system

Abstract

The system according to this embodiment aims to efficiently reflect the conditions that should be considered when scheduling work shifts. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives verbal input of conditions that the user wants to consider when creating a shift schedule. The generation unit analyzes the conditions received by the reception unit and creates an optimal shift schedule considering the wishes of employees and the needs of the work. The provision unit provides the shift schedule created by the generation unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently reflect the conditions to be considered when forming a shift.

[0005] The system according to the embodiment aims to efficiently reflect the conditions to be considered when forming a shift.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit inputs, in words, the conditions to be considered when the user forms a shift. The generation unit analyzes the conditions received by the reception unit and creates an optimal shift in consideration of the wishes of employees and the necessity of work. The provision unit provides the shift created by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently reflect the conditions that need to be considered when scheduling shifts. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. 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).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The automatic shift scheduling tool according to an embodiment of the present invention is a system that automatically creates and provides an optimal shift schedule based on the user's input of conditions to be considered when scheduling shifts. This system comprises a reception unit where the user inputs the conditions to be considered when scheduling shifts in words, a generation unit that analyzes the conditions received by the reception unit and creates an optimal shift schedule considering employee preferences and work needs, and a provision unit that provides the shift schedule created by the generation unit to the user. For example, if the user inputs conditions such as "Person A wants to take Monday off" or "Person B is good at night shifts," the generation AI analyzes these conditions and calculates an optimal shift schedule based on data such as employee skills and past shift history. The generated shift schedule is provided to the user, who can modify it as needed. This makes it possible to automatically create an optimal shift schedule that takes into account employee preferences and work needs, saving the effort of scheduling shifts and enabling efficient shift management. Thus, the automatic shift scheduling tool can automatically create and provide an optimal shift schedule based on the user's input of conditions to be considered when scheduling shifts.

[0029] The automated shift creation tool according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit allows the user to input conditions they wish to consider when creating shifts. For example, the user can input conditions such as "Person A wants to take Monday off" or "Person B is good at night shifts." The reception unit can accept conditions by methods such as voice input or text input. The generation unit analyzes the conditions received by the reception unit and creates the optimal shift considering employee preferences and work needs. The generation unit uses a generation AI to analyze the conditions and calculates the optimal shift based on data such as employee skills and past shift history. For example, the generation AI considers employee skills and past shift history to create a shift where another employee is assigned to Monday when Person A wants to take off, and Person B is assigned a night shift. The provision unit provides the shift created by the generation unit to the user. The provision unit displays the generated shift to the user, who can make modifications as needed. For example, if the user is dissatisfied with the generated shift, they can re-enter the conditions and generate the shift again. As a result, the automatic shift creation tool according to this embodiment can automatically create and provide the optimal shifts based on the user's input of conditions they wish to consider when creating shifts.

[0030] The generation unit can calculate the optimal shift based on data such as employee skills and past shift history. For example, the generation unit can generate the optimal shift by considering employee skills. For instance, it can evaluate employee qualifications, years of experience, and specific skills, and create a shift based on that. The generation unit can also analyze an employee's past shift history to generate the optimal shift. For example, it can create a shift considering an employee's past working hours and assigned duties. Furthermore, the generation unit can combine employee skills and past shift history to generate the optimal shift. For example, it can analyze an employee's skills and past shift history in detail and create the optimal shift based on that. This allows the system to create the optimal shift by considering employee skills and past shift history.

[0031] The generation unit can set priority levels for conditions and include methods for handling cases where multiple conditions conflict. For example, the generation unit can set priority levels based on factors such as the importance of the conditions or the employee's preference. The generation unit can also include methods for handling cases where multiple conditions conflict. For example, the generation unit can resolve conflicting conditions by resetting priorities or adjusting the conditions. This allows for the creation of an optimal shift schedule by setting priorities even when multiple conditions conflict.

[0032] The service provider can provide the generated shifts to the user and allow for modifications as needed. For example, the service provider can display the generated shifts to the user. For example, the service provider can provide the generated shifts to the user through a web application or mobile application. The service provider can also provide the generated shifts in paper form. For example, the service provider can print the generated shifts and provide them to the user. Furthermore, the service provider can send the generated shifts via email. For example, the service provider can email the generated shifts to the user. This allows the user to review the generated shifts and make modifications as needed.

