system

Abstract

The system according to this embodiment aims to propose sales schedules based on each employee's situation, analyze the results of their actions, and evaluate them in a way that is easy for managers to understand. [Solution] The system according to the embodiment comprises a proposal unit, an analysis unit, and an evaluation unit. The proposal unit proposes a sales schedule based on the circumstances of each employee. The analysis unit analyzes the results of the employees' actions based on the sales schedule proposed by the proposal unit. The evaluation unit displays the analysis results obtained by the analysis unit in a format that is easy for managers to understand.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is time-consuming to propose a sales schedule based on the situation of each employee and analyze the action results, and it is difficult for the administrator to evaluate in an easily understandable form.

[0005] The system according to the embodiment aims to propose a sales schedule based on the situation of each employee, analyze the action results, and evaluate in an easily understandable form for the administrator.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a proposal unit, an analysis unit, and an evaluation unit. The proposal unit proposes a sales schedule based on the circumstances of each employee. The analysis unit analyzes the results of the employees' actions based on the sales schedule proposed by the proposal unit. The evaluation unit displays the analysis results obtained by the analysis unit in a format that is easy for managers to understand. [Effects of the Invention]

[0007] The system according to this embodiment can propose a sales schedule based on the circumstances of each employee, analyze the results of their actions, and evaluate them in a way that is easy for managers to understand. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 system according to an embodiment of the present invention is a system that automates employee schedule management using an AI agent. In this system, first, the AI ​​agent proposes a sales schedule tailored to each employee's situation. Next, the AI ​​agent analyzes the employee's actions and evaluates whether they were ideal. Finally, the manager can easily understand the AI ​​agent's analysis results and evaluate the employee. This system enables efficient proposal of sales schedules, analysis of actions, and evaluation by managers. For example, the AI ​​agent generates an optimal schedule based on the employee's past behavioral data and performance data. For example, it proposes which customers should be visited and at what time of day, taking into account past sales performance and customer reactions. Next, the AI ​​agent compares the employee's actual actions with the proposed schedule and evaluates whether they were ideal. For example, it evaluates how ideal the actions were by comparing the results of following the proposed schedule with the results of the actual actions. Finally, the AI ​​agent displays the analysis results visually in an easy-to-understand manner, enabling managers to quickly make evaluations. For example, it displays the employee's actions and evaluations using graphs and charts. This system enables efficient sales schedule proposals, analysis of performance results, and evaluation by managers. This automates employee schedule management, reduces the burden on managers, and ensures accurate employee evaluations. Thus, a system utilizing AI agents can automate employee schedule management and support efficient sales activities.

[0029] The system according to this embodiment comprises a proposal unit, an analysis unit, and an evaluation unit. The proposal unit proposes a sales schedule based on the circumstances of each employee. The proposal unit generates a sales schedule considering, for example, the employee's working hours, performance, and skill set. The proposal unit can, for example, collect past behavioral data and performance data of employees and propose an optimal schedule based on that data. The proposal unit can also, for example, consider past sales performance and customer reactions to propose which customers should be visited and at what time of day they should be visited. The analysis unit analyzes the results of the employee's actions based on the sales schedule proposed by the proposal unit. The analysis unit can, for example, compare the proposed schedule with the actual actions and evaluate whether the actions were ideal. The analysis unit can, for example, evaluate how ideal the actions were by comparing the results of acting according to the proposed schedule with the results of the actual actions. The evaluation unit displays the analysis results obtained by the analysis unit in a format that is easy for managers to understand. The evaluation unit includes, for example, a display unit that visually displays the analysis results and can display the employee's actions and evaluation using graphs and charts. The evaluation unit can, for example, display analysis results in an easy-to-understand manner so that managers can quickly conduct evaluations. As a result, the system according to this embodiment can efficiently propose sales schedules for employees, analyze performance results, and perform evaluations by managers.

[0030] The proposal department proposes sales schedules based on each employee's situation. For example, the department generates sales schedules considering factors such as employee working hours, performance, and skill sets. Specifically, for working hours, it identifies the most efficient time slots for each employee based on past attendance records and shift patterns. For performance, it analyzes past sales data and customer feedback to evaluate which employees have strengths with which customers. Regarding skill sets, it databases each employee's expertise and experience to select the employee best suited to specific customer needs. The proposal department integrates this data to propose the optimal sales schedule for each employee. For example, it can suggest which customers to visit and at what time, taking into account past sales performance and customer responses. Furthermore, the proposal department utilizes AI to collect past behavioral and performance data and generate optimal schedules based on that data. The AI ​​uses machine learning algorithms to extract patterns from past data and predict future behavior. For example, it can predict when a particular customer is most responsive and what approach is most effective, and propose a schedule based on that. This allows the proposal department to provide flexible and effective sales schedules tailored to the individual circumstances of each employee.

