system

Abstract

We provide the system. [Solution] A means for receiving worker work preference information and absence preference information entered via a communication medium through a communication device, based on a command from an information processing device, A storage means for storing the received work preference information and absence preference information, A means for generating optimized work assignment information using a machine learning model based on the information stored in the aforementioned storage means, A means for transmitting and notifying the generated work assignment information via the communication medium, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 modern diverse workplace environments, shift management considering individual work preferences and absence schedules of employees is very time-consuming and laborious to perform manually, and thus inefficient. Therefore, for store and enterprise managers, it is required to quickly and accurately create a shift schedule that appropriately reflects various conditions and demands of employees, but the conventional methods lack flexibility and speed. Therefore, it is an issue to provide a means for effectively collecting work preferences and absence information and realizing an optimal work arrangement without burdening workers.

Means for Solving the Problems

[0005] According to the present invention, this problem is solved by providing an information processing device with means for receiving worker work preference information and absence preference information via a communication medium through a communication device. Furthermore, by providing means for storing the received information, the conditions of individual employees are centrally managed, and by providing means for generating optimized work assignment information using a machine learning model, a shift schedule that takes into account the preferences and constraints of each employee is automatically created. Through this automated process, the generated work assignment information is quickly notified to relevant parties via the communication medium, making it possible to reduce the workload of conventional manual work.

[0006] An "information processing device" is a system of hardware or software designed to collect, store, analyze, and generate data.

[0007] A "communication device" is a hardware or software system for sending and receiving information with other devices or systems.

[0008] "Communication medium" refers to a route or means for sending and receiving information, including email and messaging services.

[0009] A "worker" refers to an employee or staff member within an organization who uses the system and is an individual who submits work requests or notifications of absence.

[0010] "Work preference information" refers to information about the working hours and days that workers prefer.

[0011] "Absence Request Information" refers to information about the times and dates when a worker does not wish to work.

[0012] A "machine learning model" is an algorithm or mathematical model used to learn from data and perform predictions or classifications.

[0013] "Work assignment information" refers to information about work shifts that have been optimized considering the preferences and requirements of the workers. [Brief explanation of the drawing]

[0014] [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 Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

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

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

[0017] In the following embodiments, a 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.

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is a shift management system using an information processing device that collects and stores information on employees' work preferences and absence preferences, and automatically generates and notifies them of optimized work assignments based on that information, thereby improving the efficiency of shift management operations.

[0036] In this system, the "terminal" first receives work preference and absence preference information entered by the user (employee) via the "communication device." Since the user enters this information through their usual messaging application, no special interface is required. For example, a user might send a message via the app stating, "I cannot work next Monday and Friday." The terminal then prepares to send this message to the server.

[0037] The "server" receives work preference and absence preference information sent from terminals using an "information processing device." The received information is stored in a database, and each user's information is organized. Subsequently, the server uses a machine learning model based on the stored data to generate an optimal work shift that takes into account work preferences and constraints. In this process, an optimized shift plan for the entire organization is formulated, taking into account employees' skill sets and workload balance.

[0038] The generated work assignment information is then sent back to the terminal via a communication device by the server. At this time, the system generates a message containing detailed information such as the start time of work and the planned assignment location.

[0039] Users can check the shift information they receive on their device. For example, if they receive a notification that they have been assigned an early shift on Tuesday, they can immediately check the details of the shift and compare it with their own schedule. If any changes to the shift are needed, they can communicate their requests again via the communication device.

[0040] The system of this invention streamlines the conventional manual shift management process, enabling rapid and accurate shift management. Furthermore, because it uses a common messaging tool as the communication medium, it achieves both employee convenience and efficient system operation.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users enter their work schedule preferences and absence requests via their terminal. They use a messaging application to send a text message, for example, "I would like to take next Thursday off." The terminal prepares this message for transmission and sends it to the server via a communication device.

[0044] Step 2:

[0045] The server receives messages sent from terminals via a communication device. It analyzes the received messages and stores work preference and absence preference information in a database. At this stage, each user's conditions and preferences are organized in preparation for the next processing step.

[0046] Step 3:

[0047] The server runs a machine learning model based on information from all employees stored in the database. The model generates the optimal work shift, taking into account each user's preferences and constraints. This process includes calculations that reflect individual workloads and preferred hours while maintaining an overall balance of work.

[0048] Step 4:

[0049] The server organizes the generated work assignment information and creates a message to send to the terminal via the messaging application. This message includes information such as the specific start time and location of the work assignment.

[0050] Step 5:

[0051] The terminal receives work shift information sent from the server via a messaging application. It then notifies the user, making the information available for review.

[0052] Step 6:

[0053] The user reviews the shift information sent on their device and determines if it matches their schedule. If corrections are needed, they can re-enter their preferences and resubmit. The device then processes the re-entered information and sends it back to the server.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] Conventional shift management systems posed a challenge because collecting information on employee work preferences and absence requests was cumbersome, and data allocation and distribution were inefficient, making it difficult to create quick and accurate shift plans. Furthermore, their poor usability resulted in decreased operational efficiency.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes a device that receives desired information transmitted by workers via a communication device and communication medium, a device that stores the received desired information in a structured database, and a device that analyzes the stored information and constructs an optimized work schedule using a generation AI model. This enables efficient and streamlined information processing and the automated generation of an optimal shift schedule.

[0059] "Communication equipment" is a general term for hardware or software used to send and receive data via a communication medium.

[0060] A "communication medium" is a technical element that provides a means for physically or wirelessly transferring data from a sender to a receiver.

[0061] The term "worker" refers to an individual or team engaged in a specific task or job.

[0062] "Desired information" refers to the collective data that includes work-related preferences and constraints entered by workers.

[0063] A "database" is an information system that stores and manages data in a structured format, enabling rapid retrieval and editing.

[0064] A "generative AI model" is a general term for models that use machine learning algorithms to analyze data and generate optimal output results.

[0065] An "optimized work schedule" refers to a work plan that is efficient and balanced for the entire operation, while taking into account the preferences and constraints of each worker.

[0066] A "notification device" is hardware or software that provides a means for informing a recipient of generated information.

[0067] The system of this invention utilizes information and communication technology to streamline shift management for work. A specific embodiment is shown below.

[0068] The server has the functionality to receive work preference and absence preference information sent by users via communication devices. Since users can easily input information using common messaging applications, no special interface is required. For example, a user might send a message stating, "I cannot work next Monday and Friday." This information is collected by the terminal and sent to the server.

[0069] The server stores the received information in a structured format in a database. This organizes each user's preferences and makes them efficiently available for subsequent processing. This data is analyzed using a generative AI model. The AI ​​model generates an optimized work schedule, taking into account the user's preferences, skill set, and workload balance. This program processing applies advanced machine learning techniques, particularly deep learning models. A concrete example of a prompt statement is, "To create the next week's shift schedule, please propose the optimal shift arrangement considering the preferences of the following employees."

[0070] The generated work schedule is then transmitted back to the user's terminal via the communication device. The user can check the shift information on their terminal and be immediately informed of details such as, "You have been assigned an early shift on Tuesday." If the user wishes to change their shift, they can efficiently adjust it by sending the information again from their terminal.

[0071] This system can significantly improve the efficiency of shift management through automated data collection and analysis. By utilizing common communication media, it is user-friendly and contributes to reducing system operating costs.

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

[0073] Step 1:

[0074] Users enter their preferred work schedules and availability information through their usual messaging app. The entered information is provided in natural language without any specific formatting requirements. This input is in the form of text messages sent by the user.

[0075] Step 2:

[0076] The terminal receives a message from the user and prepares it to be sent to the server via a communication device. Here, data preprocessing takes place, including format-specific conversions and the addition of metadata as needed. The output of this process is data in a format that the server can interpret.

[0077] Step 3:

[0078] The server receives information transmitted from terminals via communication devices. The received data is stored in a database as structured tables. This database storage is crucial for improving the reliability and access efficiency of the information. Access to the data is easily searchable and retrievalable using the data management system.

[0079] Step 4:

[0080] The server uses a generative AI model to analyze the stored data. This model automatically generates the optimal shifts, taking into account working conditions and constraints. The model reflects employee skills and organizational needs, and the calculated output is an optimized work schedule. An example of a prompt is, "To create the next week's shifts, please suggest the optimal shift arrangement considering the following employee preferences."

[0081] Step 5:

[0082] The server returns the generated work schedule to the terminal. In this step, data including specific work details (e.g., start time, assigned location) is transferred using a communication device. The output information is formatted so that it can be viewed by the user.

[0083] Step 6:

[0084] Users check their shift information through their terminal. The information they receive concerns specific work assignments and hours. If a shift change is needed, the user can re-enter their desired information based on the information they received and restart the cycle described above.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] Traditionally, managing worker schedules in factories required manual adjustments, which was time-consuming and labor-intensive. Furthermore, automatically generating optimal shifts that considered workers' technical aptitudes and machine maintenance schedules was difficult. As a result, operational efficiency decreased, and the burden on workers increased.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes means for receiving worker's preferred work schedule and preferred absence information via a communication medium, means for storing the received information and generating optimized work assignment information that takes into account the technology set and work coordination using a generation model, and means for transmitting and notifying the generated work assignment information. This enables the generation of efficient and less burdensome work assignments.

[0090] An "information processing device" is a device that can receive and store input data, as well as perform various calculations and data processing.

[0091] A "communication device" is a device that provides an interface for sending and receiving information and enables the efficient exchange of data.

[0092] "Communication media" is a general term for means of electronically transmitting information, and includes the internet and mobile networks.

[0093] "Work preference information" refers to data that indicates the working conditions and shift times that workers desire.

[0094] "Absence Request Information" refers to data indicating the days and times when an employee is unable to work.

[0095] "Storage means" refers to devices or functions for continuously retaining received data.

[0096] A "generative model" is a mathematical model that uses machine learning techniques to calculate the optimal work assignment from input data.

[0097] "Optimized work assignment information" refers to shift information that has been adjusted to achieve efficient staffing by taking into account workers' work preferences, skill sets, and work coordination.

[0098] An "operating medium" refers to a device or interface used by an operator for inputting or confirming data, and is a means for the user to interact with the system.

[0099] The system for implementing this invention achieves efficient work assignment management by having a server and terminals communicate with each other for information processing. The server stores the received work preference and absence preference information of workers and uses a generating AI model to calculate and generate the optimal work assignment based on this information. Specifically, it uses the Python programming language and machine learning frameworks such as TENSORFLOW® to process data and optimize work shifts from the information stored in the database.

[0100] The server generates optimized work assignment information and sends it to the terminal, which then uses a communication device to notify the user of that information. The terminal provides an interface through an application developed using React Native, allowing workers to easily input their work preferences and absence requests. This interface is intuitive and easy to use on smartphones and tablets, enhancing worker convenience.

[0101] Furthermore, users can review this information, compare it to their own schedule, and request corrections as needed. This feedback is received by the server again, and recalculations are performed as necessary.

[0102] As a concrete example, if a factory worker enters into an application that they are available to work on Wednesday and Friday of next week, the generated AI model will compare this data with other workers' information and machine maintenance schedules to calculate the optimal work assignment. As a result, the worker will receive a notification on their device stating, "You have been assigned to the early shift next Wednesday."

[0103] An example of a prompt message would be, "Please enter your work preferences for next week. The AI ​​will suggest the best shift for you." This allows workers to easily communicate their work preferences to the system.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] Users enter their preferred work schedules and availability information via their device. Specifically, they use the interface of a smartphone application to enter information such as "I am available to work next Tuesday and Friday." The entered data is then transmitted directly to the server via the device's communication equipment.

[0107] Step 2:

[0108] The server receives work request and absence request information sent from the terminal. Before the received data is stored in the database, it undergoes preprocessing to prepare the data format. Specifically, it performs a process to convert text data into structured data.

[0109] Step 3:

[0110] The server runs a generative AI model based on worker work information stored in the database. The generative AI model is built using TensorFlow and takes in data such as work preferences, absence preferences, skill sets, and workload to calculate optimized work shifts. In this process, mathematical optimization is performed to maximize overall work efficiency while satisfying constraints.