[0033] The reception desk can analyze the user's past condition input history and provide the optimal input interface. For example, the reception desk can automatically display conditions that the user has frequently entered in the past as suggestions. For instance, the reception desk can record the conditions the user has entered in the past and display suggestions based on that. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input in the past, it will prioritize displaying the voice input interface. Furthermore, the reception desk can predict and suggest conditions to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze the conditions the user has entered during specific time periods in the past and suggest conditions related to that time period. In this way, by analyzing past condition input history, the reception desk can provide the user with the optimal input interface.

[0034] The reception system can filter the input conditions based on the user's current work situation and areas of interest. For example, the reception system can prioritize displaying relevant conditions based on the user's current work situation. For instance, the reception system displays relevant conditions based on the user's input of their current work situation. The reception system can also suggest relevant conditions based on the user's areas of interest. For example, the reception system suggests conditions related to areas of interest the user has. Furthermore, the reception system can filter out unnecessary conditions based on the user's work situation and areas of interest. For example, the reception system filters out conditions that are not relevant to the user's current work situation or areas of interest, preventing them from being displayed. This allows users to input more relevant conditions by filtering them based on their work situation and areas of interest.

[0035] The reception system can prioritize inputting highly relevant conditions by considering the user's geographical location when conditions are entered. For example, if the user is in a specific region, the reception system will prioritize displaying conditions related to that region. The reception system can also suggest relevant conditions based on the user's geographical location. For example, the reception system will suggest relevant conditions based on the user's geographical location. Furthermore, the reception system can filter out unnecessary conditions based on the user's current location. For example, the reception system will filter out unnecessary conditions based on the user's current location. This allows for the creation of more appropriate shifts by prioritizing the input of highly relevant conditions based on the user's geographical location.

[0036] The reception desk can analyze the user's social media activity when conditions are entered and suggest relevant conditions. For example, the reception desk can automatically suggest relevant conditions based on the user's social media activity. For example, the reception desk can analyze the user's social media activity and suggest relevant conditions based on that analysis. The reception desk can also prioritize displaying relevant conditions based on the user's social media activity. For example, the reception desk prioritizes displaying relevant conditions based on the user's social media activity. Furthermore, the reception desk can analyze the user's social media activity and filter out unnecessary conditions. For example, the reception desk can analyze the user's social media activity and filter out unnecessary conditions. This allows the reception desk to suggest relevant conditions by analyzing the user's social media activity.

[0037] The shift generation unit can create optimal shifts by thoroughly analyzing employees' skills and past shift history during shift generation. For example, the unit can generate optimal shifts by considering employees' skills. For instance, it can evaluate employees' qualifications, years of experience, and specific skills, and create shifts based on that. The unit can also generate optimal shifts by analyzing employees' past shift history. For example, it can create shifts considering employees' past working hours and assigned duties. Furthermore, the unit can generate optimal shifts by combining employees' skills and past shift history. For example, it can thoroughly analyze employees' skills and past shift history and create optimal shifts based on that. This allows for the creation of more optimal shifts by thoroughly analyzing employees' skills and past shift history.

[0038] The generation unit can apply different generation algorithms depending on the business needs when generating shifts. For example, the generation unit can apply different generation algorithms depending on the business needs. For example, the generation unit can select the optimal generation algorithm based on the business needs. The generation unit can also generate shifts by combining multiple generation algorithms depending on the business needs. For example, the generation unit can generate shifts by combining different generation algorithms such as genetic algorithms and linear programming depending on the business needs. This allows for the creation of more appropriate shifts by applying different generation algorithms depending on the business needs.

[0039] The generation unit can create optimal shifts by considering employees' geographical location information during shift generation. For example, the generation unit can generate optimal shifts based on employees' geographical location information. For example, the generation unit can generate optimal shifts based on employees' current location. The generation unit can also filter out unnecessary shifts by considering employees' geographical location information. For example, the generation unit filters out unnecessary shifts by considering employees' geographical location information. This allows for the creation of more appropriate shifts by considering employees' geographical location information.

[0040] The shift generation unit can analyze employees' social media activity and suggest relevant shifts when generating shifts. For example, the unit can automatically suggest relevant shifts based on employees' social media activity. For example, the unit can analyze employees' social media activity and suggest relevant shifts based on that. The unit can also prioritize displaying relevant shifts based on employees' social media activity. For example, the unit can prioritize displaying relevant shifts based on employees' social media activity. Furthermore, the unit can analyze employees' social media activity and filter out unnecessary shifts. For example, the unit can analyze employees' social media activity and filter out unnecessary shifts. This allows the unit to suggest relevant shifts by analyzing employees' social media activity.