[0031] The Analysis Department analyzes employee behavior based on the sales schedule proposed by the Proposal Department. For example, the Analysis Department compares the proposed schedule with actual behavior to evaluate whether it was ideal. Specifically, by comparing the results of following the proposed schedule with the results of actual behavior, it is possible to evaluate how ideal the behavior was. The Analysis Department uses AI to analyze the proposed schedule and actual behavior data in real time. The AI ​​uses data mining technology to identify correlations between the proposed schedule and actual behavior and analyze which factors had the greatest impact on results. For example, if a customer's response was good when visited during a particular time slot, the AI ​​will determine that that time slot is optimal and reflect this in future schedule proposals. It also analyzes the reasons why the proposed schedule was not followed and identifies areas for improvement in the schedule. For example, it considers the impact of external factors such as traffic conditions or sudden changes in customer schedules and reflects this in the next proposal. In this way, the Analysis Department can evaluate the effectiveness of the proposed schedule and provide feedback based on employee behavior results. Furthermore, by accumulating long-term data and conducting trend analysis, the Analysis Department also contributes to the formulation of future sales strategies.

[0032] The evaluation department displays the analysis results obtained by the analysis department in a format that is easy for managers to understand. For example, the evaluation department is equipped with a display unit that visually displays the analysis results, and can display employee behavior results and evaluations using graphs and charts. Specifically, it visually displays each employee's sales performance and behavior patterns using bar graphs, line graphs, pie charts, etc., so that they can grasp them at a glance. For example, by displaying charts that compare sales trends over a specific period or the frequency of visits to each customer and their results, managers can quickly make evaluations. Furthermore, the evaluation department displays the analysis results in a dashboard format and provides the latest information based on data that is updated in real time. The dashboard displays important indicators and KPIs (Key Performance Indicators), so managers can grasp the overall situation at a glance. The evaluation department can also automatically generate reports based on the analysis results and provide them to managers on a regular basis. The reports include detailed analysis results and suggested improvements, so managers can formulate specific instructions and strategies based on them. In this way, the evaluation department enables managers to make quick and accurate evaluations and effectively support employees' sales activities.

[0033] The system includes a data collection unit that collects past behavioral and performance data. The data collection unit can collect past behavioral data, such as employee visit history and closing history. The data collection unit can also collect performance data, such as employee sales data and closing rates. The data collection unit can automatically collect employee behavioral and performance data and provide it to the proposal unit. This allows the proposal unit to propose a more accurate sales schedule by collecting past behavioral and performance data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee behavioral data into AI and have the AI ​​perform data collection and organization.

[0034] The evaluation unit includes a display unit that visually displays the analysis results. The display unit can visually display the analysis results using, for example, graphs or charts. The display unit can display the analysis results in the form of, for example, bar graphs, pie charts, or line graphs. The display unit can also display the analysis results in a dashboard format, for example, so that administrators can understand them at a glance. This makes it easier for administrators to understand the analysis results by displaying them visually. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input the analysis results into AI and have AI perform the visual display.

[0035] The proposal department can generate an optimal schedule by considering past sales performance and customer feedback. For example, the proposal department can suggest the best destinations and times for visits based on employees' past sales data and closing rates. For example, the proposal department can also determine the priority of visits by considering customer feedback and survey results. For example, the proposal department can analyze past sales performance and customer feedback to generate an optimal schedule. This allows for the proposal of more effective sales schedules by considering past sales performance and customer feedback. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input past sales performance and customer feedback data into AI and have the AI ​​generate an optimal schedule.

[0036] The analysis department can compare and evaluate the proposed schedule with the actual actions. For example, the analysis department can compare the proposed schedule with the actual visit history of employees to evaluate how well the actions followed the schedule. For example, the analysis department can also evaluate whether the actions were ideal by comparing the results of following the proposed schedule with the results of the actual actions. For example, the analysis department can analyze the differences between the proposed schedule and the actual actions to identify areas for improvement. This allows for an accurate evaluation of actions by comparing the proposed schedule with the actual actions. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the proposed schedule and actual action data into the AI ​​and have the AI ​​perform the evaluation.

[0037] The evaluation unit can display employee behavior results and evaluations using graphs and charts. For example, the evaluation unit can display employee behavior results using bar graphs or pie charts to make them easier to understand visually. For example, the evaluation unit can display employee evaluations using line graphs to visually show changes over time. For example, the evaluation unit can display employee behavior results and evaluations in a dashboard format, allowing managers to grasp the overall situation at a glance. This makes it easier for managers to understand visually by using graphs and charts. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can input employee behavior results and evaluation data into AI and have the AI ​​generate graphs and charts.

[0038] The proposal department can analyze employees' past behavioral patterns and propose the optimal order of visits. For example, the proposal department can propose the most efficient order of visits based on past visit data. For example, the proposal department can optimize the order of visits by considering past customer responses. For example, the proposal department can propose the order of visits based on past sales performance. In this way, by analyzing past behavioral patterns, it is possible to propose an efficient order of visits. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input past behavioral data into AI and have the AI ​​propose the optimal order of visits.

[0039] The proposal unit can optimize the timing of visits by considering past customer response data. For example, the proposal unit can suggest the optimal timing of visits based on past customer response data. The proposal unit can also optimize the timing of visits by analyzing past customer response data. For example, the proposal unit can suggest the timing of visits by considering past customer response data. This allows the proposal unit to suggest the optimal timing of visits by considering past customer response data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input past customer response data into AI and have the AI ​​perform the optimization of visit timing.

[0040] The proposal department can propose a schedule that minimizes travel time by taking into account the geographical location information of employees. For example, the proposal department can propose the optimal order of visits based on the employee's current location. The proposal department can also propose a schedule that minimizes travel time by taking into account the geographical location information of employees. For example, the proposal department can propose an efficient schedule based on the employee's geographical location information. In this way, by taking into account geographical location information, an efficient schedule that minimizes travel time can be proposed. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI. For example, the proposal department can input the employee's geographical location information into AI and have AI perform the task of minimizing travel time.

