed, and provide feedback to the server. As a result of this output, the training data of the generated AI model is updated, further optimizing the system.

[0686] (Application Example 1)

[0687] 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".

[0688] In modern logistics operations, maximizing the operational efficiency of mobile equipment is crucial, but in practice, real-time information gathering and optimization are difficult. In particular, there are no systems with an intuitive user interface that takes dynamic factors such as work prioritization and energy management into account. Therefore, there is a need for systems that can build efficient workflows and minimize energy consumption.

[0689] 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.

[0690] In this invention, the server includes means for analyzing location and environmental information received from mobile devices and peripheral equipment using a generation module to optimize work procedures and travel routes; means for acquiring travel data and environmental information in real time using a communication infrastructure; means for generating optimized routes and work priorities for mobile devices and transmitting instructions based on the analysis results; means for providing an intuitively operable user interface via a terminal and dynamically adjusting work priorities and routes; and means for proposing an optimal work flow based on information learned from past data and feedback using a generation artificial intelligence model. This enables more efficient logistics operations and sustainable energy management.

[0691] A "generation module" is a program function that analyzes location and environmental information received from mobile devices and peripheral equipment to optimize work procedures and travel routes.

[0692] "Communication infrastructure" refers to information network technology that acquires movement data and environmental information in real time and transmits the analysis results to mobile devices.

[0693] A "mobile device" is an autonomous or semi-autonomous machine used for transporting and organizing goods in logistics centers and warehouses.

[0694] "Operational procedures" refer to the process of optimizing a series of tasks related to logistics and the efficient transportation of goods.

[0695] A "generative artificial intelligence model" is a machine learning algorithm model that learns from past data and user feedback to propose the optimal workflow.

[0696] A "user interface" is a user-friendly system that provides a screen and operating system that workers can intuitively operate via a terminal, allowing them to adjust work priorities and routes.

[0697] An "optimized route" is a travel path calculated to reach a designated destination in the shortest possible time while minimizing energy consumption.

[0698] The system of the present invention operates in cooperation with a server, terminal, and user to achieve efficient operation of mobile devices and optimization of business processes.

[0699] The server uses a generation module to collect and analyze location and environmental information from mobile devices and surrounding equipment in real time. This analysis includes data processing using Node.js and a generative artificial intelligence model utilizing TensorFlow.js. Based on information learned from past data and feedback, the server is responsible for generating optimal workflows and travel routes. The server then transmits these results to the terminal in real time.

[0700] The terminal features a user interface developed using React Native, designed for intuitive operation by the user. It displays optimized routes and task priorities sent from the server, allowing users to dynamically adjust task priorities and routes as needed.

[0701] Users can monitor the entire system via their terminals, checking work progress and energy consumption. In particular, in logistics centers, it's possible to understand the operating status of mobile equipment in real time and make appropriate decisions to improve operational efficiency. Furthermore, user feedback is used as further training data for the generation modules.

[0702] For example, a robot responsible for delivering fresh produce might anticipate peak congestion in the warehouse during a specific time period. In this case, the server automatically reroutes the robot's route to ensure timely delivery while maintaining the quality of the produce. An example of a prompt to the generating artificial intelligence model is: "Based on the current congestion status of the logistics center, please suggest the optimal delivery route to transport the fresh produce without damage."

[0703] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0704] Step 1:

[0705] The server collects location and environmental information in real time from mobile devices and peripheral equipment using next-generation communication technology. It then analyzes this data to understand the current state of the mobile devices. The inputs are location and environmental information, and the output is state data based on this information.

[0706] Step 2:

[0707] The server optimizes the workflow and travel route using a generation module based on the collected data. It utilizes a generational artificial intelligence model to formulate the optimal plan based on learning from past data and feedback. The input is the state data obtained in step 1, and the output is the optimized workflow and travel route.

[0708] Step 3:

[0709] The server transmits optimized workflows and routes to the mobile devices. It sends instructions in real time via the communication infrastructure to ensure the mobile devices operate efficiently. The input is the workflow and route generated in step 2, and the output is the operation instructions for the mobile devices.

[0710] Step 4:

[0711] The terminal intuitively displays the workflow and route transmitted from the server through the user's interface. Workers review this information and modify priorities and routes as needed. Input is instruction data from the server, and output is visual information for the user.

[0712] Step 5:

[0713] The user monitors the progress of the mobile device and tasks through the terminal screen, making efficient decisions while considering energy consumption and obstacles. It can also generate prompt messages to aid in training the generation AI model. Input is visual information from the terminal, and output is the user's judgment and feedback.

[0714] 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.