[0041] The service department can select the optimal service delivery method by referring to past user feedback when providing services in shifts. For example, the service department can select the optimal service delivery method based on past user feedback. For example, the service department can analyze past user feedback and select the optimal service delivery method based on that analysis. The service department can also select a service delivery method that reflects improvements based on past user feedback. For example, the service department can select a service delivery method that reflects improvements based on past user feedback. Furthermore, the service department can analyze past user feedback and select the most effective service delivery method. For example, the service department can analyze past user feedback and select the most effective service delivery method. In this way, the optimal service delivery method can be selected by referring to past user feedback.

[0042] The service provider can customize the means of providing shifts based on the user's current work situation. For example, the service provider can select the optimal means of providing shifts based on the user's current work situation. For example, the service provider can analyze the user's current work situation and select the optimal means of providing shifts based on that analysis. The service provider can also customize the means of providing shifts according to the user's work situation. For example, the service provider can customize the means of providing shifts according to the user's work situation. Furthermore, the service provider can filter out unnecessary means of providing shifts by considering the user's current work situation. For example, the service provider can filter out unnecessary means of providing shifts by considering the user's current work situation. This allows for the provision of more appropriate shifts by customizing the means of providing shifts according to the user's work situation.

[0043] The service provider can select the optimal service delivery method when providing shifts, taking into account the user's geographical location information. For example, the service provider can select the optimal service delivery method based on the user's geographical location information. For example, the service provider can analyze the user's geographical location information and select the optimal service delivery method based on that. The service provider can also select the optimal service delivery method based on the user's current location. For example, the service provider can select the optimal service delivery method based on the user's current location. Furthermore, the service provider can filter out unnecessary service delivery methods by taking into account the user's geographical location information. For example, the service provider can filter out unnecessary service delivery methods by taking into account the user's geographical location information. This allows for the provision of more appropriate shifts by selecting the optimal service delivery method based on the user's geographical location information.

[0044] The service provider can analyze the user's social media activity when providing shifts and suggest relevant shift adjustments. For example, the service provider can automatically suggest relevant shift adjustments based on the user's social media activity. For example, the service provider can analyze the user's social media activity and suggest relevant shift adjustments based on that analysis. The service provider can also prioritize displaying relevant shift adjustments based on the user's social media activity. For example, the service provider prioritizes displaying relevant shift adjustments based on the user's social media activity. Furthermore, the service provider can analyze the user's social media activity and filter out unnecessary shift adjustments. For example, the service provider analyzes the user's social media activity and filters out unnecessary shift adjustments. This allows the service provider to suggest relevant shift adjustments by analyzing the user's social media activity.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The reception desk can analyze a user's past shift creation history and automatically suggest frequently used conditions. For example, it can record conditions previously entered by the user and display suggestions based on that. It can also prioritize suggesting input methods previously used by the user (voice, text, etc.). Furthermore, it can predict and suggest conditions to be used during specific time periods based on the user's past input history. This allows the system to provide the user with the most optimal input interface by analyzing past condition input history.

[0047] The shift generation unit can create shifts while taking into account the health status of employees. For example, it can collect employee health data (e.g., heart rate, sleep duration, etc.) and adjust shifts based on that data. It can also appropriately allocate break times according to the health status of employees. Furthermore, it can monitor the health status of employees and readjust shifts if any abnormalities are detected. This makes it possible to create optimal shifts that take employee health into consideration.

[0048] The system can display a shift change history when providing generated shifts to users. For example, the system can display the shift change history in a timeline format, allowing users to see what changes they have made. It can also display the shift change history in a graph format, allowing users to visually understand the frequency and trends of changes. Furthermore, the system can export the shift change history and integrate it with other systems. This allows users to review the shift change history and make corrections as needed.

[0049] The shift generation unit can create optimal shifts by thoroughly analyzing employees' skills and past shift history during shift generation. For example, it can evaluate employees' qualifications, years of experience, and specific skills, and create shifts based on that. It can also create shifts considering employees' past work hours and assigned duties. Furthermore, it can generate optimal shifts by combining employees' skills with their past shift history. This allows for the creation of more optimal shifts by thoroughly analyzing employees' skills and past shift history.