[0041] The proposal department can analyze employees' social media activity and propose a schedule that prioritizes contact with relevant customers. For example, the proposal department can propose a schedule that prioritizes contact with relevant customers based on employees' social media activity. The proposal department can also analyze employees' social media activity and propose a schedule that prioritizes contact with relevant customers. The proposal department can also propose a schedule that prioritizes contact with relevant customers, taking into account employees' social media activity. This allows the proposal department to propose a schedule that prioritizes contact with relevant customers by analyzing social media activity. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input employee social media activity data into AI and have the AI ​​propose a schedule that prioritizes contact with relevant customers.

[0042] The analysis department can perform a detailed analysis of the correlation between employee behavior data and proposed schedules. For example, the analysis department can perform a detailed analysis of the correlation between employee behavior data and proposed schedules. The analysis department can also evaluate the effectiveness of proposed schedules based on employee behavior data. For example, the analysis department can analyze the correlation between employee behavior data and proposed schedules to identify areas for improvement. This allows for the evaluation of schedule effectiveness by analyzing the correlation between behavior data and proposed schedules. Some or all of the above-described processes in the analysis department may be performed using AI, or not. For example, the analysis department can input employee behavior data and proposed schedules into an AI and have the AI ​​perform the correlation analysis.

[0043] The analysis unit can evaluate the outcome of actions by taking customer feedback data into consideration. The analysis unit can, for example, evaluate the outcome of actions based on customer feedback data. The analysis unit can also, for example, evaluate the outcome of actions by taking customer feedback data into consideration. The analysis unit can also, for example, analyze customer feedback data and evaluate the outcome of actions. This allows for an accurate evaluation of the outcome of actions by taking customer feedback data into consideration. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input customer feedback data into AI and have AI perform the evaluation of the outcome of actions.

[0044] The analysis department can compare behavioral results by region, taking into account the geographical range of employees' activities. The analysis department can, for example, compare behavioral results by region based on the geographical range of employees' activities. The analysis department can, for example, compare behavioral results by region, taking into account the geographical range of employees' activities. The analysis department can, for example, analyze the geographical range of employees' activities and compare behavioral results by region. This allows for the comparison of behavioral results by region by considering the geographical range of activities. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input employees' geographical activity data into AI and have AI perform a comparison of behavioral results by region.

[0045] The analysis department can adjust the evaluation criteria for behavioral results by referring to employees' past performance data. For example, the analysis department can adjust the evaluation criteria for behavioral results based on employees' past performance data. The analysis department can also adjust the evaluation criteria for behavioral results by referring to employees' past performance data. For example, the analysis department can analyze employees' past performance data and adjust the evaluation criteria for behavioral results. This allows for appropriate adjustment of evaluation criteria by referring to past performance data. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input employees' past performance data into AI and have the AI ​​perform the adjustment of evaluation criteria.

[0046] The evaluation department can optimize evaluation criteria by referring to an employee's past evaluation history. The evaluation department can optimize evaluation criteria based on an employee's past evaluation history, for example. The evaluation department can also optimize evaluation criteria by referring to an employee's past evaluation history, for example. The evaluation department can also optimize evaluation criteria by analyzing an employee's past evaluation history, for example. This allows for the appropriate optimization of evaluation criteria by referring to past evaluation history. Some or all of the above processes in the evaluation department may be performed using AI, for example, or without AI. For example, the evaluation department can input an employee's past evaluation history into AI and have the AI ​​perform the optimization of evaluation criteria.

[0047] The evaluation department can customize evaluation results by taking into account the employee's skill set. For example, the evaluation department can customize evaluation results based on the employee's skill set. For example, the evaluation department can customize evaluation results by taking into account the employee's skill set. For example, the evaluation department can analyze the employee's skill set and customize the evaluation results. This allows for the provision of more appropriate evaluation results by taking skill sets into consideration. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI. For example, the evaluation department can input employee skill set data into AI and have the AI ​​perform the customization of evaluation results.

[0048] The evaluation department can compare evaluation results by region, taking into account the geographical range of employees' activities. The evaluation department can, for example, compare evaluation results by region based on the geographical range of employees' activities. The evaluation department can, for example, compare evaluation results by region, taking into account the geographical range of employees' activities. The evaluation department can, for example, analyze the geographical range of employees' activities and compare evaluation results by region. This allows for the comparison of evaluation results by region, taking into account the geographical range of activities. Some or all of the above processing in the evaluation department may be performed using AI, for example, or without AI. For example, the evaluation department can input employees' geographical activity data into AI and have AI perform the comparison of evaluation results by region.

[0049] The evaluation department can analyze employees' social media activities and display relevant evaluation results. For example, the evaluation department can display relevant evaluation results based on employees' social media activities. The evaluation department can also analyze employees' social media activities and display relevant evaluation results. The evaluation department can also consider employees' social media activities and display relevant evaluation results. This allows the evaluation department to provide relevant evaluation results by analyzing social media activities. Some or all of the above processing in the evaluation department may be performed using AI, for example, or without AI. For example, the evaluation department can input employee social media activity data into AI and have the AI ​​display relevant evaluation results.