[0111] Step 4:

[0112] The server then sends the generated optimized work assignment information back to the terminal. In practice, it converts the results obtained from machine learning processing into JSON format and sends it to the terminal via a communication device. It generates a prompt message and prepares the notification content for the user.

[0113] Step 5:

[0114] The terminal receives optimized work assignment information sent from the server. It generates a notification for the user, specifically displaying a message such as, "You are assigned to the morning shift this Wednesday." After receiving the notification, the user reviews the information and compares it with their own schedule.

[0115] Step 6:

[0116] If a user wishes to modify their shift schedule, they send the information back to the server via their terminal. Using the same interface, the user can, for example, enter "I want to change my Thursday shift," and that information is sent back to the server, repeating the optimization process.

[0117] 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.

[0118] This invention relates to a shift management system that uses an information processing device to collect employees' work and absence preferences, and combines this with an emotion engine that recognizes users' emotions. This system enables flexible shift scheduling that takes into account the individual needs and emotional states of employees, thereby improving the workplace environment.

[0119] In the system, a "terminal" receives user-entered work requests and absence requests as messages and transmits them to the server via a communication device. Users use a typical messaging application to enter text such as, for example, "I'd like to take this weekend off."

[0120] The "server" receives messages from terminals via communication devices and analyzes the emotions expressed by the user using an emotion engine. This analysis uses text analysis technology to identify the user's emotions from the context and linguistic tone of the message. In particular, if stress or anxiety is detected, corresponding information is recorded in the database.

[0121] The server then runs a machine learning model utilizing user preference information along with emotional data. This model generates optimal work shifts, taking into account the employees' emotional states. For example, if positive emotions are detected, the user's preferences can be given higher priority.

[0122] The generated work assignment information is then transmitted back to the terminal via the communication device. At this point, users who respond to positive emotions can be notified of their preferred shift assignments.

[0123] Users can check the notified shift information on their device and, if necessary, re-enter and submit their preferences. This is expected to improve the flexibility of shift management and boost workplace motivation.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The user uses their terminal to enter their work schedule and absence requests. They then use a messaging application to send a text message such as, "I would like to take next Friday off." The terminal then prepares to send this message to the server.

[0127] Step 2:

[0128] The server receives messages sent from terminals via communication devices. To analyze the message content, it uses an emotion engine to determine the user's emotional state from the text. For example, contextual analysis identifies whether the user is relaxed or stressed.

[0129] Step 3:

[0130] The server stores work preference information, including analyzed emotional data, in a database. Each user's data is organized and recorded in detail, along with their emotional state. This data is used in the subsequent shift generation process.

[0131] Step 4:

[0132] The server utilizes stored data to run a machine learning model. This model takes into account the user's emotional state and other work preferences to generate the optimal shift schedule for all employees. For example, it incorporates processing to prioritize the preferences of users who exhibit positive emotions.

[0133] Step 5:

[0134] The server organizes the generated work assignment information and forms a message to be sent to the terminal using a messaging application. This message contains detailed information about work hours and location.

[0135] Step 6:

[0136] The terminal receives shift information sent from the server. It then prepares to notify the user and prompt them to confirm the information.

[0137] Step 7:

[0138] The user checks the shift information received on their terminal and determines if it matches their schedule. If adjustments are needed, they can re-enter their requests and submit the information. The terminal then sends this information back to the server, updating the system.

[0139] (Example 2)

[0140] 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".

[0141] Traditional shift management methods struggle to adequately address individual employee needs, such as preferred working hours and absences. Furthermore, they fail to consider employees' emotional states, making it difficult to optimize the work environment. As a result, there is a challenge in achieving flexible and efficient shift scheduling to improve employee motivation and performance.

[0142] 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.

[0143] In this invention, the server includes means for identifying worker emotional data using emotion analysis technology, means for generating optimized work assignment data using a machine learning model by combining the identified emotional data and preference data, and means for transmitting and notifying the generated work assignment data via a communication interface. This enables flexible and efficient shift scheduling that takes into account the emotional state of employees and responds to individual needs.

[0144] An "information processing unit" is a computing device or combination thereof used for collecting, analyzing, storing, and transmitting data.

[0145] A "communication unit" is a device or module that has the function of receiving and transmitting information via a communication interface.

[0146] "Communication interface" refers to means or devices for inputting or outputting information, including screens and terminals on which users can input information.

[0147] "Work schedule preference data" refers to information submitted by workers indicating their preferred work schedules.

[0148] "Vacation request data" refers to information indicating the desired vacation period for workers.

[0149] "Memory means" refers to a device or function for storing received data or generated information.

[0150] "Emotion analysis technology" is a technology that analyzes and identifies a person's emotional state from text, audio, and other data.

[0151] "Emotional data" refers to information representing the emotional state of a worker, identified based on emotional analysis.

[0152] A "machine learning model" is an algorithm or its implementation used to predict or generate the optimal outcome based on data.

[0153] "Work assignment data" refers to schedule information generated to optimize workers' working hours and shifts.

[0154] This invention is a system that generates optimal shift assignments by using an information processing system to collect employees' work preferences and absence preferences, and by using sentiment analysis technology to identify emotional data.

[0155] The terminal receives user-entered work and absence requests as messages. Users can do this using a standard messaging application, for example, by entering text such as "I would like to take next Monday off." This text is sent to the server via the communication unit. Communication is conducted using a secure communication protocol such as HTTPS.

[0156] The server analyzes messages received from the terminal and stores them in its storage device. Next, the server utilizes NLP libraries to analyze the user's emotional state from the received text using sentiment analysis techniques. Specifically, it uses spaCy or NLTK to analyze the text and identify an emotional score. This analysis result is recorded in a database.

[0157] Subsequently, the server runs a machine learning model based on sentiment data, work preferences, and absence preferences to generate the optimal work schedule. This model is built using TensorFlow or PyTorch, and is optimized after being trained on prior data. If the sentiment is positive, it is possible to adjust the model to prioritize the user's preferences.

[0158] The generated work assignment information is then transmitted back to the terminal via the communication unit. The terminal displays a notification to the user, who can then confirm it and reflect it in their shift management. If necessary, the user can make adjustments by re-entering and submitting their preferences, thereby ensuring flexibility in shift management.

[0159] For example, if a cafe staff member enters a preference such as "I want to spend the weekend with my family," the system can take this information into account and suggest the optimal shift based on their emotional state. An example of a prompt to input into the generative AI model would be, "Please create the optimal shift schedule based on the user's work preferences and emotional data."

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

[0161] Step 1:

[0162] Users enter their work and absence requests using a messaging application on their device. Specifically, they might enter text such as, "I'd like to take next Monday off." Once this message is entered, the device encrypts it via a communication unit and sends it to the server using a secure protocol (e.g., HTTPS). The input data is the text of the work or absence request, and the output is an encrypted data packet.

[0163] Step 2:

[0164] The server decodes the encrypted data received from the terminal and treats it as received data. The server analyzes the message content, records it in the database, and associates it with the ID of the person in charge. In this process, the input data is encrypted text data, and the output is plaintext text data. The plaintext text data is stored in the database as a log.

[0165] Step 3:

[0166] The server performs text analysis using sentiment analysis technology. Specifically, it extracts sentiment data from input messages using NLP libraries such as spaCy and NLTK. At this stage, the input is plain text data, and the output is sentiment data (such as sentiment scores and labels). This sentiment data is recorded in a database along with user information.

[0167] Step 4:

[0168] The server inputs information combining sentiment data and user work preferences and absence preferences into a machine learning model. At this stage, a model using TensorFlow or PyTorch analyzes the sentiment data and work preferences to generate the optimal work schedule. The input for this process is sentiment data and preference data, and the output is optimized work schedule data.

[0169] Step 5:

[0170] The server formats the generated work assignment data and sends it to the terminal via the communication unit. The data is formatted for display before transmission. The input data is work assignment data, and the output is formatted shift information.

[0171] Step 6:

[0172] The terminal displays the shift information received from the server on the screen. The user reviews this information and modifies or re-enters it as needed. The input data in this step is formatted shift information, and the output is new desired data (if necessary) based on the user's review.

[0173] (Application Example 2)

[0174] 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".

[0175] Traditional work management systems failed to adequately consider the personal emotional state of workers when assigning shifts, leading to problems such as decreased worker motivation and accumulated stress. Furthermore, it was difficult to implement the necessary measures to improve the overall work efficiency of the team, limiting productivity improvements. This highlighted the need for improvements to the workplace environment.

[0176] 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.

[0177] In this invention, the server includes means for receiving workers' work preference data and absence preference data, means for storing the received data, means for generating optimized work assignment data using a machine learning algorithm, and means for acquiring emotional state data using an emotion analysis engine and reflecting it in the work assignment. This enables flexible shift assignments that take into account the emotional state of workers, and is expected to improve the workplace environment and increase work efficiency.

[0178] An "information processing system" is a computing device used to interpret and analyze data related to workers' work performance and emotions.

[0179] A "communication interface" is a means of connection for sending and receiving information between different devices.

[0180] A "network medium" is an electronic medium used to transmit digital information.

[0181] "Worker's work preference data" refers to information about the working conditions desired by individual workers.

[0182] "Absence request data" refers to information about the days and times when an employee does not wish to work.

[0183] "Storage means" refers to devices or software that store received data and allow it to be retrieved as needed.

[0184] A "machine learning algorithm" is an analytical method that learns patterns and rules from large amounts of data to derive optimal results.

[0185] "Work assignment data" refers to information regarding the shift schedules assigned to each worker.

[0186] An "emotion analysis engine" is a system that analyzes and identifies a person's emotional state from text and other data.

[0187] This invention functions as a shift management system for workers in the workplace. This system consists of an information processing system, a communication interface, an emotion analysis engine, and a machine learning algorithm. A specific example of this system is described below.

[0188] First, the user (worker) inputs work preference data and absence preference data via a network medium using a terminal. The information is then transmitted to a communication interface via a smartphone or dedicated terminal. An information processing system connected to a server receives this data and stores it in a storage device.

[0189] Next, a machine learning algorithm is activated to generate optimized work assignment data based on stored work preference and absence preference data. A sentiment analysis engine is also used, employing text analysis techniques to understand the emotional state of workers from their messages. Natural language processing libraries such as NLTK and spaCy are used to identify emotional states such as stress and anxiety, and this information is reflected in the work assignments.

[0190] As a result, this system enables flexible and optimal shift scheduling that takes into account the emotional state of workers. For example, if a worker sends a message saying, "I'm busy this week and would like to take time off," their emotions are analyzed and a shift schedule is created that matches their wishes. Through this process, it is possible to reduce worker stress and improve the work environment.

[0191] The generation AI model uses a pre-trained model. An example of a prompt message is, "Calculate the optimal work arrangement to improve the workplace environment based on workers' shift preferences and sentiment data." Based on this, the AI ​​infers the optimal shift arrangement and notifies each worker and the necessary terminals of the result.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] Users input their desired work schedules and absence requests using smartphones or dedicated terminals. This data is entered in text format and transmitted via a network medium through a communication interface. The input in this step consists of the user's various requests, and the output is the data received by the server.

[0195] Step 2:

[0196] The server receives work request data and absence request data transmitted through the communication interface and stores them in a storage device. This stored data serves as the basis for analysis in subsequent processing steps. The input for this step is the data transmitted by the user, and the output is the stored data.

[0197] Step 3:

[0198] The server uses stored work request and absence request data to analyze the user's emotional state using a sentiment analysis engine, leveraging natural language processing libraries (e.g., NLTK, spaCy). The analysis identifies the degree of stress and dissatisfaction and generates corresponding sentiment data. The input for this step is the stored data, and the output is the analyzed sentiment data.

[0199] Step 4:

[0200] The server generates optimized work assignment data using machine learning algorithms based on data obtained through sentiment analysis. The generating AI model calculates shift assignments that take into account workers' emotional states and work preferences. This step ensures that each worker receives the most efficient and preferred shifts. The inputs to this step are sentiment data and preference data, and the output is optimized shift data.