[0715] This invention combines a system that optimizes business flows and travel routes using a generation module with an emotion engine that recognizes user emotions. By considering the user's emotional state, it enables more flexible and user-friendly system operation.

[0716] server

[0717] The server is the central hub of the system, receiving location and environmental information transmitted from mobile devices and peripherals via the generation module. In addition, the server integrates user emotion data analyzed by the emotion engine. The server adapts the workflow according to the user's emotional state, dynamically adjusting the mobile device's route and work schedule as needed. For example, if the server determines that the user is experiencing stress, it reduces the frequency of robot movements and decreases the number of notifications to alleviate the user's burden.

[0718] terminal

[0719] The terminal functions as an interface that provides the user with optimization instructions received from the server, both visually and audibly. It displays the user's emotional state in real time and shows work instructions that reflect feedback from the emotion engine. The terminal adjusts the interface design and the speed of information presentation to ensure the user can perform their tasks with confidence.

[0720] User

[0721] Users interact with the system via their devices during their daily work. The emotion engine recognizes emotions from the user's facial expressions and tone of voice, and provides this data to the generation module. Feedback based on the user's emotions is used to optimize work progress and maintain a bright and productive work environment. For example, if a user shows signs of anxiety or impatience, the system reduces the frequency of notifications, creating a more relaxed work environment.

[0722] In this way, by utilizing the emotion engine, servers, terminals, and users work closely together, improving the overall system performance. This enables efficient and environmentally friendly business operations, while also reducing the psychological burden on users.

[0723] The following describes the processing flow.

[0724] Step 1:

[0725] The server receives location and environmental information in real time from mobile devices and peripheral equipment using next-generation communication technology. This allows the server to understand the current work status and the location of robots.

[0726] Step 2:

[0727] The server analyzes emotional data collected from users by the emotion engine. This emotional data includes the user's emotional state obtained using facial recognition technology and voice analysis. This data is integrated into the business process optimization algorithm.

[0728] Step 3:

[0729] A generation module that takes emotional states into account calculates efficient workflows and travel routes. This can lead to route adjustments being made to reduce user stress. For example, it might slow down robot movements to avoid sudden actions or suggest increasing break times.

[0730] Step 4:

[0731] The terminal receives instructions from the server and provides information to the user through visual and auditory means. This is designed to make it easy for the user to understand appropriate work instructions. The terminal provides an interface that reduces the burden on the user and supports more intuitive operation.

[0732] Step 5:

[0733] Users monitor business processes through their terminals and provide feedback as needed. This feedback is analyzed by an emotion engine and used for further system optimization. Users also record their stress reduction through questionnaires, which helps improve the system.

[0734] Step 6:

[0735] The server utilizes collected user feedback to evolve the algorithms of its generated modules. Through this process, the system continuously learns how to balance user emotions with operational efficiency, thereby improving the quality of business operations.

[0736] Through these steps, the roles of the server, terminal, and user are integrated, maximizing system efficiency and user comfort.

[0737] (Example 2)

[0738] 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".

[0739] Traditional business management systems optimize workflows and travel routes without considering the user's emotional state, potentially placing an excessive burden on users. Furthermore, limitations in the energy efficiency and obstacle avoidance capabilities of mobile devices can reduce the efficiency of business operations. Additionally, insufficient real-time information updates can lead to a lack of rapid adaptability.

[0740] 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.

[0741] In this invention, the server includes means for analyzing location information and environmental information received from mobile devices and peripheral devices using a generation module to optimize work activities and travel routes; means for estimating the user's emotional state using an emotion analysis device and reflecting it in work activities; and means for generating optimized routes and work assignments adapted to the user's emotional state using a generation AI model and transmitting instructions. This enables flexible work operations that respond to the user's emotions, resulting in efficient and less burdensome system operation.

[0742] A "generation module" is a program or device that optimizes business activities and travel routes based on location information and environmental information acquired from mobile devices and peripheral equipment.

[0743] A "mobile device" is a device that possesses location information and is used to move through space along a specific route, and that has the function of performing normal tasks.

[0744] "Peripheral devices" are devices or tools that operate in conjunction with mobile devices and are used to provide or collect information necessary for business operations.

[0745] "Location information" refers to data indicating the current geographical location of a mobile device or peripheral equipment, which the system uses to optimize its travel path.

[0746] "Environmental information" refers to data that describes the physical or measurable surrounding conditions, and is necessary for mobile devices and peripheral equipment to function properly.

[0747] An "emotion analysis device" is a device or algorithm used to analyze a user's voice, facial expressions, and behavioral data to estimate the user's emotional state.