[0050] The data collection unit can analyze employees' past behavioral data in detail and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on employees' past behavioral data. The data collection unit can also analyze employees' past behavioral data in detail and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method by considering employees' past behavioral data. This allows for the selection of the optimal data collection method by analyzing past behavioral data in detail. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employees' past behavioral data into AI and have the AI ​​select the optimal data collection method.

[0051] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees. For example, the data collection unit can prioritize the collection of highly relevant data based on the geographical location information of employees. For example, the data collection unit can also prioritize the collection of highly relevant data by considering the geographical location information of employees. For example, the data collection unit can analyze the geographical location information of employees and prioritize the collection of highly relevant data. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of employees into AI and have AI perform the collection of highly relevant data.

[0052] The display unit can select the optimal display method by referring to the administrator's past operation history. The display unit can, for example, select the optimal display method based on the administrator's past operation history. The display unit can also, for example, refer to the administrator's past operation history to select the optimal display method. The display unit can also, for example, analyze the administrator's past operation history to select the optimal display method. This allows the display unit to select the optimal display method by referring to past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the administrator's past operation history into AI and have AI perform the selection of the optimal display method.

[0053] The display unit can select the optimal display method by considering the administrator's device information. For example, if the administrator is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the administrator is using a tablet, the display unit can also provide a display method optimized for a larger screen. For example, if the administrator is using a desktop, the display unit can also provide a detailed display method. In this way, the optimal display method can be provided by considering the device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the administrator's device information into the AI ​​and have the AI ​​select the optimal display method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The proposal department can suggest sales schedules that take into account the health status of employees. For example, by collecting employee health data, they can propose a schedule that prioritizes rest if fatigue is accumulating. Conversely, if an employee is in good health, they can suggest more proactive sales activities. Furthermore, for employees with specific health risks, they can propose a schedule that helps mitigate those risks. This allows them to propose the optimal sales schedule based on the health status of each employee.

[0056] The analytics department can collect employee behavioral data in real time and provide analysis results immediately. For example, by collecting behavioral data in real time while employees are conducting sales activities and performing immediate analysis, rapid feedback can be provided. Furthermore, real-time data collection allows for analysis results that can respond to sudden changes in circumstances. In addition, real-time data collection and analysis can provide advice to immediately correct employee behavior. In short, real-time data collection and analysis enable the provision of quick and appropriate feedback.

[0057] The evaluation department can conduct relative evaluations by comparing employees' actions with those of other employees. For example, it can evaluate relative performance by comparing it with the actions of other employees within the same sales team. It can also evaluate progress by comparing it with similar past sales activities. Furthermore, it can evaluate industry-wide performance by comparing it with employees in other departments or other companies. This allows for a more accurate assessment of employee performance through relative evaluation.

[0058] The proposal department can propose sales schedules based on each employee's individual goals. For example, it can propose an optimal schedule for achieving individual goals set by each employee. It can also adjust the schedule as needed, taking into account the progress towards achieving those goals. Furthermore, it can support goal achievement by optimizing resource allocation according to each employee's goals. This allows for the proposal of optimal sales schedules based on each employee's individual goals.

[0059] The analytics department can identify behavioral patterns and propose areas for improvement based on employee behavioral data. For example, it can analyze past behavioral data to identify successful and unsuccessful patterns. It can also analyze how specific behavioral patterns affect results and propose areas for improvement. Furthermore, it can provide specific advice to employees based on the analysis of behavioral patterns. In this way, it can make concrete suggestions for improving employee behavior based on the analysis of behavioral patterns.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The proposal department proposes a sales schedule based on each employee's situation. The proposal department generates a sales schedule considering factors such as employees' working hours, performance, and skill sets. The proposal department collects past behavioral and performance data and proposes the optimal schedule based on it. Furthermore, it can also propose which customers should be visited and at what time of day, taking into account past sales performance and customer reactions. Step 2: The analysis department analyzes the results of employee actions based on the sales schedule proposed by the proposal department. The analysis department compares the proposed schedule with the actual actions and evaluates whether the actions were ideal. Furthermore, it evaluates the degree to which the actions were ideal by comparing the results of following the proposed schedule with the results of the actual actions. Step 3: The evaluation unit displays the analysis results obtained by the analysis unit in a format that is easy for managers to understand. The evaluation unit is equipped with a display unit that visually displays the analysis results, using graphs and charts to display employee behavior results and evaluations. This makes it possible to display the analysis results in an easy-to-understand manner, enabling managers to conduct evaluations quickly.

[0062] (Example of form 2) The system according to an embodiment of the present invention is a system that automates employee schedule management using an AI agent. In this system, first, the AI ​​agent proposes a sales schedule tailored to each employee's situation. Next, the AI ​​agent analyzes the employee's actions and evaluates whether they were ideal. Finally, the manager can easily understand the AI ​​agent's analysis results and evaluate the employee. This system enables efficient proposal of sales schedules, analysis of actions, and evaluation by managers. For example, the AI ​​agent generates an optimal schedule based on the employee's past behavioral data and performance data. For example, it proposes which customers should be visited and at what time of day, taking into account past sales performance and customer reactions. Next, the AI ​​agent compares the employee's actual actions with the proposed schedule and evaluates whether they were ideal. For example, it evaluates how ideal the actions were by comparing the results of following the proposed schedule with the results of the actual actions. Finally, the AI ​​agent displays the analysis results visually in an easy-to-understand manner, enabling managers to quickly make evaluations. For example, it displays the employee's actions and evaluations using graphs and charts. This system enables efficient sales schedule proposals, analysis of performance results, and evaluation by managers. This automates employee schedule management, reduces the burden on managers, and ensures accurate employee evaluations. Thus, a system utilizing AI agents can automate employee schedule management and support efficient sales activities.