[0201] Step 5:

[0202] Finally, the server transmits and notifies each user's terminal via the network medium of the generated optimized work assignment data. Users can check the notified shift information on their terminal and request readjustments if necessary. The input for this step is the optimized shift data, and the output is the notified shift data.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] This invention is a shift management system using an information processing device that collects and stores information on employees' work preferences and absence preferences, and automatically generates and notifies them of optimized work assignments based on that information, thereby improving the efficiency of shift management operations.

[0220] In this system, the "terminal" first receives work preference and absence preference information entered by the user (employee) via the "communication device." Since the user enters this information through their usual messaging application, no special interface is required. For example, a user might send a message via the app stating, "I cannot work next Monday and Friday." The terminal then prepares to send this message to the server.

[0221] The "server" receives work preference and absence preference information sent from terminals using an "information processing device." The received information is stored in a database, and each user's information is organized. Subsequently, the server uses a machine learning model based on the stored data to generate an optimal work shift that takes into account work preferences and constraints. In this process, an optimized shift plan for the entire organization is formulated, taking into account employees' skill sets and workload balance.

[0222] The generated work assignment information is then sent back to the terminal via a communication device by the server. At this time, the system generates a message containing detailed information such as the start time of work and the planned assignment location.

[0223] Users can check the shift information they receive on their device. For example, if they receive a notification that they have been assigned an early shift on Tuesday, they can immediately check the details of the shift and compare it with their own schedule. If any changes to the shift are needed, they can communicate their requests again via the communication device.

[0224] The system of this invention streamlines the conventional manual shift management process, enabling rapid and accurate shift management. Furthermore, because it uses a common messaging tool as the communication medium, it achieves both employee convenience and efficient system operation.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] Users enter their work schedule preferences and absence requests via their terminal. They use a messaging application to send a text message, for example, "I would like to take next Thursday off." The terminal prepares this message for transmission and sends it to the server via a communication device.

[0228] Step 2:

[0229] The server receives messages sent from terminals via a communication device. It analyzes the received messages and stores work preference and absence preference information in a database. At this stage, each user's conditions and preferences are organized in preparation for the next processing step.

[0230] Step 3:

[0231] The server runs a machine learning model based on information from all employees stored in the database. The model generates the optimal work shift, taking into account each user's preferences and constraints. This process includes calculations that reflect individual workloads and preferred hours while maintaining an overall balance of work.

[0232] Step 4:

[0233] The server organizes the generated work assignment information and creates a message to send to the terminal via the messaging application. This message includes information such as the specific start time and location of the work assignment.

[0234] Step 5:

[0235] The terminal receives work shift information sent from the server via a messaging application. It then notifies the user, making the information available for review.

[0236] Step 6:

[0237] The user reviews the shift information sent on their device and determines if it matches their schedule. If corrections are needed, they can re-enter their preferences and resubmit. The device then processes the re-entered information and sends it back to the server.

[0238] (Example 1)

[0239] 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."

[0240] Conventional shift management systems posed a challenge because collecting information on employee work preferences and absence requests was cumbersome, and data allocation and distribution were inefficient, making it difficult to create quick and accurate shift plans. Furthermore, their poor usability resulted in decreased operational efficiency.

[0241] 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.

[0242] In this invention, the server includes a device that receives desired information transmitted by workers via a communication device and communication medium, a device that stores the received desired information in a structured database, and a device that analyzes the stored information and constructs an optimized work schedule using a generation AI model. This enables efficient and streamlined information processing and the automated generation of an optimal shift schedule.

[0243] "Communication equipment" is a general term for hardware or software used to send and receive data via a communication medium.

[0244] A "communication medium" is a technical element that provides a means for physically or wirelessly transferring data from a sender to a receiver.

[0245] The term "worker" refers to an individual or team engaged in a specific task or job.

[0246] "Desired information" refers to the collective data that includes work-related preferences and constraints entered by workers.

[0247] A "database" is an information system that stores and manages data in a structured format, enabling rapid retrieval and editing.

[0248] A "generative AI model" is a general term for models that use machine learning algorithms to analyze data and generate optimal output results.

[0249] An "optimized work schedule" refers to a work plan that is efficient and balanced for the entire operation, while taking into account the preferences and constraints of each worker.

[0250] A "notification device" is hardware or software that provides a means for informing a recipient of generated information.

[0251] The system of this invention utilizes information and communication technology to streamline shift management for work. A specific embodiment is shown below.

[0252] The server has the functionality to receive work preference and absence preference information sent by users via communication devices. Since users can easily input information using common messaging applications, no special interface is required. For example, a user might send a message stating, "I cannot work next Monday and Friday." This information is collected by the terminal and sent to the server.

[0253] The server stores the received information in a structured format in a database. This organizes each user's preferences and makes them efficiently available for subsequent processing. This data is analyzed using a generative AI model. The AI ​​model generates an optimized work schedule, taking into account the user's preferences, skill set, and workload balance. This program processing applies advanced machine learning techniques, particularly deep learning models. A concrete example of a prompt statement is, "To create the next week's shift schedule, please propose the optimal shift arrangement considering the preferences of the following employees."

[0254] The generated work schedule is then transmitted back to the user's terminal via the communication device. The user can check the shift information on their terminal and be immediately informed of details such as, "You have been assigned an early shift on Tuesday." If the user wishes to change their shift, they can efficiently adjust it by sending the information again from their terminal.

[0255] This system can significantly improve the efficiency of shift management through automated data collection and analysis. By utilizing common communication media, it is user-friendly and contributes to reducing system operating costs.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] Users enter their preferred work schedules and availability information through their usual messaging app. The entered information is provided in natural language without any specific formatting requirements. This input is in the form of text messages sent by the user.

[0259] Step 2:

[0260] The terminal receives a message from the user and prepares it to be sent to the server via a communication device. Here, data preprocessing takes place, including format-specific conversions and the addition of metadata as needed. The output of this process is data in a format that the server can interpret.

[0261] Step 3:

[0262] The server receives information transmitted from terminals via communication devices. The received data is stored in a database as structured tables. This database storage is crucial for improving the reliability and access efficiency of the information. Access to the data is easily searchable and retrievalable using the data management system.

[0263] Step 4:

[0264] The server uses a generative AI model to analyze the stored data. This model automatically generates the optimal shifts, taking into account working conditions and constraints. The model reflects employee skills and organizational needs, and the calculated output is an optimized work schedule. An example of a prompt is, "To create the next week's shifts, please suggest the optimal shift arrangement considering the following employee preferences."

[0265] Step 5:

[0266] The server returns the generated work schedule to the terminal. In this step, data including specific work details (e.g., start time, assigned location) is transferred using a communication device. The output information is formatted so that it can be viewed by the user.

[0267] Step 6:

[0268] Users check their shift information through their terminal. The information they receive concerns specific work assignments and hours. If a shift change is needed, the user can re-enter their desired information based on the information they received and restart the cycle described above.

[0269] (Application Example 1)

[0270] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] Traditionally, managing worker schedules in factories required manual adjustments, which was time-consuming and labor-intensive. Furthermore, automatically generating optimal shifts that considered workers' technical aptitudes and machine maintenance schedules was difficult. As a result, operational efficiency decreased, and the burden on workers increased.

[0272] 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.

[0273] In this invention, the server includes means for receiving worker's preferred work schedule and preferred absence information via a communication medium, means for storing the received information and generating optimized work assignment information that takes into account the technology set and work coordination using a generation model, and means for transmitting and notifying the generated work assignment information. This enables the generation of efficient and less burdensome work assignments.

[0274] An "information processing device" is a device that can receive and store input data, as well as perform various calculations and data processing.

[0275] A "communication device" is a device that provides an interface for sending and receiving information and enables the efficient exchange of data.

[0276] "Communication media" is a general term for means of electronically transmitting information, and includes the internet and mobile networks.

[0277] "Work preference information" refers to data that indicates the working conditions and shift times that workers desire.

[0278] "Absence Request Information" refers to data indicating the days and times when an employee is unable to work.

[0279] "Storage means" refers to devices or functions for continuously retaining received data.

[0280] A "generative model" is a mathematical model that uses machine learning techniques to calculate the optimal work assignment from input data.

[0281] "Optimized work assignment information" refers to shift information that has been adjusted to achieve efficient staffing by taking into account workers' work preferences, skill sets, and work coordination.

[0282] An "operating medium" refers to a device or interface used by an operator for inputting or confirming data, and is a means for the user to interact with the system.

[0283] The system for implementing this invention achieves efficient work assignment management by having a server and terminals communicate with each other for information processing. The server stores the received work preference and absence preference information of workers and uses a generation AI model to calculate and generate the optimal work assignment based on this information. Specifically, it uses the Python programming language and machine learning frameworks such as TensorFlow to process data and optimize work shifts from the information stored in the database.

[0284] When the server generates optimized work schedule information, it sends it to the terminal, and the terminal uses the communication device to notify the user of the information. The terminal provides an operation interface for workers to easily input work preferences and non - working preferences through an application developed using React Native. This interface can be intuitively operated on smartphones and tablets, enhancing the convenience for workers.

[0285] In addition, the user can check this information, compare it with their own schedule, and request corrections if necessary. This feedback is received again by the server, and recalculation is performed if necessary.

[0286] As a specific example, when an operator in a certain factory uses the application to input "I can work on Wednesday and Friday next week", the generated AI model compares it with the data of other workers and the machine maintenance schedule to calculate the optimal work arrangement. As a result, a notification "You have been assigned the early shift on Wednesday next week" is sent to the terminal for the operator.

[0287] As an example of a prompt sentence, a message such as "Please enter your work preferences for next week. The AI will propose the optimal shift." is used. This allows the operator to easily convey their work preferences to the system.

[0288] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0289] Step 1:

[0290] The user inputs work preference information and non - working preference information through the terminal. Specifically, using the operation interface of the smartphone application, information such as "I can work on Tuesday and Friday next week" is input. The input data is directly sent to the server via the communication device of the terminal.

[0291] Step 2:

[0292] The server receives work request and absence request information sent from the terminal. Before the received data is stored in the database, it undergoes preprocessing to prepare the data format. Specifically, it performs a process to convert text data into structured data.

[0293] Step 3:

[0294] The server runs a generative AI model based on worker work information stored in the database. The generative AI model is built using TensorFlow and takes in data such as work preferences, absence preferences, skill sets, and workload to calculate optimized work shifts. In this process, mathematical optimization is performed to maximize overall work efficiency while satisfying constraints.

[0295] Step 4:

[0296] The server then sends the generated optimized work assignment information back to the terminal. In practice, it converts the results obtained from machine learning processing into JSON format and sends it to the terminal via a communication device. It generates a prompt message and prepares the notification content for the user.

[0297] Step 5:

[0298] The terminal receives optimized work assignment information sent from the server. It generates a notification for the user, specifically displaying a message such as, "You are assigned to the morning shift this Wednesday." After receiving the notification, the user reviews the information and compares it with their own schedule.

[0299] Step 6:

[0300] If a user wishes to modify their shift schedule, they send the information back to the server via their terminal. Using the same interface, the user can, for example, enter "I want to change my Thursday shift," and that information is sent back to the server, repeating the optimization process.

[0301] 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.

[0302] This invention relates to a shift management system that uses an information processing device to collect employees' work and absence preferences, and combines this with an emotion engine that recognizes users' emotions. This system enables flexible shift scheduling that takes into account the individual needs and emotional states of employees, thereby improving the workplace environment.

[0303] In the system, a "terminal" receives user-entered work requests and absence requests as messages and transmits them to the server via a communication device. Users use a typical messaging application to enter text such as, for example, "I'd like to take this weekend off."

[0304] The "server" receives messages from terminals via communication devices and analyzes the emotions expressed by the user using an emotion engine. This analysis uses text analysis technology to identify the user's emotions from the context and linguistic tone of the message. In particular, if stress or anxiety is detected, corresponding information is recorded in the database.