[0050] The service department can select the optimal service delivery method by referring to past user feedback when providing services in shifts. For example, it can select the optimal delivery method based on past user feedback. It can also select a delivery method that reflects improvements based on past user feedback. Furthermore, it can analyze past user feedback and select the most effective delivery method. In this way, the optimal delivery method can be selected by referring to past user feedback.

[0051] The service provider can customize the method of service provision based on the user's current work situation when providing shifts. For example, it can select the most suitable method of service provision based on the user's current work situation. It can also customize the method of service provision according to the user's work situation. Furthermore, it can filter out unnecessary methods of service provision by considering the user's current work situation. This allows for the provision of more appropriate shifts by customizing the method of service provision according to the user's work situation.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The reception desk receives verbal input of conditions that users want to consider when creating work schedules. For example, users can enter conditions such as "Person A wants to take Monday off" or "Person B is good at night shifts." The reception desk can accept these conditions via voice input, text input, or other methods. Step 2: The generation unit analyzes the conditions received by the reception unit and creates the optimal shifts, taking into account employee preferences and work needs. The generation unit uses generation AI to analyze the conditions and calculates the optimal shifts based on data such as employee skills and past shift history. For example, the generation AI considers employee skills and past shift history to create a shift where, on Mondays when employee A wants to take the day off, another employee is assigned, and employee B is assigned the night shift. Step 3: The supply unit provides the user with the shifts created by the generation unit. The supply unit displays the generated shifts to the user, who can make modifications as needed. For example, if the user is dissatisfied with the generated shifts, they can re-enter the conditions and generate the shifts again.

[0054] (Example of form 2) The automatic shift scheduling tool according to an embodiment of the present invention is a system that automatically creates and provides an optimal shift schedule based on the user's input of conditions to be considered when scheduling shifts. This system comprises a reception unit where the user inputs the conditions to be considered when scheduling shifts in words, a generation unit that analyzes the conditions received by the reception unit and creates an optimal shift schedule considering employee preferences and work needs, and a provision unit that provides the shift schedule created by the generation unit to the user. For example, if the user inputs conditions such as "Person A wants to take Monday off" or "Person B is good at night shifts," the generation AI analyzes these conditions and calculates an optimal shift schedule based on data such as employee skills and past shift history. The generated shift schedule is provided to the user, who can modify it as needed. This makes it possible to automatically create an optimal shift schedule that takes into account employee preferences and work needs, saving the effort of scheduling shifts and enabling efficient shift management. Thus, the automatic shift scheduling tool can automatically create and provide an optimal shift schedule based on the user's input of conditions to be considered when scheduling shifts.

[0055] The automated shift creation tool according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit allows the user to input conditions they wish to consider when creating shifts. For example, the user can input conditions such as "Person A wants to take Monday off" or "Person B is good at night shifts." The reception unit can accept conditions by methods such as voice input or text input. The generation unit analyzes the conditions received by the reception unit and creates the optimal shift considering employee preferences and work needs. The generation unit uses a generation AI to analyze the conditions and calculates the optimal shift based on data such as employee skills and past shift history. For example, the generation AI considers employee skills and past shift history to create a shift where another employee is assigned to Monday when Person A wants to take off, and Person B is assigned a night shift. The provision unit provides the shift created by the generation unit to the user. The provision unit displays the generated shift to the user, who can make modifications as needed. For example, if the user is dissatisfied with the generated shift, they can re-enter the conditions and generate the shift again. As a result, the automatic shift creation tool according to this embodiment can automatically create and provide the optimal shifts based on the user's input of conditions they wish to consider when creating shifts.

[0056] The generation unit can calculate the optimal shift based on data such as employee skills and past shift history. For example, the generation unit can generate the optimal shift by considering employee skills. For instance, it can evaluate employee qualifications, years of experience, and specific skills, and create a shift based on that. The generation unit can also analyze an employee's past shift history to generate the optimal shift. For example, it can create a shift considering an employee's past working hours and assigned duties. Furthermore, the generation unit can combine employee skills and past shift history to generate the optimal shift. For example, it can analyze an employee's skills and past shift history in detail and create the optimal shift based on that. This allows the system to create the optimal shift by considering employee skills and past shift history.

[0057] The generation unit can set priority levels for conditions and include methods for handling cases where multiple conditions conflict. For example, the generation unit can set priority levels based on factors such as the importance of the conditions or the employee's preference. The generation unit can also include methods for handling cases where multiple conditions conflict. For example, the generation unit can resolve conflicting conditions by resetting priorities or adjusting the conditions. This allows for the creation of an optimal shift schedule by setting priorities even when multiple conditions conflict.