[0748] A "generative AI model" is a program or mathematical model that uses artificial intelligence technology to generate optimized workflows and routes based on user emotions and environment.

[0749] A "communication infrastructure" is a network infrastructure used to exchange location information, environmental information, instructions, and other data with mobile devices and peripheral equipment in real time.

[0750] "Advanced communication technology" refers to cutting-edge technologies aimed at improving the efficiency and speed of data communication, and typically includes next-generation communication protocols and hardware.

[0751] This invention is a system that optimizes business activities based on information acquired from mobile devices and peripheral equipment. The system consists of components including a generation module, an emotion analysis device, a generation AI model, and a communication infrastructure.

[0752] server

[0753] The server first utilizes a generation module to collect location and environmental information acquired from mobile devices and peripheral equipment. This includes periodically acquiring information via a data reception API. Next, the server uses an emotion analysis device to estimate the user's emotional state in real time. The analysis takes voice and facial expression data as input and performs analysis using machine learning algorithms. The server integrates these analysis results into a generation AI model to optimize the workflow and travel route according to the user's emotional state. This process makes conventional business operations more flexible and user-friendly.

[0754] terminal

[0755] The terminal provides users with optimization instructions sent from the server visually and audibly. Specifically, it displays work instructions in real time through the user interface and presents important information based on feedback from the emotion analysis device. This allows users to perform their tasks with confidence through the system. The terminal also adjusts the interface design and the speed of information presentation to provide appropriate information according to the user's emotional state.

[0756] User

[0757] Users interact with the system via terminals during their daily work. An emotion analysis device analyzes the user's facial expressions and voice tone to recognize emotions and passes this data to a generation module. Based on this feedback, work processes are optimized, maintaining a brighter and more productive work environment.

[0758] Specific example

[0759] For example, if an emotion analysis device determines that a user working in an office is experiencing stress, the server will reduce the frequency of the robot's actions and decrease the number of notifications. This allows the user to work in a more relaxed environment.

[0760] Example of a prompt

[0761] For example, it's possible to input a prompt like, "If a user shows signs of anxiety, please suggest how to adjust the notification frequency," into a generating AI model and have it derive the optimal workflow.

[0762] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0763] Step 1:

[0764] The server receives location and environmental information from mobile devices and peripheral equipment. This information is retrieved periodically using an API. The input data includes location information and environmental data such as temperature and humidity. The server preprocesses this data and performs data cleansing, such as imputing missing values. It generates a clean dataset as output.

[0765] Step 2:

[0766] The server collects user voice and facial expression data transmitted from the terminal. Voice data is converted to text via a speech recognition system, and facial expression data is analyzed using an image processing algorithm. Input is user voice and image data. Data analysis provides a numerical representation of the user's emotional state. Output is a set of emotion scores.

[0767] Step 3:

[0768] The server uses a generative AI model to integrate clean location and environmental data with sentiment scores. First, the AI ​​model receives the dynamic environment and sentiment state as input and calculates the optimal workflow and travel route. A multi-layer neural network is used for data processing, resulting in an optimized work plan being output.

[0769] Step 4:

[0770] The server generates specific instructions based on the output of the generated AI model and sends them to the terminal. The input is a work plan with optimized instructions already set. The server pushes this information to the terminal in real time and provides the user with actionable task instructions as output.

[0771] Step 5:

[0772] The terminal displays received instructions through a user interface. The user receives work instructions sent from the server as visual and auditory information. Input consists of work flow instructions from previous stages. The terminal displays these clearly to the user and collects feedback. Output is the presentation of information to the user.

[0773] Step 6:

[0774] The user performs tasks according to instructions from the terminal. As feedback, they provide newly generated voice and motion data. This data is returned to the server and incorporated again into the processing from step 1. The input is the user's feedback information to the server. The output is a continuous data improvement cycle.

[0775] (Application Example 2)

[0776] 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".

[0777] In today's work environment, there is a demand for both increased efficiency in work processes and reduced emotional burden on workers. In particular, the current situation lacks the optimal operation of mobile equipment and dynamic adjustment of work flows that take into account the emotional state of workers. Therefore, technologies are needed that can improve work efficiency while reducing worker stress and fatigue.

[0778] 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.

[0779] In this invention, the server includes means for analyzing location information and environmental information received from mobile devices and peripheral devices by a generation module to optimize work processes and travel routes; means for acquiring travel data and environmental information in real time using communication equipment; means for generating optimized routes and work assignments for mobile devices based on the analysis results and transmitting instructions; means for recognizing emotional states using an emotion engine and flexibly adjusting work processes based on the recognition results; and means for providing visual and auditory feedback through worker devices and providing relaxation guidance tailored to the worker's emotions. This makes it possible to improve work efficiency and reduce the emotional burden on workers.