[0305] The server then runs a machine learning model utilizing user preference information along with emotional data. This model generates optimal work shifts, taking into account the employees' emotional states. For example, if positive emotions are detected, the user's preferences can be given higher priority.

[0306] The generated work assignment information is then transmitted back to the terminal via the communication device. At this point, users who respond to positive emotions can be notified of their preferred shift assignments.

[0307] The user can view the notified shift information on the terminal and, if readjustment is necessary according to the situation, input and send their preferences again. This is expected to improve the flexibility of shift management and enhance workplace motivation.

[0308] The following is an explanation of the processing flow.

[0309] Step 1:

[0310] The user uses the terminal to input work preference information and absence preference information. Using a messaging application, they send a text message such as "I want to take a vacation on Friday next week." The terminal proceeds with preparations to send this message to the server.

[0311] Step 2:

[0312] The server receives the message sent from the terminal through the communication device. To analyze the content of the message, it uses an emotion engine to determine the user's emotional state from the text. For example, it identifies whether the user is in a relaxed state or feeling stressed through context analysis.

[0313] Step 3:

[0314] The server saves the work preference information including the analyzed emotion data in the database. Each user's data is organized along with their emotional state and recorded in detail. This data is used in the subsequent shift generation process.

[0315] Step 4:

[0316] The server utilizes the saved data and runs a machine learning model. This model takes into account the user's emotional state and other work preference conditions to generate an optimal shift arrangement for all employees. For example, processing is incorporated to prioritize the preferences of users showing positive emotions.

[0317] Step 5:

[0318] The server organizes the generated work assignment information and forms a message to be sent to the terminal using a messaging application. This message contains detailed information about work hours and location.

[0319] Step 6:

[0320] The terminal receives shift information sent from the server. It then prepares to notify the user and prompt them to confirm the information.

[0321] Step 7:

[0322] The user checks the shift information received on their terminal and determines if it matches their schedule. If adjustments are needed, they can re-enter their requests and submit the information. The terminal then sends this information back to the server, updating the system.

[0323] (Example 2)

[0324] 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".

[0325] Traditional shift management methods struggle to adequately address individual employee needs, such as preferred working hours and absences. Furthermore, they fail to consider employees' emotional states, making it difficult to optimize the work environment. As a result, there is a challenge in achieving flexible and efficient shift scheduling to improve employee motivation and performance.

[0326] 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.

[0327] In this invention, the server includes means for identifying worker emotional data using emotion analysis technology, means for generating optimized work assignment data using a machine learning model by combining the identified emotional data and preference data, and means for transmitting and notifying the generated work assignment data via a communication interface. This enables flexible and efficient shift scheduling that takes into account the emotional state of employees and responds to individual needs.

[0328] An "information processing unit" is a computing device or combination thereof used for collecting, analyzing, storing, and transmitting data.

[0329] A "communication unit" is a device or module that has the function of receiving and transmitting information via a communication interface.

[0330] "Communication interface" refers to means or devices for inputting or outputting information, including screens and terminals on which users can input information.

[0331] "Work schedule preference data" refers to information submitted by workers indicating their preferred work schedules.

[0332] "Vacation request data" refers to information indicating the desired vacation period for workers.

[0333] "Memory means" refers to a device or function for storing received data or generated information.

[0334] "Emotion analysis technology" is a technology that analyzes and identifies a person's emotional state from text, audio, and other data.

[0335] "Emotional data" refers to information representing the emotional state of a worker, identified based on emotional analysis.

[0336] A "machine learning model" is an algorithm or its implementation used to predict or generate the optimal outcome based on data.

[0337] "Work assignment data" refers to schedule information generated to optimize workers' working hours and shifts.

[0338] This invention is a system that generates optimal shift assignments by using an information processing system to collect employees' work preferences and absence preferences, and by using sentiment analysis technology to identify emotional data.

[0339] The terminal receives user-entered work and absence requests as messages. Users can do this using a standard messaging application, for example, by entering text such as "I would like to take next Monday off." This text is sent to the server via the communication unit. Communication is conducted using a secure communication protocol such as HTTPS.

[0340] The server analyzes messages received from the terminal and stores them in its storage device. Next, the server utilizes NLP libraries to analyze the user's emotional state from the received text using sentiment analysis techniques. Specifically, it uses spaCy or NLTK to analyze the text and identify an emotional score. This analysis result is recorded in a database.

[0341] Subsequently, the server runs a machine learning model based on sentiment data, work preferences, and absence preferences to generate the optimal work schedule. This model is built using TensorFlow or PyTorch, and is optimized after being trained on prior data. If the sentiment is positive, it is possible to adjust the model to prioritize the user's preferences.

[0342] The generated work assignment information is then transmitted back to the terminal via the communication unit. The terminal displays a notification to the user, who can then confirm it and reflect it in their shift management. If necessary, the user can make adjustments by re-entering and submitting their preferences, thereby ensuring flexibility in shift management.

[0343] For example, if a cafe staff member enters a preference such as "I want to spend the weekend with my family," the system can take this information into account and suggest the optimal shift based on their emotional state. An example of a prompt to input into the generative AI model would be, "Please create the optimal shift schedule based on the user's work preferences and emotional data."

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] Users enter their work and absence requests using a messaging application on their device. Specifically, they might enter text such as, "I'd like to take next Monday off." Once this message is entered, the device encrypts it via a communication unit and sends it to the server using a secure protocol (e.g., HTTPS). The input data is the text of the work or absence request, and the output is an encrypted data packet.

[0347] Step 2:

[0348] The server decodes the encrypted data received from the terminal and treats it as received data. The server analyzes the message content, records it in the database, and associates it with the ID of the person in charge. In this process, the input data is encrypted text data, and the output is plaintext text data. The plaintext text data is stored in the database as a log.

[0349] Step 3:

[0350] The server performs text analysis using sentiment analysis technology. Specifically, it extracts sentiment data from input messages using NLP libraries such as spaCy and NLTK. At this stage, the input is plain text data, and the output is sentiment data (such as sentiment scores and labels). This sentiment data is recorded in a database along with user information.

[0351] Step 4:

[0352] The server inputs information combining sentiment data and user work preferences and absence preferences into a machine learning model. At this stage, a model using TensorFlow or PyTorch analyzes the sentiment data and work preferences to generate the optimal work schedule. The input for this process is sentiment data and preference data, and the output is optimized work schedule data.

[0353] Step 5:

[0354] The server formats the generated work assignment data and sends it to the terminal via the communication unit. The data is formatted for display before transmission. The input data is work assignment data, and the output is formatted shift information.

[0355] Step 6:

[0356] The terminal displays the shift information received from the server on the screen. The user reviews this information and modifies or re-enters it as needed. The input data in this step is formatted shift information, and the output is new desired data (if necessary) based on the user's review.

[0357] (Application Example 2)

[0358] 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."

[0359] Traditional work management systems failed to adequately consider the personal emotional state of workers when assigning shifts, leading to problems such as decreased worker motivation and accumulated stress. Furthermore, it was difficult to implement the necessary measures to improve the overall work efficiency of the team, limiting productivity improvements. This highlighted the need for improvements to the workplace environment.

[0360] 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.

[0361] In this invention, the server includes means for receiving workers' work preference data and absence preference data, means for storing the received data, means for generating optimized work assignment data using a machine learning algorithm, and means for acquiring emotional state data using an emotion analysis engine and reflecting it in the work assignment. This enables flexible shift assignments that take into account the emotional state of workers, and is expected to improve the workplace environment and increase work efficiency.

[0362] An "information processing system" is a computing device used to interpret and analyze data related to workers' work performance and emotions.

[0363] A "communication interface" is a means of connection for sending and receiving information between different devices.

[0364] A "network medium" is an electronic medium used to transmit digital information.

[0365] "Worker's work preference data" refers to information about the working conditions desired by individual workers.

[0366] "Absence request data" refers to information about the days and times when an employee does not wish to work.

[0367] "Storage means" refers to devices or software that store received data and allow it to be retrieved as needed.

[0368] A "machine learning algorithm" is an analytical method that learns patterns and rules from large amounts of data to derive optimal results.

[0369] "Work assignment data" refers to information regarding the shift schedules assigned to each worker.

[0370] An "emotion analysis engine" is a system that analyzes and identifies a person's emotional state from text and other data.

[0371] This invention functions as a shift management system for workers in the workplace. This system consists of an information processing system, a communication interface, an emotion analysis engine, and a machine learning algorithm. A specific example of this system is described below.

[0372] First, the user (worker) inputs work preference data and absence preference data via a network medium using a terminal. The information is then transmitted to a communication interface via a smartphone or dedicated terminal. An information processing system connected to a server receives this data and stores it in a storage device.

[0373] Next, a machine learning algorithm is activated to generate optimized work assignment data based on stored work preference and absence preference data. A sentiment analysis engine is also used, employing text analysis techniques to understand the emotional state of workers from their messages. Natural language processing libraries such as NLTK and spaCy are used to identify emotional states such as stress and anxiety, and this information is reflected in the work assignments.

[0374] As a result, this system enables flexible and optimal shift scheduling that takes into account the emotional state of workers. For example, if a worker sends a message saying, "I'm busy this week and would like to take time off," their emotions are analyzed and a shift schedule is created that matches their wishes. Through this process, it is possible to reduce worker stress and improve the work environment.

[0375] The generation AI model uses a pre-trained model. An example of a prompt message is, "Calculate the optimal work arrangement to improve the workplace environment based on workers' shift preferences and sentiment data." Based on this, the AI ​​infers the optimal shift arrangement and notifies each worker and the necessary terminals of the result.

[0376] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0377] Step 1:

[0378] Users input their desired work schedules and absence requests using smartphones or dedicated terminals. This data is entered in text format and transmitted via a network medium through a communication interface. The input in this step consists of the user's various requests, and the output is the data received by the server.

[0379] Step 2:

[0380] The server receives work request data and absence request data transmitted through the communication interface and stores them in a storage device. This stored data serves as the basis for analysis in subsequent processing steps. The input for this step is the data transmitted by the user, and the output is the stored data.

[0381] Step 3:

[0382] The server uses stored work request and absence request data to analyze the user's emotional state using a sentiment analysis engine, leveraging natural language processing libraries (e.g., NLTK, spaCy). The analysis identifies the degree of stress and dissatisfaction and generates corresponding sentiment data. The input for this step is the stored data, and the output is the analyzed sentiment data.

[0383] Step 4:

[0384] The server generates optimized work assignment data using machine learning algorithms based on data obtained through sentiment analysis. The generating AI model calculates shift assignments that take into account workers' emotional states and work preferences. This step ensures that each worker receives the most efficient and preferred shifts. The inputs to this step are sentiment data and preference data, and the output is optimized shift data.

[0385] Step 5:

[0386] Finally, the server transmits and notifies each user's terminal via the network medium of the generated optimized work assignment data. Users can check the notified shift information on their terminal and request readjustments if necessary. The input for this step is the optimized shift data, and the output is the notified shift data.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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".

[0403] This invention is a shift management system using an information processing device that collects and stores information on employees' work preferences and absence preferences, and automatically generates and notifies them of optimized work assignments based on that information, thereby improving the efficiency of shift management operations.

[0404] In this system, the "terminal" first receives work preference and absence preference information entered by the user (employee) via the "communication device." Since the user enters this information through their usual messaging application, no special interface is required. For example, a user might send a message via the app stating, "I cannot work next Monday and Friday." The terminal then prepares to send this message to the server.

[0405] The "server" receives work preference and absence preference information sent from terminals using an "information processing device." The received information is stored in a database, and each user's information is organized. Subsequently, the server uses a machine learning model based on the stored data to generate an optimal work shift that takes into account work preferences and constraints. In this process, an optimized shift plan for the entire organization is formulated, taking into account employees' skill sets and workload balance.

[0406] The generated work assignment information is then sent back to the terminal via a communication device by the server. At this time, the system generates a message containing detailed information such as the start time of work and the planned assignment location.

[0407] Users can check the shift information they receive on their device. For example, if they receive a notification that they have been assigned an early shift on Tuesday, they can immediately check the details of the shift and compare it with their own schedule. If any changes to the shift are needed, they can communicate their requests again via the communication device.