[0063] The system according to this embodiment comprises a proposal unit, an analysis unit, and an evaluation unit. The proposal unit proposes a sales schedule based on the circumstances of each employee. The proposal unit generates a sales schedule considering, for example, the employee's working hours, performance, and skill set. The proposal unit can, for example, collect past behavioral data and performance data of employees and propose an optimal schedule based on that data. The proposal unit can also, for example, consider past sales performance and customer reactions to propose which customers should be visited and at what time of day they should be visited. The analysis unit analyzes the results of the employee's actions based on the sales schedule proposed by the proposal unit. The analysis unit can, for example, compare the proposed schedule with the actual actions and evaluate whether the actions were ideal. The analysis unit can, for example, evaluate how ideal the actions were by comparing the results of acting according to the proposed schedule with the results of the actual actions. The evaluation unit displays the analysis results obtained by the analysis unit in a format that is easy for managers to understand. The evaluation unit includes, for example, a display unit that visually displays the analysis results and can display the employee's actions and evaluation using graphs and charts. The evaluation unit can, for example, display analysis results in an easy-to-understand manner so that managers can quickly conduct evaluations. As a result, the system according to this embodiment can efficiently propose sales schedules for employees, analyze performance results, and perform evaluations by managers.

[0064] The proposal department proposes sales schedules based on each employee's situation. For example, the department generates sales schedules considering factors such as employee working hours, performance, and skill sets. Specifically, for working hours, it identifies the most efficient time slots for each employee based on past attendance records and shift patterns. For performance, it analyzes past sales data and customer feedback to evaluate which employees have strengths with which customers. Regarding skill sets, it databases each employee's expertise and experience to select the employee best suited to specific customer needs. The proposal department integrates this data to propose the optimal sales schedule for each employee. For example, it can suggest which customers to visit and at what time, taking into account past sales performance and customer responses. Furthermore, the proposal department utilizes AI to collect past behavioral and performance data and generate optimal schedules based on that data. The AI ​​uses machine learning algorithms to extract patterns from past data and predict future behavior. For example, it can predict when a particular customer is most responsive and what approach is most effective, and propose a schedule based on that. This allows the proposal department to provide flexible and effective sales schedules tailored to the individual circumstances of each employee.

[0065] The Analysis Department analyzes employee behavior based on the sales schedule proposed by the Proposal Department. For example, the Analysis Department compares the proposed schedule with actual behavior to evaluate whether it was ideal. Specifically, by comparing the results of following the proposed schedule with the results of actual behavior, it is possible to evaluate how ideal the behavior was. The Analysis Department uses AI to analyze the proposed schedule and actual behavior data in real time. The AI ​​uses data mining technology to identify correlations between the proposed schedule and actual behavior and analyze which factors had the greatest impact on results. For example, if a customer's response was good when visited during a particular time slot, the AI ​​will determine that that time slot is optimal and reflect this in future schedule proposals. It also analyzes the reasons why the proposed schedule was not followed and identifies areas for improvement in the schedule. For example, it considers the impact of external factors such as traffic conditions or sudden changes in customer schedules and reflects this in the next proposal. In this way, the Analysis Department can evaluate the effectiveness of the proposed schedule and provide feedback based on employee behavior results. Furthermore, by accumulating long-term data and conducting trend analysis, the Analysis Department also contributes to the formulation of future sales strategies.

[0066] The evaluation department displays the analysis results obtained by the analysis department in a format that is easy for managers to understand. For example, the evaluation department is equipped with a display unit that visually displays the analysis results, and can display employee behavior results and evaluations using graphs and charts. Specifically, it visually displays each employee's sales performance and behavior patterns using bar graphs, line graphs, pie charts, etc., so that they can grasp them at a glance. For example, by displaying charts that compare sales trends over a specific period or the frequency of visits to each customer and their results, managers can quickly make evaluations. Furthermore, the evaluation department displays the analysis results in a dashboard format and provides the latest information based on data that is updated in real time. The dashboard displays important indicators and KPIs (Key Performance Indicators), so managers can grasp the overall situation at a glance. The evaluation department can also automatically generate reports based on the analysis results and provide them to managers on a regular basis. The reports include detailed analysis results and suggested improvements, so managers can formulate specific instructions and strategies based on them. In this way, the evaluation department enables managers to make quick and accurate evaluations and effectively support employees' sales activities.

[0067] The system includes a data collection unit that collects past behavioral and performance data. The data collection unit can collect past behavioral data, such as employee visit history and closing history. The data collection unit can also collect performance data, such as employee sales data and closing rates. The data collection unit can automatically collect employee behavioral and performance data and provide it to the proposal unit. This allows the proposal unit to propose a more accurate sales schedule by collecting past behavioral and performance data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee behavioral data into AI and have the AI ​​perform data collection and organization.