[0780] A "generation module" is a device or software that analyzes data from mobile devices and peripheral devices to optimize work processes and travel routes.

[0781] A "mobile device" is a machine or device that operates based on location information and performs a specific task or transport.

[0782] A "peripheral device" is a device that operates in conjunction with a mobile device and provides additional information or functions.

[0783] "Location information" refers to data that indicates the geographical location of mobile devices and peripheral devices.

[0784] "Environmental information" refers to data that shows the conditions of the mobile device and its surroundings, and includes temperature, humidity, and other factors.

[0785] A "business process" refers to the procedures and steps for efficiently executing a specific task or process.

[0786] "Travel path" refers to information that indicates the route or course that a mobile device follows when it moves.

[0787] "Communication equipment" refers to physical or virtual infrastructure or technology for sending and receiving data.

[0788] "Real-time" refers to the ability to instantly check or manipulate the situation currently unfolding.

[0789] "Analysis results" refer to the information and conclusions obtained after analyzing data.

[0790] "Work allocation" refers to the allocation of resources and time to individual tasks within a business process.

[0791] "Instructions" are commands or guidelines given to perform a task or action.

[0792] An "emotion engine" is a technology that analyzes a user's emotions and psychological state and outputs the results.

[0793] "Emotional state" is a temporary expression that describes an individual's psychological feelings.

[0794] A "worker device" is a device used by a worker that functions as an information receiver and interface.

[0795] "Feedback" refers to information about reactions or responses given in response to actions or situations.

[0796] "Relaxation guidance" refers to advice and suggestions for reducing stress and stabilizing the mind and body.

[0797] The system of this invention is composed of three main components: a server, a terminal, and a user.

[0798] First, the server functions as the central processing unit, utilizing a generation module, communication equipment, and an emotion engine. The generation module analyzes location and environmental information acquired from mobile devices and peripheral devices to dynamically optimize work processes and travel routes. The server further acquires this information in real time and, based on the analysis results, instructs the mobile devices on optimized routes and work allocation. The emotion engine recognizes the user's emotional state, such as facial expressions and voice, and sends this data to the coordinating module. This allows for flexible adaptation of work processes according to the user's emotional state. Specifically, if a user experiences stress, the system adjusts the route and notification frequency to reduce the workload.

[0799] Next, the terminal functions as an interface with the user, providing instructions from the server visually and audibly. The terminal includes worker devices that provide emotionally responsive feedback and guidance to promote relaxation. This allows users to understand their own emotional state and make appropriate decisions to perform their tasks smoothly.

[0800] Finally, users engage in their daily tasks through this system and receive information from their terminals. When the emotion engine recognizes the user's emotions, that information is reflected in the work process via the server, allowing users to maintain a more comfortable and efficient work environment.

[0801] As a concrete example, factory workers use smart devices to record their emotional state, and a server analyzes this data to automatically adjust work processes and break times. In this way, system operation that takes the user's emotional state into consideration is realized. An example of a prompt sentence to be input into the generating AI model is, "What are some ways to reduce the fatigue you are feeling in your current work situation?"

[0802] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0803] Step 1:

[0804] The server acquires location and environmental information from mobile devices and peripheral devices in real time. It processes this data as input and performs data manipulation. Specifically, information from GPS sensors and environmental sensors is stored in a database, and preparations for analysis are made.

[0805] Step 2:

[0806] The server analyzes location and environmental information acquired using a generation module. Based on the input data, it calculates the optimal work process and travel route. This analysis utilizes historical data and predictive algorithms to generate an optimized operation plan. The output is the optimized route and work allocation.

[0807] Step 3:

[0808] The server uses an emotion engine to analyze the user's facial recognition and voice data to recognize their emotional state. This emotional data is then input, and the server uses data calculations to determine the user's emotional state. The output is a determination of whether the user is relaxed, stressed, or otherwise unsettled.

[0809] Step 4:

[0810] The server flexibly adjusts tasks based on the generated task processes and emotion assessment results. Using task process data and emotion assessment results as input, it processes the data to create an action plan that includes optimized notification frequency and route changes. The adjusted action plan is obtained as output.

[0811] Step 5:

[0812] The terminal receives an action plan sent from the server and provides it to the user visually and audibly. It takes the action plan as input and presents the information in a user-friendly format as output. In this step, it displays appropriate guidance to help the user relax when they are feeling stressed.