[0408] The system of this invention streamlines the conventional manual shift management process, enabling rapid and accurate shift management. Furthermore, because it uses a common messaging tool as the communication medium, it achieves both employee convenience and efficient system operation.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] Users enter their work schedule preferences and absence requests via their terminal. They use a messaging application to send a text message, for example, "I would like to take next Thursday off." The terminal prepares this message for transmission and sends it to the server via a communication device.

[0412] Step 2:

[0413] The server receives messages sent from terminals via a communication device. It analyzes the received messages and stores work preference and absence preference information in a database. At this stage, each user's conditions and preferences are organized in preparation for the next processing step.

[0414] Step 3:

[0415] The server runs a machine learning model based on information from all employees stored in the database. The model generates the optimal work shift, taking into account each user's preferences and constraints. This process includes calculations that reflect individual workloads and preferred hours while maintaining an overall balance of work.

[0416] Step 4:

[0417] The server organizes the generated work assignment information and creates a message to send to the terminal via the messaging application. This message includes information such as the specific start time and location of the work assignment.

[0418] Step 5:

[0419] The terminal receives work shift information sent from the server via a messaging application. It then notifies the user, making the information available for review.

[0420] Step 6:

[0421] The user reviews the shift information sent on their device and determines if it matches their schedule. If corrections are needed, they can re-enter their preferences and resubmit. The device then processes the re-entered information and sends it back to the server.

[0422] (Example 1)

[0423] 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."

[0424] Conventional shift management systems posed a challenge because collecting information on employee work preferences and absence requests was cumbersome, and data allocation and distribution were inefficient, making it difficult to create quick and accurate shift plans. Furthermore, their poor usability resulted in decreased operational efficiency.

[0425] 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.

[0426] In this invention, the server includes a device that receives desired information transmitted by workers via a communication device and communication medium, a device that stores the received desired information in a structured database, and a device that analyzes the stored information and constructs an optimized work schedule using a generation AI model. This enables efficient and streamlined information processing and the automated generation of an optimal shift schedule.

[0427] "Communication equipment" is a general term for hardware or software used to send and receive data via a communication medium.

[0428] A "communication medium" is a technical element that provides a means for physically or wirelessly transferring data from a sender to a receiver.

[0429] The term "worker" refers to an individual or team engaged in a specific task or job.

[0430] "Desired information" refers to the collective data that includes work-related preferences and constraints entered by workers.

[0431] A "database" is an information system that stores and manages data in a structured format, enabling rapid retrieval and editing.

[0432] A "generative AI model" is a general term for models that use machine learning algorithms to analyze data and generate optimal output results.

[0433] An "optimized work schedule" refers to a work plan that is efficient and balanced for the entire operation, while taking into account the preferences and constraints of each worker.

[0434] A "notification device" is hardware or software that provides a means for informing a recipient of generated information.

[0435] The system of this invention utilizes information and communication technology to streamline shift management for work. A specific embodiment is shown below.

[0436] The server has the functionality to receive work preference and absence preference information sent by users via communication devices. Since users can easily input information using common messaging applications, no special interface is required. For example, a user might send a message stating, "I cannot work next Monday and Friday." This information is collected by the terminal and sent to the server.

[0437] The server stores the received information in a structured format in a database. This organizes each user's preferences and makes them efficiently available for subsequent processing. This data is analyzed using a generative AI model. The AI ​​model generates an optimized work schedule, taking into account the user's preferences, skill set, and workload balance. This program processing applies advanced machine learning techniques, particularly deep learning models. A concrete example of a prompt statement is, "To create the next week's shift schedule, please propose the optimal shift arrangement considering the preferences of the following employees."

[0438] The generated work schedule is then transmitted back to the user's terminal via the communication device. The user can check the shift information on their terminal and be immediately informed of details such as, "You have been assigned an early shift on Tuesday." If the user wishes to change their shift, they can efficiently adjust it by sending the information again from their terminal.

[0439] This system can significantly improve the efficiency of shift management through automated data collection and analysis. By utilizing common communication media, it is user-friendly and contributes to reducing system operating costs.

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] Users enter their preferred work schedules and availability information through their usual messaging app. The entered information is provided in natural language without any specific formatting requirements. This input is in the form of text messages sent by the user.

[0443] Step 2:

[0444] The terminal receives a message from the user and prepares it to be sent to the server via a communication device. Here, data preprocessing takes place, including format-specific conversions and the addition of metadata as needed. The output of this process is data in a format that the server can interpret.

[0445] Step 3:

[0446] The server receives information transmitted from terminals via communication devices. The received data is stored in a database as structured tables. This database storage is crucial for improving the reliability and access efficiency of the information. Access to the data is easily searchable and retrievalable using the data management system.

[0447] Step 4:

[0448] The server uses a generative AI model to analyze the stored data. This model automatically generates the optimal shifts, taking into account working conditions and constraints. The model reflects employee skills and organizational needs, and the calculated output is an optimized work schedule. An example of a prompt is, "To create the next week's shifts, please suggest the optimal shift arrangement considering the following employee preferences."

[0449] Step 5:

[0450] The server returns the generated work schedule to the terminal. In this step, data including specific work details (e.g., start time, assigned location) is transferred using a communication device. The output information is formatted so that it can be viewed by the user.

[0451] Step 6:

[0452] Users check their shift information through their terminal. The information they receive concerns specific work assignments and hours. If a shift change is needed, the user can re-enter their desired information based on the information they received and restart the cycle described above.

[0453] (Application Example 1)

[0454] 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."

[0455] Traditionally, managing worker schedules in factories required manual adjustments, which was time-consuming and labor-intensive. Furthermore, automatically generating optimal shifts that considered workers' technical aptitudes and machine maintenance schedules was difficult. As a result, operational efficiency decreased, and the burden on workers increased.

[0456] 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.

[0457] In this invention, the server includes means for receiving worker's preferred work schedule and preferred absence information via a communication medium, means for storing the received information and generating optimized work assignment information that takes into account the technology set and work coordination using a generation model, and means for transmitting and notifying the generated work assignment information. This enables the generation of efficient and less burdensome work assignments.

[0458] An "information processing device" is a device that can receive and store input data, as well as perform various calculations and data processing.

[0459] A "communication device" is a device that provides an interface for sending and receiving information and enables the efficient exchange of data.

[0460] "Communication media" is a general term for means of electronically transmitting information, and includes the internet and mobile networks.

[0461] "Work preference information" refers to data that indicates the working conditions and shift times that workers desire.

[0462] "Absence Request Information" refers to data indicating the days and times when an employee is unable to work.

[0463] "Storage means" refers to devices or functions for continuously retaining received data.

[0464] A "generative model" is a mathematical model that uses machine learning techniques to calculate the optimal work assignment from input data.

[0465] "Optimized work assignment information" refers to shift information that has been adjusted to achieve efficient staffing by taking into account workers' work preferences, skill sets, and work coordination.

[0466] An "operating medium" refers to a device or interface used by an operator for inputting or confirming data, and is a means for the user to interact with the system.

[0467] The system for implementing this invention achieves efficient work assignment management by having a server and terminals communicate with each other for information processing. The server stores the received work preference and absence preference information of workers and uses a generation AI model to calculate and generate the optimal work assignment based on this information. Specifically, it uses the Python programming language and machine learning frameworks such as TensorFlow to process data and optimize work shifts from the information stored in the database.

[0468] The server generates optimized work assignment information and sends it to the terminal, which then uses a communication device to notify the user of that information. The terminal provides an interface through an application developed using React Native, allowing workers to easily input their work preferences and absence requests. This interface is intuitive and easy to use on smartphones and tablets, enhancing worker convenience.

[0469] Furthermore, users can review this information, compare it to their own schedule, and request corrections as needed. This feedback is received by the server again, and recalculations are performed as necessary.

[0470] As a concrete example, if a factory worker enters into an application that they are available to work on Wednesday and Friday of next week, the generated AI model will compare this data with other workers' information and machine maintenance schedules to calculate the optimal work assignment. As a result, the worker will receive a notification on their device stating, "You have been assigned to the early shift next Wednesday."

[0471] An example of a prompt message would be, "Please enter your work preferences for next week. The AI ​​will suggest the best shift for you." This allows workers to easily communicate their work preferences to the system.

[0472] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0473] Step 1:

[0474] Users enter their preferred work schedules and availability information via their device. Specifically, they use the interface of a smartphone application to enter information such as "I am available to work next Tuesday and Friday." The entered data is then transmitted directly to the server via the device's communication equipment.

[0475] Step 2:

[0476] The server receives work request and absence request information sent from the terminal. Before the received data is stored in the database, it undergoes preprocessing to prepare the data format. Specifically, it performs a process to convert text data into structured data.

[0477] Step 3:

[0478] The server runs a generative AI model based on worker work information stored in the database. The generative AI model is built using TensorFlow and takes in data such as work preferences, absence preferences, skill sets, and workload to calculate optimized work shifts. In this process, mathematical optimization is performed to maximize overall work efficiency while satisfying constraints.

[0479] Step 4:

[0480] The server then sends the generated optimized work assignment information back to the terminal. In practice, it converts the results obtained from machine learning processing into JSON format and sends it to the terminal via a communication device. It generates a prompt message and prepares the notification content for the user.

[0481] Step 5:

[0482] The terminal receives optimized work assignment information sent from the server. It generates a notification for the user, specifically displaying a message such as, "You are assigned to the morning shift this Wednesday." After receiving the notification, the user reviews the information and compares it with their own schedule.

[0483] Step 6:

[0484] If a user wishes to modify their shift schedule, they send the information back to the server via their terminal. Using the same interface, the user can, for example, enter "I want to change my Thursday shift," and that information is sent back to the server, repeating the optimization process.

[0485] 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.

[0486] This invention relates to a shift management system that uses an information processing device to collect employees' work and absence preferences, and combines this with an emotion engine that recognizes users' emotions. This system enables flexible shift scheduling that takes into account the individual needs and emotional states of employees, thereby improving the workplace environment.

[0487] In the system, a "terminal" receives user-entered work requests and absence requests as messages and transmits them to the server via a communication device. Users use a typical messaging application to enter text such as, for example, "I'd like to take this weekend off."

[0488] The "server" receives messages from terminals via communication devices and analyzes the emotions expressed by the user using an emotion engine. This analysis uses text analysis technology to identify the user's emotions from the context and linguistic tone of the message. In particular, if stress or anxiety is detected, corresponding information is recorded in the database.

[0489] The server then runs a machine learning model utilizing user preference information along with emotional data. This model generates optimal work shifts, taking into account the employees' emotional states. For example, if positive emotions are detected, the user's preferences can be given higher priority.

[0490] The generated work assignment information is then transmitted back to the terminal via the communication device. At this point, users who respond to positive emotions can be notified of their preferred shift assignments.

[0491] Users can check the notified shift information on their device and, if necessary, re-enter and submit their preferences. This is expected to improve the flexibility of shift management and boost workplace motivation.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The user uses their terminal to enter their work schedule and absence requests. They then use a messaging application to send a text message such as, "I would like to take next Friday off." The terminal then prepares to send this message to the server.

[0495] Step 2:

[0496] The server receives messages sent from terminals via communication devices. To analyze the message content, it uses an emotion engine to determine the user's emotional state from the text. For example, contextual analysis identifies whether the user is relaxed or stressed.

[0497] Step 3:

[0498] The server stores work preference information, including analyzed emotional data, in a database. Each user's data is organized and recorded in detail, along with their emotional state. This data is used in the subsequent shift generation process.

[0499] Step 4:

[0500] The server utilizes stored data to run a machine learning model. This model takes into account the user's emotional state and other work preferences to generate the optimal shift schedule for all employees. For example, it incorporates processing to prioritize the preferences of users who exhibit positive emotions.

[0501] Step 5:

[0502] The server organizes the generated work assignment information and forms a message to be sent to the terminal using a messaging application. This message contains detailed information about work hours and location.