[0068] The evaluation unit includes a display unit that visually displays the analysis results. The display unit can visually display the analysis results using, for example, graphs or charts. The display unit can display the analysis results in the form of, for example, bar graphs, pie charts, or line graphs. The display unit can also display the analysis results in a dashboard format, for example, so that administrators can understand them at a glance. This makes it easier for administrators to understand the analysis results by displaying them visually. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input the analysis results into AI and have AI perform the visual display.

[0069] The proposal department can generate an optimal schedule by considering past sales performance and customer feedback. For example, the proposal department can suggest the best destinations and times for visits based on employees' past sales data and closing rates. For example, the proposal department can also determine the priority of visits by considering customer feedback and survey results. For example, the proposal department can analyze past sales performance and customer feedback to generate an optimal schedule. This allows for the proposal of more effective sales schedules by considering past sales performance and customer feedback. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input past sales performance and customer feedback data into AI and have the AI ​​generate an optimal schedule.

[0070] The analysis department can compare and evaluate the proposed schedule with the actual actions. For example, the analysis department can compare the proposed schedule with the actual visit history of employees to evaluate how well the actions followed the schedule. For example, the analysis department can also evaluate whether the actions were ideal by comparing the results of following the proposed schedule with the results of the actual actions. For example, the analysis department can analyze the differences between the proposed schedule and the actual actions to identify areas for improvement. This allows for an accurate evaluation of actions by comparing the proposed schedule with the actual actions. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the proposed schedule and actual action data into the AI ​​and have the AI ​​perform the evaluation.

[0071] The evaluation unit can display employee behavior results and evaluations using graphs and charts. For example, the evaluation unit can display employee behavior results using bar graphs or pie charts to make them easier to understand visually. For example, the evaluation unit can display employee evaluations using line graphs to visually show changes over time. For example, the evaluation unit can display employee behavior results and evaluations in a dashboard format, allowing managers to grasp the overall situation at a glance. This makes it easier for managers to understand visually by using graphs and charts. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can input employee behavior results and evaluation data into AI and have the AI ​​generate graphs and charts.

[0072] The proposal unit can estimate the user's emotions and adjust the proposed sales schedule based on those emotions. For example, if the user is feeling stressed, the proposal unit can suggest a less burdensome schedule. If the user is relaxed, the proposal unit can suggest a challenging schedule. If the user is in a hurry, the proposal unit can suggest an efficient schedule. By adjusting the proposal based on the user's emotions, a more appropriate sales schedule can be proposed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input user emotion data into a generative AI and have the generative AI perform schedule adjustments based on emotions.

[0073] The proposal department can analyze employees' past behavioral patterns and propose the optimal order of visits. For example, the proposal department can propose the most efficient order of visits based on past visit data. For example, the proposal department can optimize the order of visits by considering past customer responses. For example, the proposal department can propose the order of visits based on past sales performance. In this way, by analyzing past behavioral patterns, it is possible to propose an efficient order of visits. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input past behavioral data into AI and have the AI ​​propose the optimal order of visits.

[0074] The proposal unit can optimize the timing of visits by considering past customer response data. For example, the proposal unit can suggest the optimal timing of visits based on past customer response data. The proposal unit can also optimize the timing of visits by analyzing past customer response data. For example, the proposal unit can suggest the timing of visits by considering past customer response data. This allows the proposal unit to suggest the optimal timing of visits by considering past customer response data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input past customer response data into AI and have the AI ​​perform the optimization of visit timing.

[0075] The proposal unit can estimate the user's emotions and determine the priority of the proposed sales schedule based on those emotions. For example, if the user is stressed, the proposal unit can postpone less important tasks. For example, if the user is relaxed, the proposal unit can prioritize more important tasks. For example, if the user is in a hurry, the proposal unit can prioritize efficient tasks. This allows for the proposal of a more appropriate schedule by prioritizing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0076] The proposal department can propose a schedule that minimizes travel time by taking into account the geographical location information of employees. For example, the proposal department can propose the optimal order of visits based on the employee's current location. The proposal department can also propose a schedule that minimizes travel time by taking into account the geographical location information of employees. For example, the proposal department can propose an efficient schedule based on the employee's geographical location information. In this way, by taking into account geographical location information, an efficient schedule that minimizes travel time can be proposed. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI. For example, the proposal department can input the employee's geographical location information into AI and have AI perform the task of minimizing travel time.

[0077] The proposal department can analyze employees' social media activity and propose a schedule that prioritizes contact with relevant customers. For example, the proposal department can propose a schedule that prioritizes contact with relevant customers based on employees' social media activity. The proposal department can also analyze employees' social media activity and propose a schedule that prioritizes contact with relevant customers. The proposal department can also propose a schedule that prioritizes contact with relevant customers, taking into account employees' social media activity. This allows the proposal department to propose a schedule that prioritizes contact with relevant customers by analyzing social media activity. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input employee social media activity data into AI and have the AI ​​propose a schedule that prioritizes contact with relevant customers.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis method of behavioral results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a rapid analysis result. This allows for more appropriate analysis results to be provided by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the analysis method based on emotions.

[0079] The analysis department can perform a detailed analysis of the correlation between employee behavior data and proposed schedules. For example, the analysis department can perform a detailed analysis of the correlation between employee behavior data and proposed schedules. The analysis department can also evaluate the effectiveness of proposed schedules based on employee behavior data. For example, the analysis department can analyze the correlation between employee behavior data and proposed schedules to identify areas for improvement. This allows for the evaluation of schedule effectiveness by analyzing the correlation between behavior data and proposed schedules. Some or all of the above-described processes in the analysis department may be performed using AI, or not. For example, the analysis department can input employee behavior data and proposed schedules into an AI and have the AI ​​perform the correlation analysis.