[0813] Step 6:

[0814] Users perform tasks based on information from their devices and provide feedback to the system. Specifically, changes in the user's facial expressions and voice are fed back to the system and used as input data for the next analysis. This cycle allows the system to continuously provide the user with an optimal environment.

[0815] 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.

[0816] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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."

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] The following is further disclosed regarding the embodiments described above.

[0837] (Claim 1)

[0838] The generation module analyzes location and environmental information received from mobile devices and peripheral equipment, and provides means for optimizing the workflow and travel route.

[0839] A means of acquiring real-time movement data and environmental information using communication infrastructure,

[0840] A means for generating optimized routes and work assignments for mobile equipment based on the analysis results and transmitting instructions,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, wherein the data to be analyzed includes the battery level of a mobile device and information on obstacles.

[0844] (Claim 3)

[0845] The system according to claim 1, wherein the communication infrastructure uses next-generation communication technology.

[0846] "Example 1"

[0847] (Claim 1)

[0848] The generation module analyzes location data and environmental data received from mobile devices and peripheral equipment, and provides means for optimizing work procedures and travel routes.

[0849] A means of acquiring movement data and environmental data in real time using a communication infrastructure,

[0850] A means for generating optimized routes and work assignments for mobile equipment based on the analysis results and transmitting instructions,

[0851] A means by which the server uses generated AI models to evolutionarily improve the analysis results,

[0852] A means of displaying the status of mobile devices in real time through a user interface and assisting in their operation,

[0853] A means of collecting user feedback and using it as training data for the generation module,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, wherein the data to be analyzed includes the remaining energy consumption and fault information of the mobile device.

[0857] (Claim 3)

[0858] The system according to claim 1, wherein the communication infrastructure uses next-generation communication technology.

[0859] "Application Example 1"

[0860] (Claim 1)

[0861] The generation module analyzes location and environmental information received from mobile devices and peripheral equipment, and provides means for optimizing work procedures and travel routes.

[0862] A means of acquiring movement data and environmental information in real time using a communication infrastructure,

[0863] A means for generating optimized routes and work priorities for mobile devices based on analysis results, and for transmitting instructions,

[0864] It provides an intuitive user interface that can be operated via a terminal, and means that allow for dynamic adjustment of work priority and route.

[0865] A method for proposing the optimal business flow based on information learned from past data and feedback using a generative artificial intelligence model,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, wherein the data to be analyzed includes the remaining energy of the mobile device and information on obstacles.

[0869] (Claim 3)

[0870] The system according to claim 1, wherein the communication infrastructure uses next-generation communication technology.

[0871] "Example 2 of combining an emotion engine"

[0872] (Claim 1)

[0873] The generation module analyzes location and environmental information received from mobile devices and peripheral equipment, and provides means for optimizing work activities and travel routes.

[0874] A means of using an emotion analysis device to estimate the user's emotional state and reflect it in business activities,

[0875] A means for generating optimized paths and task allocations adapted to the user's emotional state using a generative AI model, and sending instructions accordingly.

[0876] A means of acquiring movement data and environmental information in real time using a communication infrastructure,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, wherein the data to be analyzed includes the remaining energy of a mobile device and information on obstacles.

[0880] (Claim 3)

[0881] The system according to claim 1, wherein the communication infrastructure uses advanced communication technology.

[0882] "Application example 2 when combining with an emotional engine"

[0883] (Claim 1)

[0884] The generation module analyzes location and environmental information received from mobile devices and peripheral devices, and provides means for optimizing work processes and movement routes.

[0885] A means of acquiring movement data and environmental information in real time using communication equipment,

[0886] A means for generating an optimized route and work allocation for the mobile device based on the analysis results and transmitting instructions,

[0887] An emotion engine provides a means to recognize emotional states and flexibly adjust work processes based on the recognition results.

[0888] A means of providing visual and auditory feedback through worker devices and providing relaxation guidance tailored to the worker's emotions,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, wherein the data to be analyzed includes the remaining energy of the mobile device and information on obstacles.

[0892] (Claim 3)

[0893] The system according to claim 1, wherein the communication equipment uses advanced communication technology. [Explanation of Symbols]

[0894] 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. The generation module analyzes location and environmental information received from mobile devices and peripheral equipment, and provides means for optimizing the workflow and travel route. A means of acquiring real-time movement data and environmental information using communication infrastructure, A means for generating optimized routes and work assignments for mobile equipment based on the analysis results and transmitting instructions, A system that includes this.

2. The system according to claim 1, wherein the data to be analyzed includes the battery level of a mobile device and information on obstacles.

3. The system according to claim 1, wherein the communication infrastructure uses next-generation communication technology.

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