[0503] Step 6:

[0504] The terminal receives shift information sent from the server. It then prepares to notify the user and prompt them to confirm the information.

[0505] Step 7:

[0506] The user checks the shift information received on their terminal and determines if it matches their schedule. If adjustments are needed, they can re-enter their requests and submit the information. The terminal then sends this information back to the server, updating the system.

[0507] (Example 2)

[0508] 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."

[0509] Traditional shift management methods struggle to adequately address individual employee needs, such as preferred working hours and absences. Furthermore, they fail to consider employees' emotional states, making it difficult to optimize the work environment. As a result, there is a challenge in achieving flexible and efficient shift scheduling to improve employee motivation and performance.

[0510] 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.

[0511] In this invention, the server includes means for identifying worker emotional data using emotion analysis technology, means for generating optimized work assignment data using a machine learning model by combining the identified emotional data and preference data, and means for transmitting and notifying the generated work assignment data via a communication interface. This enables flexible and efficient shift scheduling that takes into account the emotional state of employees and responds to individual needs.

[0512] An "information processing unit" is a computing device or combination thereof used for collecting, analyzing, storing, and transmitting data.

[0513] A "communication unit" is a device or module that has the function of receiving and transmitting information via a communication interface.

[0514] "Communication interface" refers to means or devices for inputting or outputting information, including screens and terminals on which users can input information.

[0515] "Work schedule preference data" refers to information submitted by workers indicating their preferred work schedules.

[0516] "Vacation request data" refers to information indicating the desired vacation period for workers.

[0517] "Memory means" refers to a device or function for storing received data or generated information.

[0518] "Emotion analysis technology" is a technology that analyzes and identifies a person's emotional state from text, audio, and other data.

[0519] "Emotional data" refers to information representing the emotional state of a worker, identified based on emotional analysis.

[0520] A "machine learning model" is an algorithm or its implementation used to predict or generate the optimal outcome based on data.

[0521] "Work assignment data" refers to schedule information generated to optimize workers' working hours and shifts.

[0522] This invention is a system that generates optimal shift assignments by using an information processing system to collect employees' work preferences and absence preferences, and by using sentiment analysis technology to identify emotional data.

[0523] The terminal receives user-entered work and absence requests as messages. Users can do this using a standard messaging application, for example, by entering text such as "I would like to take next Monday off." This text is sent to the server via the communication unit. Communication is conducted using a secure communication protocol such as HTTPS.

[0524] The server analyzes messages received from the terminal and stores them in its storage device. Next, the server utilizes NLP libraries to analyze the user's emotional state from the received text using sentiment analysis techniques. Specifically, it uses spaCy or NLTK to analyze the text and identify an emotional score. This analysis result is recorded in a database.

[0525] Subsequently, the server runs a machine learning model based on sentiment data, work preferences, and absence preferences to generate the optimal work schedule. This model is built using TensorFlow or PyTorch, and is optimized after being trained on prior data. If the sentiment is positive, it is possible to adjust the model to prioritize the user's preferences.

[0526] The generated work assignment information is then transmitted back to the terminal via the communication unit. The terminal displays a notification to the user, who can then confirm it and reflect it in their shift management. If necessary, the user can make adjustments by re-entering and submitting their preferences, thereby ensuring flexibility in shift management.

[0527] For example, if a cafe staff member enters a preference such as "I want to spend the weekend with my family," the system can take this information into account and suggest the optimal shift based on their emotional state. An example of a prompt to input into the generative AI model would be, "Please create the optimal shift schedule based on the user's work preferences and emotional data."

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1:

[0530] Users enter their work and absence requests using a messaging application on their device. Specifically, they might enter text such as, "I'd like to take next Monday off." Once this message is entered, the device encrypts it via a communication unit and sends it to the server using a secure protocol (e.g., HTTPS). The input data is the text of the work or absence request, and the output is an encrypted data packet.

[0531] Step 2:

[0532] The server decodes the encrypted data received from the terminal and treats it as received data. The server analyzes the message content, records it in the database, and associates it with the ID of the person in charge. In this process, the input data is encrypted text data, and the output is plaintext text data. The plaintext text data is stored in the database as a log.

[0533] Step 3:

[0534] The server performs text analysis using sentiment analysis technology. Specifically, it extracts sentiment data from input messages using NLP libraries such as spaCy and NLTK. At this stage, the input is plain text data, and the output is sentiment data (such as sentiment scores and labels). This sentiment data is recorded in a database along with user information.

[0535] Step 4:

[0536] The server inputs information combining sentiment data and user work preferences and absence preferences into a machine learning model. At this stage, a model using TensorFlow or PyTorch analyzes the sentiment data and work preferences to generate the optimal work schedule. The input for this process is sentiment data and preference data, and the output is optimized work schedule data.

[0537] Step 5:

[0538] The server formats the generated work assignment data and sends it to the terminal via the communication unit. The data is formatted for display before transmission. The input data is work assignment data, and the output is formatted shift information.

[0539] Step 6:

[0540] The terminal displays the shift information received from the server on the screen. The user reviews this information and modifies or re-enters it as needed. The input data in this step is formatted shift information, and the output is new desired data (if necessary) based on the user's review.

[0541] (Application Example 2)

[0542] 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."

[0543] Traditional work management systems failed to adequately consider the personal emotional state of workers when assigning shifts, leading to problems such as decreased worker motivation and accumulated stress. Furthermore, it was difficult to implement the necessary measures to improve the overall work efficiency of the team, limiting productivity improvements. This highlighted the need for improvements to the workplace environment.

[0544] 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.

[0545] In this invention, the server includes means for receiving workers' work preference data and absence preference data, means for storing the received data, means for generating optimized work assignment data using a machine learning algorithm, and means for acquiring emotional state data using an emotion analysis engine and reflecting it in the work assignment. This enables flexible shift assignments that take into account the emotional state of workers, and is expected to improve the workplace environment and increase work efficiency.

[0546] An "information processing system" is a computing device used to interpret and analyze data related to workers' work performance and emotions.

[0547] A "communication interface" is a means of connection for sending and receiving information between different devices.

[0548] A "network medium" is an electronic medium used to transmit digital information.

[0549] "Worker's work preference data" refers to information about the working conditions desired by individual workers.

[0550] "Absence request data" refers to information about the days and times when an employee does not wish to work.

[0551] "Storage means" refers to devices or software that store received data and allow it to be retrieved as needed.

[0552] A "machine learning algorithm" is an analytical method that learns patterns and rules from large amounts of data to derive optimal results.

[0553] "Work assignment data" refers to information regarding the shift schedules assigned to each worker.

[0554] An "emotion analysis engine" is a system that analyzes and identifies a person's emotional state from text and other data.

[0555] This invention functions as a shift management system for workers in the workplace. This system consists of an information processing system, a communication interface, an emotion analysis engine, and a machine learning algorithm. A specific example of this system is described below.

[0556] First, the user (worker) inputs work preference data and absence preference data via a network medium using a terminal. The information is then transmitted to a communication interface via a smartphone or dedicated terminal. An information processing system connected to a server receives this data and stores it in a storage device.

[0557] Next, a machine learning algorithm is activated to generate optimized work assignment data based on stored work preference and absence preference data. A sentiment analysis engine is also used, employing text analysis techniques to understand the emotional state of workers from their messages. Natural language processing libraries such as NLTK and spaCy are used to identify emotional states such as stress and anxiety, and this information is reflected in the work assignments.

[0558] As a result, this system enables flexible and optimal shift scheduling that takes into account the emotional state of workers. For example, if a worker sends a message saying, "I'm busy this week and would like to take time off," their emotions are analyzed and a shift schedule is created that matches their wishes. Through this process, it is possible to reduce worker stress and improve the work environment.

[0559] The generation AI model uses a pre-trained model. An example of a prompt message is, "Calculate the optimal work arrangement to improve the workplace environment based on workers' shift preferences and sentiment data." Based on this, the AI ​​infers the optimal shift arrangement and notifies each worker and the necessary terminals of the result.

[0560] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0561] Step 1:

[0562] Users input their desired work schedules and absence requests using smartphones or dedicated terminals. This data is entered in text format and transmitted via a network medium through a communication interface. The input in this step consists of the user's various requests, and the output is the data received by the server.

[0563] Step 2:

[0564] The server receives work request data and absence request data transmitted through the communication interface and stores them in a storage device. This stored data serves as the basis for analysis in subsequent processing steps. The input for this step is the data transmitted by the user, and the output is the stored data.

[0565] Step 3:

[0566] The server uses stored work request and absence request data to analyze the user's emotional state using a sentiment analysis engine, leveraging natural language processing libraries (e.g., NLTK, spaCy). The analysis identifies the degree of stress and dissatisfaction and generates corresponding sentiment data. The input for this step is the stored data, and the output is the analyzed sentiment data.

[0567] Step 4:

[0568] The server generates optimized work assignment data using machine learning algorithms based on data obtained through sentiment analysis. The generating AI model calculates shift assignments that take into account workers' emotional states and work preferences. This step ensures that each worker receives the most efficient and preferred shifts. The inputs to this step are sentiment data and preference data, and the output is optimized shift data.

[0569] Step 5:

[0570] Finally, the server transmits and notifies each user's terminal via the network medium of the generated optimized work assignment data. Users can check the notified shift information on their terminal and request readjustments if necessary. The input for this step is the optimized shift data, and the output is the notified shift data.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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".

[0588] This invention is a shift management system using an information processing device that collects and stores information on employees' work preferences and absence preferences, and automatically generates and notifies them of optimized work assignments based on that information, thereby improving the efficiency of shift management operations.

[0589] In this system, the "terminal" first receives work preference and absence preference information entered by the user (employee) via the "communication device." Since the user enters this information through their usual messaging application, no special interface is required. For example, a user might send a message via the app stating, "I cannot work next Monday and Friday." The terminal then prepares to send this message to the server.

[0590] The "server" receives work preference and absence preference information sent from terminals using an "information processing device." The received information is stored in a database, and each user's information is organized. Subsequently, the server uses a machine learning model based on the stored data to generate an optimal work shift that takes into account work preferences and constraints. In this process, an optimized shift plan for the entire organization is formulated, taking into account employees' skill sets and workload balance.

[0591] The generated work assignment information is then sent back to the terminal via a communication device by the server. At this time, the system generates a message containing detailed information such as the start time of work and the planned assignment location.

[0592] Users can check the shift information they receive on their device. For example, if they receive a notification that they have been assigned an early shift on Tuesday, they can immediately check the details of the shift and compare it with their own schedule. If any changes to the shift are needed, they can communicate their requests again via the communication device.

[0593] The system of this invention streamlines the conventional manual shift management process, enabling rapid and accurate shift management. Furthermore, because it uses a common messaging tool as the communication medium, it achieves both employee convenience and efficient system operation.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] Users enter their work schedule preferences and absence requests via their terminal. They use a messaging application to send a text message, for example, "I would like to take next Thursday off." The terminal prepares this message for transmission and sends it to the server via a communication device.

[0597] Step 2:

[0598] The server receives messages sent from terminals via a communication device. It analyzes the received messages and stores work preference and absence preference information in a database. At this stage, each user's conditions and preferences are organized in preparation for the next processing step.

[0599] Step 3:

[0600] The server runs a machine learning model based on information from all employees stored in the database. The model generates the optimal work shift, taking into account each user's preferences and constraints. This process includes calculations that reflect individual workloads and preferred hours while maintaining an overall balance of work.

[0601] Step 4:

[0602] The server organizes the generated work assignment information and creates a message to send to the terminal via the messaging application. This message includes information such as the specific start time and location of the work assignment.

[0603] Step 5:

[0604] The terminal receives work shift information sent from the server via a messaging application. It then notifies the user, making the information available for review.

[0605] Step 6:

[0606] The user reviews the shift information sent on their device and determines if it matches their schedule. If corrections are needed, they can re-enter their preferences and resubmit. The device then processes the re-entered information and sends it back to the server.

[0607] (Example 1)

[0608] 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".