[0080] The analysis unit can evaluate the outcome of actions by taking customer feedback data into consideration. The analysis unit can, for example, evaluate the outcome of actions based on customer feedback data. The analysis unit can also, for example, evaluate the outcome of actions by taking customer feedback data into consideration. The analysis unit can also, for example, analyze customer feedback data and evaluate the outcome of actions. This allows for an accurate evaluation of the outcome of actions by taking customer feedback data into consideration. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input customer feedback data into AI and have AI perform the evaluation of the outcome of actions.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a concise display method. For example, if the user is relaxed, the analysis unit can provide a detailed display method. For example, if the user is in a hurry, the analysis unit can provide a rapid display method. By adjusting the display method based on the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method based on emotions.

[0082] The analysis department can compare behavioral results by region, taking into account the geographical range of employees' activities. The analysis department can, for example, compare behavioral results by region based on the geographical range of employees' activities. The analysis department can, for example, compare behavioral results by region, taking into account the geographical range of employees' activities. The analysis department can, for example, analyze the geographical range of employees' activities and compare behavioral results by region. This allows for the comparison of behavioral results by region by considering the geographical range of activities. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input employees' geographical activity data into AI and have AI perform a comparison of behavioral results by region.

[0083] The analysis department can adjust the evaluation criteria for behavioral results by referring to employees' past performance data. For example, the analysis department can adjust the evaluation criteria for behavioral results based on employees' past performance data. The analysis department can also adjust the evaluation criteria for behavioral results by referring to employees' past performance data. For example, the analysis department can analyze employees' past performance data and adjust the evaluation criteria for behavioral results. This allows for appropriate adjustment of evaluation criteria by referring to past performance data. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input employees' past performance data into AI and have the AI ​​perform the adjustment of evaluation criteria.

[0084] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is stressed, the evaluation unit can provide a concise display method. For example, if the user is relaxed, the evaluation unit can provide a detailed display method. For example, if the user is in a hurry, the evaluation unit can provide a rapid display method. By adjusting the display method based on the user's emotions, more appropriate evaluation results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method based on emotions.

[0085] The evaluation department can optimize evaluation criteria by referring to an employee's past evaluation history. The evaluation department can optimize evaluation criteria based on an employee's past evaluation history, for example. The evaluation department can also optimize evaluation criteria by referring to an employee's past evaluation history, for example. The evaluation department can also optimize evaluation criteria by analyzing an employee's past evaluation history, for example. This allows for the appropriate optimization of evaluation criteria by referring to past evaluation history. Some or all of the above processes in the evaluation department may be performed using AI, for example, or without AI. For example, the evaluation department can input an employee's past evaluation history into AI and have the AI ​​perform the optimization of evaluation criteria.

[0086] The evaluation department can customize evaluation results by taking into account the employee's skill set. For example, the evaluation department can customize evaluation results based on the employee's skill set. For example, the evaluation department can customize evaluation results by taking into account the employee's skill set. For example, the evaluation department can analyze the employee's skill set and customize the evaluation results. This allows for the provision of more appropriate evaluation results by taking skill sets into consideration. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI. For example, the evaluation department can input employee skill set data into AI and have the AI ​​perform the customization of evaluation results.

[0087] The evaluation unit can estimate the user's emotions and determine the priority of evaluation results based on the estimated user emotions. For example, if the user is stressed, the evaluation unit can postpone evaluation results of lower importance. For example, if the user is relaxed, the evaluation unit can prioritize evaluation results of higher importance. For example, if the user is in a hurry, the evaluation unit can prioritize evaluation results of efficiency. This allows for the provision of more appropriate evaluation results by determining priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0088] The evaluation department can compare evaluation results by region, taking into account the geographical range of employees' activities. The evaluation department can, for example, compare evaluation results by region based on the geographical range of employees' activities. The evaluation department can, for example, compare evaluation results by region, taking into account the geographical range of employees' activities. The evaluation department can, for example, analyze the geographical range of employees' activities and compare evaluation results by region. This allows for the comparison of evaluation results by region, taking into account the geographical range of activities. Some or all of the above processing in the evaluation department may be performed using AI, for example, or without AI. For example, the evaluation department can input employees' geographical activity data into AI and have AI perform the comparison of evaluation results by region.

[0089] The evaluation department can analyze employees' social media activities and display relevant evaluation results. For example, the evaluation department can display relevant evaluation results based on employees' social media activities. The evaluation department can also analyze employees' social media activities and display relevant evaluation results. The evaluation department can also consider employees' social media activities and display relevant evaluation results. This allows the evaluation department to provide relevant evaluation results by analyzing social media activities. Some or all of the above processing in the evaluation department may be performed using AI, for example, or without AI. For example, the evaluation department can input employee social media activity data into AI and have the AI ​​display relevant evaluation results.

[0090] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection. For example, if the user is in a hurry, the data collection unit can perform rapid data collection. This allows for more appropriate data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection based on emotions.

[0091] The data collection unit can analyze employees' past behavioral data in detail and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on employees' past behavioral data. The data collection unit can also analyze employees' past behavioral data in detail and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method by considering employees' past behavioral data. This allows for the selection of the optimal data collection method by analyzing past behavioral data in detail. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employees' past behavioral data into AI and have the AI ​​select the optimal data collection method.