[0058] The service provider can provide the generated shifts to the user and allow for modifications as needed. For example, the service provider can display the generated shifts to the user. For example, the service provider can provide the generated shifts to the user through a web application or mobile application. The service provider can also provide the generated shifts in paper form. For example, the service provider can print the generated shifts and provide them to the user. Furthermore, the service provider can send the generated shifts via email. For example, the service provider can email the generated shifts to the user. This allows the user to review the generated shifts and make modifications as needed.

[0059] The reception desk can estimate the user's emotions and adjust the input method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For instance, if the user is stressed, the reception desk can display a simple input form and require only the minimum necessary information. Alternatively, if the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. For example, if the user is relaxed, the reception desk can display a detailed input form, allowing the user to freely input conditions. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick condition input. For example, if the user is in a hurry, the reception desk can display a voice input interface, allowing the user to input conditions by voice. This allows for more appropriate condition input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0060] The reception desk can analyze the user's past condition input history and provide the optimal input interface. For example, the reception desk can automatically display conditions that the user has frequently entered in the past as suggestions. For instance, the reception desk can record the conditions the user has entered in the past and display suggestions based on that. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input in the past, it will prioritize displaying the voice input interface. Furthermore, the reception desk can predict and suggest conditions to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze the conditions the user has entered during specific time periods in the past and suggest conditions related to that time period. In this way, by analyzing past condition input history, the reception desk can provide the user with the optimal input interface.

[0061] The reception system can filter the input conditions based on the user's current work situation and areas of interest. For example, the reception system can prioritize displaying relevant conditions based on the user's current work situation. For instance, the reception system displays relevant conditions based on the user's input of their current work situation. The reception system can also suggest relevant conditions based on the user's areas of interest. For example, the reception system suggests conditions related to areas of interest the user has. Furthermore, the reception system can filter out unnecessary conditions based on the user's work situation and areas of interest. For example, the reception system filters out conditions that are not relevant to the user's current work situation or areas of interest, preventing them from being displayed. This allows users to input more relevant conditions by filtering them based on their work situation and areas of interest.

[0062] The reception desk can estimate the user's emotions and determine the priority of the input conditions based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize processing important conditions. For example, if the user is stressed, the reception desk will prioritize processing conditions of high importance. For example, if the user is relaxed, the reception desk will prioritize processing detailed conditions. For example, if the user is relaxed, the reception desk will prioritize processing detailed conditions. For example, if the user is in a hurry, the reception desk will prioritize processing conditions that can be processed quickly. For example, if the user is in a hurry, the reception desk will prioritize processing conditions that can be processed quickly. This allows for the creation of more appropriate shifts by determining the priority of conditions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0063] The reception system can prioritize inputting highly relevant conditions by considering the user's geographical location when conditions are entered. For example, if the user is in a specific region, the reception system will prioritize displaying conditions related to that region. The reception system can also suggest relevant conditions based on the user's geographical location. For example, the reception system will suggest relevant conditions based on the user's geographical location. Furthermore, the reception system can filter out unnecessary conditions based on the user's current location. For example, the reception system will filter out unnecessary conditions based on the user's current location. This allows for the creation of more appropriate shifts by prioritizing the input of highly relevant conditions based on the user's geographical location.

[0064] The reception desk can analyze the user's social media activity when conditions are entered and suggest relevant conditions. For example, the reception desk can automatically suggest relevant conditions based on the user's social media activity. For example, the reception desk can analyze the user's social media activity and suggest relevant conditions based on that analysis. The reception desk can also prioritize displaying relevant conditions based on the user's social media activity. For example, the reception desk prioritizes displaying relevant conditions based on the user's social media activity. Furthermore, the reception desk can analyze the user's social media activity and filter out unnecessary conditions. For example, the reception desk can analyze the user's social media activity and filter out unnecessary conditions. This allows the reception desk to suggest relevant conditions by analyzing the user's social media activity.