[0609] Conventional shift management systems posed a challenge because collecting information on employee work preferences and absence requests was cumbersome, and data allocation and distribution were inefficient, making it difficult to create quick and accurate shift plans. Furthermore, their poor usability resulted in decreased operational efficiency.

[0610] 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.

[0611] In this invention, the server includes a device that receives desired information transmitted by workers via a communication device and communication medium, a device that stores the received desired information in a structured database, and a device that analyzes the stored information and constructs an optimized work schedule using a generation AI model. This enables efficient and streamlined information processing and the automated generation of an optimal shift schedule.

[0612] "Communication equipment" is a general term for hardware or software used to send and receive data via a communication medium.

[0613] A "communication medium" is a technical element that provides a means for physically or wirelessly transferring data from a sender to a receiver.

[0614] The term "worker" refers to an individual or team engaged in a specific task or job.

[0615] "Desired information" refers to the collective data that includes work-related preferences and constraints entered by workers.

[0616] A "database" is an information system that stores and manages data in a structured format, enabling rapid retrieval and editing.

[0617] A "generative AI model" is a general term for models that use machine learning algorithms to analyze data and generate optimal output results.

[0618] An "optimized work schedule" refers to a work plan that is efficient and balanced for the entire operation, while taking into account the preferences and constraints of each worker.

[0619] A "notification device" is hardware or software that provides a means for informing a recipient of generated information.

[0620] The system of this invention utilizes information and communication technology to streamline shift management for work. A specific embodiment is shown below.

[0621] The server has the functionality to receive work preference and absence preference information sent by users via communication devices. Since users can easily input information using common messaging applications, no special interface is required. For example, a user might send a message stating, "I cannot work next Monday and Friday." This information is collected by the terminal and sent to the server.

[0622] The server stores the received information in a structured format in a database. This organizes each user's preferences and makes them efficiently available for subsequent processing. This data is analyzed using a generative AI model. The AI ​​model generates an optimized work schedule, taking into account the user's preferences, skill set, and workload balance. This program processing applies advanced machine learning techniques, particularly deep learning models. A concrete example of a prompt statement is, "To create the next week's shift schedule, please propose the optimal shift arrangement considering the preferences of the following employees."

[0623] The generated work schedule is then transmitted back to the user's terminal via the communication device. The user can check the shift information on their terminal and be immediately informed of details such as, "You have been assigned an early shift on Tuesday." If the user wishes to change their shift, they can efficiently adjust it by sending the information again from their terminal.

[0624] This system can significantly improve the efficiency of shift management through automated data collection and analysis. By utilizing common communication media, it is user-friendly and contributes to reducing system operating costs.

[0625] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0626] Step 1:

[0627] Users enter their preferred work schedules and availability information through their usual messaging app. The entered information is provided in natural language without any specific formatting requirements. This input is in the form of text messages sent by the user.

[0628] Step 2:

[0629] The terminal receives a message from the user and prepares it to be sent to the server via a communication device. Here, data preprocessing takes place, including format-specific conversions and the addition of metadata as needed. The output of this process is data in a format that the server can interpret.

[0630] Step 3:

[0631] The server receives information transmitted from terminals via communication devices. The received data is stored in a database as structured tables. This database storage is crucial for improving the reliability and access efficiency of the information. Access to the data is easily searchable and retrievalable using the data management system.

[0632] Step 4:

[0633] The server uses a generative AI model to analyze the stored data. This model automatically generates the optimal shifts, taking into account working conditions and constraints. The model reflects employee skills and organizational needs, and the calculated output is an optimized work schedule. An example of a prompt is, "To create the next week's shifts, please suggest the optimal shift arrangement considering the following employee preferences."

[0634] Step 5:

[0635] The server returns the generated work schedule to the terminal. In this step, data including specific work details (e.g., start time, assigned location) is transferred using a communication device. The output information is formatted so that it can be viewed by the user.

[0636] Step 6:

[0637] Users check their shift information through their terminal. The information they receive concerns specific work assignments and hours. If a shift change is needed, the user can re-enter their desired information based on the information they received and restart the cycle described above.

[0638] (Application Example 1)

[0639] 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".

[0640] Traditionally, managing worker schedules in factories required manual adjustments, which was time-consuming and labor-intensive. Furthermore, automatically generating optimal shifts that considered workers' technical aptitudes and machine maintenance schedules was difficult. As a result, operational efficiency decreased, and the burden on workers increased.

[0641] 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.

[0642] In this invention, the server includes means for receiving worker's preferred work schedule and preferred absence information via a communication medium, means for storing the received information and generating optimized work assignment information that takes into account the technology set and work coordination using a generation model, and means for transmitting and notifying the generated work assignment information. This enables the generation of efficient and less burdensome work assignments.

[0643] An "information processing device" is a device that can receive and store input data, as well as perform various calculations and data processing.

[0644] A "communication device" is a device that provides an interface for sending and receiving information and enables the efficient exchange of data.

[0645] "Communication media" is a general term for means of electronically transmitting information, and includes the internet and mobile networks.

[0646] "Work preference information" refers to data that indicates the working conditions and shift times that workers desire.

[0647] "Absence Request Information" refers to data indicating the days and times when an employee is unable to work.

[0648] "Storage means" refers to devices or functions for continuously retaining received data.

[0649] A "generative model" is a mathematical model that uses machine learning techniques to calculate the optimal work assignment from input data.

[0650] "Optimized work assignment information" refers to shift information that has been adjusted to achieve efficient staffing by taking into account workers' work preferences, skill sets, and work coordination.

[0651] An "operating medium" refers to a device or interface used by an operator for inputting or confirming data, and is a means for the user to interact with the system.

[0652] The system for implementing this invention achieves efficient work assignment management by having a server and terminals communicate with each other for information processing. The server stores the received work preference and absence preference information of workers and uses a generation AI model to calculate and generate the optimal work assignment based on this information. Specifically, it uses the Python programming language and machine learning frameworks such as TensorFlow to process data and optimize work shifts from the information stored in the database.

[0653] The server generates optimized work assignment information and sends it to the terminal, which then uses a communication device to notify the user of that information. The terminal provides an interface through an application developed using React Native, allowing workers to easily input their work preferences and absence requests. This interface is intuitive and easy to use on smartphones and tablets, enhancing worker convenience.

[0654] Furthermore, users can review this information, compare it to their own schedule, and request corrections as needed. This feedback is received by the server again, and recalculations are performed as necessary.

[0655] As a concrete example, if a factory worker enters into an application that they are available to work on Wednesday and Friday of next week, the generated AI model will compare this data with other workers' information and machine maintenance schedules to calculate the optimal work assignment. As a result, the worker will receive a notification on their device stating, "You have been assigned to the early shift next Wednesday."

[0656] An example of a prompt message would be, "Please enter your work preferences for next week. The AI ​​will suggest the best shift for you." This allows workers to easily communicate their work preferences to the system.

[0657] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0658] Step 1:

[0659] Users enter their preferred work schedules and availability information via their device. Specifically, they use the interface of a smartphone application to enter information such as "I am available to work next Tuesday and Friday." The entered data is then transmitted directly to the server via the device's communication equipment.

[0660] Step 2:

[0661] The server receives work request and absence request information sent from the terminal. Before the received data is stored in the database, it undergoes preprocessing to prepare the data format. Specifically, it performs a process to convert text data into structured data.

[0662] Step 3:

[0663] The server runs a generative AI model based on worker work information stored in the database. The generative AI model is built using TensorFlow and takes in data such as work preferences, absence preferences, skill sets, and workload to calculate optimized work shifts. In this process, mathematical optimization is performed to maximize overall work efficiency while satisfying constraints.

[0664] Step 4:

[0665] The server then sends the generated optimized work assignment information back to the terminal. In practice, it converts the results obtained from machine learning processing into JSON format and sends it to the terminal via a communication device. It generates a prompt message and prepares the notification content for the user.

[0666] Step 5:

[0667] The terminal receives optimized work assignment information sent from the server. It generates a notification for the user, specifically displaying a message such as, "You are assigned to the morning shift this Wednesday." After receiving the notification, the user reviews the information and compares it with their own schedule.

[0668] Step 6:

[0669] If a user wishes to modify their shift schedule, they send the information back to the server via their terminal. Using the same interface, the user can, for example, enter "I want to change my Thursday shift," and that information is sent back to the server, repeating the optimization process.

[0670] 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.

[0671] This invention relates to a shift management system that uses an information processing device to collect employees' work and absence preferences, and combines this with an emotion engine that recognizes users' emotions. This system enables flexible shift scheduling that takes into account the individual needs and emotional states of employees, thereby improving the workplace environment.

[0672] In the system, a "terminal" receives user-entered work requests and absence requests as messages and transmits them to the server via a communication device. Users use a typical messaging application to enter text such as, for example, "I'd like to take this weekend off."

[0673] The "server" receives messages from terminals via communication devices and analyzes the emotions expressed by the user using an emotion engine. This analysis uses text analysis technology to identify the user's emotions from the context and linguistic tone of the message. In particular, if stress or anxiety is detected, corresponding information is recorded in the database.

[0674] The server then runs a machine learning model utilizing user preference information along with emotional data. This model generates optimal work shifts, taking into account the employees' emotional states. For example, if positive emotions are detected, the user's preferences can be given higher priority.

[0675] The generated work assignment information is then transmitted back to the terminal via the communication device. At this point, users who respond to positive emotions can be notified of their preferred shift assignments.

[0676] Users can check the notified shift information on their device and, if necessary, re-enter and submit their preferences. This is expected to improve the flexibility of shift management and boost workplace motivation.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] The user uses their terminal to enter their work schedule and absence requests. They then use a messaging application to send a text message such as, "I would like to take next Friday off." The terminal then prepares to send this message to the server.

[0680] Step 2:

[0681] The server receives messages sent from terminals via communication devices. To analyze the message content, it uses an emotion engine to determine the user's emotional state from the text. For example, contextual analysis identifies whether the user is relaxed or stressed.

[0682] Step 3:

[0683] The server stores work preference information, including analyzed emotional data, in a database. Each user's data is organized and recorded in detail, along with their emotional state. This data is used in the subsequent shift generation process.

[0684] Step 4:

[0685] The server utilizes stored data to run a machine learning model. This model takes into account the user's emotional state and other work preferences to generate the optimal shift schedule for all employees. For example, it incorporates processing to prioritize the preferences of users who exhibit positive emotions.

[0686] Step 5:

[0687] The server organizes the generated work assignment information and forms a message to be sent to the terminal using a messaging application. This message contains detailed information about work hours and location.

[0688] Step 6:

[0689] The terminal receives shift information sent from the server. It then prepares to notify the user and prompt them to confirm the information.

[0690] Step 7:

[0691] The user checks the shift information received on their terminal and determines if it matches their schedule. If adjustments are needed, they can re-enter their requests and submit the information. The terminal then sends this information back to the server, updating the system.

[0692] (Example 2)

[0693] 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".

[0694] Traditional shift management methods struggle to adequately address individual employee needs, such as preferred working hours and absences. Furthermore, they fail to consider employees' emotional states, making it difficult to optimize the work environment. As a result, there is a challenge in achieving flexible and efficient shift scheduling to improve employee motivation and performance.

[0695] 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.

[0696] In this invention, the server includes means for identifying worker emotional data using emotion analysis technology, means for generating optimized work assignment data using a machine learning model by combining the identified emotional data and preference data, and means for transmitting and notifying the generated work assignment data via a communication interface. This enables flexible and efficient shift scheduling that takes into account the emotional state of employees and responds to individual needs.

[0697] An "information processing unit" is a computing device or combination thereof used for collecting, analyzing, storing, and transmitting data.

[0698] A "communication unit" is a device or module that has the function of receiving and transmitting information via a communication interface.

[0699] "Communication interface" refers to means or devices for inputting or outputting information, including screens and terminals on which users can input information.

[0700] "Work schedule preference data" refers to information submitted by workers indicating their preferred work schedules.

[0701] "Vacation request data" refers to information indicating the desired vacation period for workers.

[0702] "Memory means" refers to a device or function for storing received data or generated information.