[0065] The generation unit can estimate the user's emotions and adjust the shift generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a detailed shift. For example, if the user is relaxed, the generation unit can generate a detailed shift. The generation unit can also generate a fast shift if the user is in a hurry. For example, if the user is in a hurry, the generation unit can generate a fast shift. Furthermore, if the user is stressed, the generation unit can generate a simple shift. For example, if the user is stressed, the generation unit can generate a simple shift. In this way, by adjusting the shift generation method according to the user's emotions, more appropriate shifts can be created. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generation AI. Generation AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0066] The shift generation unit can create optimal shifts by thoroughly analyzing employees' skills and past shift history during shift generation. For example, the unit can generate optimal shifts by considering employees' skills. For instance, it can evaluate employees' qualifications, years of experience, and specific skills, and create shifts based on that. The unit can also generate optimal shifts by analyzing employees' past shift history. For example, it can create shifts considering employees' past working hours and assigned duties. Furthermore, the unit can generate optimal shifts by combining employees' skills and past shift history. For example, it can thoroughly analyze employees' skills and past shift history and create optimal shifts based on that. This allows for the creation of more optimal shifts by thoroughly analyzing employees' skills and past shift history.

[0067] The generation unit can apply different generation algorithms depending on the business needs when generating shifts. For example, the generation unit can apply different generation algorithms depending on the business needs. For example, the generation unit can select the optimal generation algorithm based on the business needs. The generation unit can also generate shifts by combining multiple generation algorithms depending on the business needs. For example, the generation unit can generate shifts by combining different generation algorithms such as genetic algorithms and linear programming depending on the business needs. This allows for the creation of more appropriate shifts by applying different generation algorithms depending on the business needs.

[0068] The generation unit can estimate the user's emotions and determine shift priorities based on those emotions. For example, if the user is stressed, the generation unit will prioritize generating important shifts. Similarly, if the user is relaxed, the generation unit can prioritize generating detailed shifts. Furthermore, if the user is in a hurry, the generation unit can prioritize generating shifts that can be generated quickly. This allows for the creation of more appropriate shifts by prioritizing them according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The generation unit can create optimal shifts by considering employees' geographical location information during shift generation. For example, the generation unit can generate optimal shifts based on employees' geographical location information. For example, the generation unit can generate optimal shifts based on employees' current location. The generation unit can also filter out unnecessary shifts by considering employees' geographical location information. For example, the generation unit filters out unnecessary shifts by considering employees' geographical location information. This allows for the creation of more appropriate shifts by considering employees' geographical location information.

[0070] The shift generation unit can analyze employees' social media activity and suggest relevant shifts when generating shifts. For example, the unit can automatically suggest relevant shifts based on employees' social media activity. For example, the unit can analyze employees' social media activity and suggest relevant shifts based on that. The unit can also prioritize displaying relevant shifts based on employees' social media activity. For example, the unit can prioritize displaying relevant shifts based on employees' social media activity. Furthermore, the unit can analyze employees' social media activity and filter out unnecessary shifts. For example, the unit can analyze employees' social media activity and filter out unnecessary shifts. This allows the unit to suggest relevant shifts by analyzing employees' social media activity.

[0071] The service provider can estimate the user's emotions and adjust the shift delivery method based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible delivery method. For example, if the user is nervous, the service provider can display the shifts with a simple interface. The service provider can also provide a delivery method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the service provider can display detailed shift information. Furthermore, if the user is in a hurry, the service provider can provide a concise delivery method. For example, if the user is in a hurry, the service provider can display concise shift information. By adjusting the shift delivery method according to the user's emotions, a more appropriate shift can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The service department can select the optimal service delivery method by referring to past user feedback when providing services in shifts. For example, the service department can select the optimal service delivery method based on past user feedback. For example, the service department can analyze past user feedback and select the optimal service delivery method based on that analysis. The service department can also select a service delivery method that reflects improvements based on past user feedback. For example, the service department can select a service delivery method that reflects improvements based on past user feedback. Furthermore, the service department can analyze past user feedback and select the most effective service delivery method. For example, the service department can analyze past user feedback and select the most effective service delivery method. In this way, the optimal service delivery method can be selected by referring to past user feedback.

[0073] The service provider can customize the means of providing shifts based on the user's current work situation. For example, the service provider can select the optimal means of providing shifts based on the user's current work situation. For example, the service provider can analyze the user's current work situation and select the optimal means of providing shifts based on that analysis. The service provider can also customize the means of providing shifts according to the user's work situation. For example, the service provider can customize the means of providing shifts according to the user's work situation. Furthermore, the service provider can filter out unnecessary means of providing shifts by considering the user's current work situation. For example, the service provider can filter out unnecessary means of providing shifts by considering the user's current work situation. This allows for the provision of more appropriate shifts by customizing the means of providing shifts according to the user's work situation.