[0703] "Emotion analysis technology" is a technology that analyzes and identifies a person's emotional state from text, audio, and other data.

[0704] "Emotional data" refers to information representing the emotional state of a worker, identified based on emotional analysis.

[0705] A "machine learning model" is an algorithm or its implementation used to predict or generate the optimal outcome based on data.

[0706] "Work assignment data" refers to schedule information generated to optimize workers' working hours and shifts.

[0707] This invention is a system that generates optimal shift assignments by using an information processing system to collect employees' work preferences and absence preferences, and by using sentiment analysis technology to identify emotional data.

[0708] The terminal receives user-entered work and absence requests as messages. Users can do this using a standard messaging application, for example, by entering text such as "I would like to take next Monday off." This text is sent to the server via the communication unit. Communication is conducted using a secure communication protocol such as HTTPS.

[0709] The server analyzes messages received from the terminal and stores them in its storage device. Next, the server utilizes NLP libraries to analyze the user's emotional state from the received text using sentiment analysis techniques. Specifically, it uses spaCy or NLTK to analyze the text and identify an emotional score. This analysis result is recorded in a database.

[0710] Subsequently, the server runs a machine learning model based on sentiment data, work preferences, and absence preferences to generate the optimal work schedule. This model is built using TensorFlow or PyTorch, and is optimized after being trained on prior data. If the sentiment is positive, it is possible to adjust the model to prioritize the user's preferences.

[0711] The generated work assignment information is then transmitted back to the terminal via the communication unit. The terminal displays a notification to the user, who can then confirm it and reflect it in their shift management. If necessary, the user can make adjustments by re-entering and submitting their preferences, thereby ensuring flexibility in shift management.

[0712] For example, if a cafe staff member enters a preference such as "I want to spend the weekend with my family," the system can take this information into account and suggest the optimal shift based on their emotional state. An example of a prompt to input into the generative AI model would be, "Please create the optimal shift schedule based on the user's work preferences and emotional data."

[0713] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0714] Step 1:

[0715] Users enter their work and absence requests using a messaging application on their device. Specifically, they might enter text such as, "I'd like to take next Monday off." Once this message is entered, the device encrypts it via a communication unit and sends it to the server using a secure protocol (e.g., HTTPS). The input data is the text of the work or absence request, and the output is an encrypted data packet.

[0716] Step 2:

[0717] The server decodes the encrypted data received from the terminal and treats it as received data. The server analyzes the message content, records it in the database, and associates it with the ID of the person in charge. In this process, the input data is encrypted text data, and the output is plaintext text data. The plaintext text data is stored in the database as a log.

[0718] Step 3:

[0719] The server performs text analysis using sentiment analysis technology. Specifically, it extracts sentiment data from input messages using NLP libraries such as spaCy and NLTK. At this stage, the input is plain text data, and the output is sentiment data (such as sentiment scores and labels). This sentiment data is recorded in a database along with user information.

[0720] Step 4:

[0721] The server inputs information combining sentiment data and user work preferences and absence preferences into a machine learning model. At this stage, a model using TensorFlow or PyTorch analyzes the sentiment data and work preferences to generate the optimal work schedule. The input for this process is sentiment data and preference data, and the output is optimized work schedule data.

[0722] Step 5:

[0723] The server formats the generated work assignment data and sends it to the terminal via the communication unit. The data is formatted for display before transmission. The input data is work assignment data, and the output is formatted shift information.

[0724] Step 6:

[0725] The terminal displays the shift information received from the server on the screen. The user reviews this information and modifies or re-enters it as needed. The input data in this step is formatted shift information, and the output is new desired data (if necessary) based on the user's review.

[0726] (Application Example 2)

[0727] 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".

[0728] Traditional work management systems failed to adequately consider the personal emotional state of workers when assigning shifts, leading to problems such as decreased worker motivation and accumulated stress. Furthermore, it was difficult to implement the necessary measures to improve the overall work efficiency of the team, limiting productivity improvements. This highlighted the need for improvements to the workplace environment.

[0729] 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.

[0730] In this invention, the server includes means for receiving workers' work preference data and absence preference data, means for storing the received data, means for generating optimized work assignment data using a machine learning algorithm, and means for acquiring emotional state data using an emotion analysis engine and reflecting it in the work assignment. This enables flexible shift assignments that take into account the emotional state of workers, and is expected to improve the workplace environment and increase work efficiency.

[0731] An "information processing system" is a computing device used to interpret and analyze data related to workers' work performance and emotions.

[0732] A "communication interface" is a means of connection for sending and receiving information between different devices.

[0733] A "network medium" is an electronic medium used to transmit digital information.

[0734] "Worker's work preference data" refers to information about the working conditions desired by individual workers.

[0735] "Absence request data" refers to information about the days and times when an employee does not wish to work.

[0736] "Storage means" refers to devices or software that store received data and allow it to be retrieved as needed.

[0737] A "machine learning algorithm" is an analytical method that learns patterns and rules from large amounts of data to derive optimal results.

[0738] "Work assignment data" refers to information regarding the shift schedules assigned to each worker.

[0739] An "emotion analysis engine" is a system that analyzes and identifies a person's emotional state from text and other data.

[0740] This invention functions as a shift management system for workers in the workplace. This system consists of an information processing system, a communication interface, an emotion analysis engine, and a machine learning algorithm. A specific example of this system is described below.

[0741] First, the user (worker) inputs work preference data and absence preference data via a network medium using a terminal. The information is then transmitted to a communication interface via a smartphone or dedicated terminal. An information processing system connected to a server receives this data and stores it in a storage device.

[0742] Next, a machine learning algorithm is activated to generate optimized work assignment data based on stored work preference and absence preference data. A sentiment analysis engine is also used, employing text analysis techniques to understand the emotional state of workers from their messages. Natural language processing libraries such as NLTK and spaCy are used to identify emotional states such as stress and anxiety, and this information is reflected in the work assignments.

[0743] As a result, this system enables flexible and optimal shift scheduling that takes into account the emotional state of workers. For example, if a worker sends a message saying, "I'm busy this week and would like to take time off," their emotions are analyzed and a shift schedule is created that matches their wishes. Through this process, it is possible to reduce worker stress and improve the work environment.

[0744] The generation AI model uses a pre-trained model. An example of a prompt message is, "Calculate the optimal work arrangement to improve the workplace environment based on workers' shift preferences and sentiment data." Based on this, the AI ​​infers the optimal shift arrangement and notifies each worker and the necessary terminals of the result.

[0745] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0746] Step 1:

[0747] Users input their desired work schedules and absence requests using smartphones or dedicated terminals. This data is entered in text format and transmitted via a network medium through a communication interface. The input in this step consists of the user's various requests, and the output is the data received by the server.

[0748] Step 2:

[0749] The server receives work request data and absence request data transmitted through the communication interface and stores them in a storage device. This stored data serves as the basis for analysis in subsequent processing steps. The input for this step is the data transmitted by the user, and the output is the stored data.

[0750] Step 3:

[0751] The server uses stored work request and absence request data to analyze the user's emotional state using a sentiment analysis engine, leveraging natural language processing libraries (e.g., NLTK, spaCy). The analysis identifies the degree of stress and dissatisfaction and generates corresponding sentiment data. The input for this step is the stored data, and the output is the analyzed sentiment data.

[0752] Step 4:

[0753] The server generates optimized work assignment data using machine learning algorithms based on data obtained through sentiment analysis. The generating AI model calculates shift assignments that take into account workers' emotional states and work preferences. This step ensures that each worker receives the most efficient and preferred shifts. The inputs to this step are sentiment data and preference data, and the output is optimized shift data.

[0754] Step 5:

[0755] Finally, the server transmits and notifies each user's terminal via the network medium of the generated optimized work assignment data. Users can check the notified shift information on their terminal and request readjustments if necessary. The input for this step is the optimized shift data, and the output is the notified shift data.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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."

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] The following is further disclosed regarding the embodiments described above.

[0778] (Claim 1)

[0779] A means for receiving worker work preference information and absence preference information entered via a communication medium through a communication device, based on a command from an information processing device,

[0780] A storage means for storing the received work preference information and absence preference information,

[0781] A means for generating optimized work assignment information using a machine learning model based on the information stored in the aforementioned storage means,

[0782] A means for transmitting and notifying the generated work assignment information via the communication medium,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, wherein spatiotemporal constraints are taken into consideration in the generation of the optimized work assignment information.

[0786] (Claim 3)

[0787] The system according to claim 1, wherein the input of the work preference information and the reception of the work assignment information are performed at the same operating interface.

[0788] "Example 1"

[0789] (Claim 1)

[0790] A device that receives desired information transmitted by a worker via a communication device and a communication medium,

[0791] A device that stores the received request information in a structured database,

[0792] A device that analyzes stored information and constructs an optimized work schedule using a generated AI model,

[0793] A device that notifies workers of the generated work schedule via a communication medium,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, wherein in constructing the optimized work schedule, the elements of work balance and capacity set are taken into consideration.

[0797] (Claim 3)

[0798] The system according to claim 1, wherein the input of the desired information and the reception of the work schedule are processed using a unified operating medium.

[0799] "Application Example 1"

[0800] (Claim 1)

[0801] A means for receiving worker work preference information and absence preference information entered via a communication medium through a communication device, based on a command from an information processing device,

[0802] A storage means for storing the received work preference information and absence preference information,

[0803] A means for generating optimized work assignment information based on the information stored in the aforementioned storage means, taking into account the skill set and work coordination of each worker using a generation model,

[0804] A means for transmitting and notifying the generated work assignment information via the communication medium,

[0805] A terminal is used by workers to input their preferred work schedules and to confirm their assigned work assignments.

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, wherein spatiotemporal constraints and machine maintenance schedules are taken into consideration in the generation of the optimized work assignment information.

[0809] (Claim 3)

[0810] The system according to claim 1, wherein the input of the work preference information and the reception of the work assignment information are performed on the same operating interface, and the system also has a function to display the operating status of the machine.

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

[0812] (Claim 1)

[0813] A means for receiving worker work preference data and leave preference data entered via a communication interface through a communication unit, based on a command from an information processing unit,

[0814] A storage means for storing the received work request data and the leave request data,

[0815] A means for identifying the worker's emotional data using emotion analysis technology based on the information stored in the aforementioned storage means,

[0816] A means for generating optimized work assignment data using a machine learning model by combining identified emotion data and the aforementioned preference data,

[0817] A means for transmitting and notifying the generated work assignment data via the communication interface,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, wherein in generating the optimized work assignment data, spatiotemporal constraints are taken into consideration and emotional data is used.

[0821] (Claim 3)

[0822] The system according to claim 1, wherein the input of the work preference data and the reception of the work assignment data are performed on the same operation screen.

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

[0824] (Claim 1)

[0825] A means for receiving worker work preference data and absence preference data provided via network media through a communication interface, in accordance with instructions from an information processing system.

[0826] A storage means for storing the received work request data and absence request data,

[0827] A means for generating optimized work assignment data using a machine learning algorithm based on the data recorded in the aforementioned storage means, and for acquiring workers' emotional state data using an emotion analysis engine and reflecting it in the work assignment,

[0828] A means for transmitting and notifying the generated work assignment data via the network medium,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein spatiotemporal constraints and emotional states are taken into consideration in the generation of the optimized work assignment data.

[0832] (Claim 3)

[0833] The system according to claim 1, wherein the provision of work preference data and the receipt of work assignment data are performed through a unified operating interface. [Explanation of Symbols]

[0834] 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. A means for receiving worker work preference information and absence preference information entered via a communication medium through a communication device, based on a command from an information processing device, A storage means for storing the received work preference information and absence preference information, A means for generating optimized work assignment information using a machine learning model based on the information stored in the aforementioned storage means, A means for transmitting and notifying the generated work assignment information via the communication medium, A system that includes this.

2. The system according to claim 1, wherein spatiotemporal constraints are taken into consideration in the generation of the optimized work assignment information.

3. The system according to claim 1, wherein the input of the work preference information and the reception of the work assignment information are performed at the same operating interface.

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