[0074] The system can estimate the user's emotions and propose shift adjustment methods based on those emotions. For example, if the user is nervous, the system can propose a simple and easily visible adjustment method. For example, if the user is nervous, the system can propose an adjustment method with a simple interface. The system can also propose an adjustment method with more detailed information if the user is relaxed. For example, if the user is relaxed, the system can propose a detailed adjustment method. Furthermore, if the user is in a hurry, the system can propose an adjustment method that gets straight to the point. For example, if the user is in a hurry, the system can propose an adjustment method that gets straight to the point. This allows for more appropriate shift adjustments by proposing adjustment methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The service provider can select the optimal service delivery method when providing shifts, taking into account the user's geographical location information. For example, the service provider can select the optimal service delivery method based on the user's geographical location information. For example, the service provider can analyze the user's geographical location information and select the optimal service delivery method based on that. The service provider can also select the optimal service delivery method based on the user's current location. For example, the service provider can select the optimal service delivery method based on the user's current location. Furthermore, the service provider can filter out unnecessary service delivery methods by taking into account the user's geographical location information. For example, the service provider can filter out unnecessary service delivery methods by taking into account the user's geographical location information. This allows for the provision of more appropriate shifts by selecting the optimal service delivery method based on the user's geographical location information.

[0076] The service provider can analyze the user's social media activity when providing shifts and suggest relevant shift adjustments. For example, the service provider can automatically suggest relevant shift adjustments based on the user's social media activity. For example, the service provider can analyze the user's social media activity and suggest relevant shift adjustments based on that analysis. The service provider can also prioritize displaying relevant shift adjustments based on the user's social media activity. For example, the service provider prioritizes displaying relevant shift adjustments based on the user's social media activity. Furthermore, the service provider can analyze the user's social media activity and filter out unnecessary shift adjustments. For example, the service provider analyzes the user's social media activity and filters out unnecessary shift adjustments. This allows the service provider to suggest relevant shift adjustments by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user conditions using the touch panel 38A and microphone 38B of the smart device 14. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the conditions using generation AI and calculates the optimal shift based on data such as employee skills and past shift history. The provision unit provides the generated shift to the user using, for example, the display 40A and speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives user conditions using the microphone 238 of the smart glasses 214. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the conditions using generation AI and calculates the optimal shift based on data such as employee skills and past shift history. The provision unit provides the generated shift to the user, for example, using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives user conditions using the microphone 238 of the headset terminal 314. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the conditions using generation AI and calculates the optimal shift based on data such as employee skills and past shift history. The provision unit provides the generated shift to the user, for example, using the display 343 and speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives user conditions using the microphone 238 of the robot 414. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the conditions using a generation AI and calculates the optimal shift based on data such as employee skills and past shift history. The provision unit provides the generated shift to the user, for example, using the speaker 240 of the robot 414 and the controlled object 443.

[0077] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0078] The reception desk can analyze a user's past shift creation history and automatically suggest frequently used conditions. For example, it can record conditions previously entered by the user and display suggestions based on that. It can also prioritize suggesting input methods previously used by the user (voice, text, etc.). Furthermore, it can predict and suggest conditions to be used during specific time periods based on the user's past input history. This allows the system to provide the user with the most optimal input interface by analyzing past condition input history.

[0079] The shift generation unit can create shifts while taking into account the health status of employees. For example, it can collect employee health data (e.g., heart rate, sleep duration, etc.) and adjust shifts based on that data. It can also appropriately allocate break times according to the health status of employees. Furthermore, it can monitor the health status of employees and readjust shifts if any abnormalities are detected. This makes it possible to create optimal shifts that take employee health into consideration.

[0080] The system can display a shift change history when providing generated shifts to users. For example, the system can display the shift change history in a timeline format, allowing users to see what changes they have made. It can also display the shift change history in a graph format, allowing users to visually understand the frequency and trends of changes. Furthermore, the system can export the shift change history and integrate it with other systems. This allows users to review the shift change history and make corrections as needed.

[0081] The reception desk can estimate the user's emotions and adjust the input method based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of the conditions. This allows for more appropriate condition input by adjusting the input method according to the user's emotions.

[0082] The generation unit can estimate the user's emotions and adjust the shift generation method based on the estimated emotions. For example, if the user is relaxed, it can generate a detailed shift. If the user is in a hurry, it can generate a quick shift. Furthermore, if the user is stressed, it can generate a simple shift. In this way, by adjusting the shift generation method according to the user's emotions, a more appropriate shift can be created.

[0083] The delivery unit can estimate the user's emotions and adjust the way the shift is delivered based on those emotions. For example, if the user is nervous, a simple and highly visible delivery method can be provided. If the user is relaxed, a delivery method with more detailed information can be provided. Furthermore, if the user is in a hurry, a concise delivery method can be provided. By adjusting the way the shift is delivered according to the user's emotions, a more appropriate shift can be provided.

[0084] The shift generation unit can create optimal shifts by thoroughly analyzing employees' skills and past shift history during shift generation. For example, it can evaluate employees' qualifications, years of experience, and specific skills, and create shifts based on that. It can also create shifts considering employees' past work hours and assigned duties. Furthermore, it can generate optimal shifts by combining employees' skills with their past shift history. This allows for the creation of more optimal shifts by thoroughly analyzing employees' skills and past shift history.

[0085] The service department can select the optimal service delivery method by referring to past user feedback when providing services in shifts. For example, it can select the optimal delivery method based on past user feedback. It can also select a delivery method that reflects improvements based on past user feedback. Furthermore, it can analyze past user feedback and select the most effective delivery method. In this way, the optimal delivery method can be selected by referring to past user feedback.

[0086] The service provider can customize the method of service provision based on the user's current work situation when providing shifts. For example, it can select the most suitable method of service provision based on the user's current work situation. It can also customize the method of service provision according to the user's work situation. Furthermore, it can filter out unnecessary methods of service provision by considering the user's current work situation. This allows for the provision of more appropriate shifts by customizing the method of service provision according to the user's work situation.

[0087] The system can estimate the user's emotions and suggest shift adjustments based on those emotions. For example, if the user is stressed, it can suggest a simple and easily visible adjustment. If the user is relaxed, it can suggest an adjustment that includes more detailed information. Furthermore, if the user is in a hurry, it can suggest an adjustment that gets straight to the point. By suggesting shift adjustments according to the user's emotions, more appropriate shift adjustments become possible.

[0088] The following briefly describes the processing flow for example form 2.

[0089] Step 1: The reception desk receives verbal input of conditions that users want to consider when creating work schedules. For example, users can enter conditions such as "Person A wants to take Monday off" or "Person B is good at night shifts." The reception desk can accept these conditions via voice input, text input, or other methods. Step 2: The generation unit analyzes the conditions received by the reception unit and creates the optimal shifts, taking into account employee preferences and work needs. The generation unit uses generation AI to analyze the conditions and calculates the optimal shifts based on data such as employee skills and past shift history. For example, the generation AI considers employee skills and past shift history to create a shift where, on Mondays when employee A wants to take the day off, another employee is assigned, and employee B is assigned the night shift. Step 3: The supply unit provides the user with the shifts created by the generation unit. The supply unit displays the generated shifts to the user, who can make modifications as needed. For example, if the user is dissatisfied with the generated shifts, they can re-enter the conditions and generate the shifts again.

[0090] 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.

[0091] Data generation model 58 is a form of 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> 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 inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0092] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0094] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0095] 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.

[0096] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0097] 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.

[0098] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0099] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0100] 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.

[0101] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0102] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0103] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0104] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0110] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0111] 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.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0113] 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.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0115] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] 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.

[0117] 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.

[0118] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0129] 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.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0131] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] 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.

[0133] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0134] 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.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] 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.

[0144] Figure 9 shows the 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.

[0145] 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.

[0146] 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.

[0147] 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, and motorcycles, 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 based, for example, 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.

[0148] 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."

[0149] 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.

[0150] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0159] 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 other things 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.

[0160] 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.

[0161] [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk allows users to input in words the conditions they want to consider when creating shifts, A generation unit analyzes the conditions received by the reception unit and creates an optimal shift schedule considering employee preferences and work needs. The system includes a providing unit that provides the shift created by the generation unit to the user. A system characterized by the following features.

2. The generating unit is The system calculates the optimal shift schedule based on data such as employee skills and past shift history. The system according to feature 1.

3. The generating unit is This includes setting the priority of conditions and how to handle cases where multiple conditions conflict. The system according to feature 1.

4. The aforementioned supply unit is, The generated shifts can be provided to the user, who can then make corrections as needed. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for conditions based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyzes the user's past input history and provides the optimal input interface. The system according to feature 1.

7. The aforementioned reception unit is When entering criteria, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input conditions based on the estimated user emotions. The system according to feature 1.

Images