[0092] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone collecting less important data. For example, if the user is relaxed, the data collection unit can prioritize more important data. For example, if the user is in a hurry, the data collection unit can prioritize efficient data. This allows for more appropriate data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority based on emotions.

[0093] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees. For example, the data collection unit can prioritize the collection of highly relevant data based on the geographical location information of employees. For example, the data collection unit can also prioritize the collection of highly relevant data by considering the geographical location information of employees. For example, the data collection unit can analyze the geographical location information of employees and prioritize the collection of highly relevant data. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of employees into AI and have AI perform the collection of highly relevant data.

[0094] The display unit can estimate the user's emotions and adjust the displayed content based on the estimated emotions. For example, if the user is stressed, the display unit can provide concise content. For example, if the user is relaxed, the display unit can provide detailed content. For example, if the user is in a hurry, the display unit can provide quick content. By adjusting the displayed content based on the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the displayed content.

[0095] The display unit can select the optimal display method by referring to the administrator's past operation history. The display unit can, for example, select the optimal display method based on the administrator's past operation history. The display unit can also, for example, refer to the administrator's past operation history to select the optimal display method. The display unit can also, for example, analyze the administrator's past operation history to select the optimal display method. This allows the display unit to select the optimal display method by referring to past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the administrator's past operation history into AI and have AI perform the selection of the optimal display method.

[0096] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is stressed, the display unit can postpone less important display content. For example, if the user is relaxed, the display unit can prioritize more important display content. For example, if the user is in a hurry, the display unit can prioritize efficient display content. This allows for more appropriate displays to be provided by determining the display priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform the determination of display priority based on emotions.

[0097] The display unit can select the optimal display method by considering the administrator's device information. For example, if the administrator is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the administrator is using a tablet, the display unit can also provide a display method optimized for a larger screen. For example, if the administrator is using a desktop, the display unit can also provide a detailed display method. In this way, the optimal display method can be provided by considering the device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the administrator's device information into the AI ​​and have the AI ​​select the optimal display method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The proposal department can suggest sales schedules that take into account the health status of employees. For example, by collecting employee health data, they can propose a schedule that prioritizes rest if fatigue is accumulating. Conversely, if an employee is in good health, they can suggest more proactive sales activities. Furthermore, for employees with specific health risks, they can propose a schedule that helps mitigate those risks. This allows them to propose the optimal sales schedule based on the health status of each employee.

[0100] The analytics department can collect employee behavioral data in real time and provide analysis results immediately. For example, by collecting behavioral data in real time while employees are conducting sales activities and performing immediate analysis, rapid feedback can be provided. Furthermore, real-time data collection allows for analysis results that can respond to sudden changes in circumstances. In addition, real-time data collection and analysis can provide advice to immediately correct employee behavior. In short, real-time data collection and analysis enable the provision of quick and appropriate feedback.

[0101] The evaluation department can conduct relative evaluations by comparing employees' actions with those of other employees. For example, it can evaluate relative performance by comparing it with the actions of other employees within the same sales team. It can also evaluate progress by comparing it with similar past sales activities. Furthermore, it can evaluate industry-wide performance by comparing it with employees in other departments or other companies. This allows for a more accurate assessment of employee performance through relative evaluation.

[0102] The proposal department can propose sales schedules based on each employee's individual goals. For example, it can propose an optimal schedule for achieving individual goals set by each employee. It can also adjust the schedule as needed, taking into account the progress towards achieving those goals. Furthermore, it can support goal achievement by optimizing resource allocation according to each employee's goals. This allows for the proposal of optimal sales schedules based on each employee's individual goals.

[0103] The analytics department can identify behavioral patterns and propose areas for improvement based on employee behavioral data. For example, it can analyze past behavioral data to identify successful and unsuccessful patterns. It can also analyze how specific behavioral patterns affect results and propose areas for improvement. Furthermore, it can provide specific advice to employees based on the analysis of behavioral patterns. In this way, it can make concrete suggestions for improving employee behavior based on the analysis of behavioral patterns.

[0104] The proposal department can estimate the user's emotions and adjust the proposed sales schedule based on those emotions. For example, if the user is feeling stressed, a less burdensome schedule can be proposed. Conversely, if the user is relaxed, a more challenging schedule can be proposed. Furthermore, if the user is in a hurry, an efficient schedule can be proposed. In this way, by adjusting the proposal based on the user's emotions, a more appropriate sales schedule can be proposed.

[0105] The analysis unit can estimate the user's emotions and adjust the analysis method of behavioral results based on the estimated user emotions. For example, if the user is stressed, it can provide a concise analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a rapid analysis result. In this way, by adjusting the analysis method based on the user's emotions, more appropriate analysis results can be provided.

[0106] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated emotions. For example, if the user is stressed, a concise display method can be provided. If the user is relaxed, a detailed display method can be provided. Furthermore, if the user is in a hurry, a quick display method can be provided. In this way, by adjusting the display method based on the user's emotions, more appropriate evaluation results can be provided.

[0107] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced. Conversely, if the user is relaxed, the frequency of data collection can be increased. Furthermore, if the user is in a hurry, rapid data collection can be performed. By adjusting the timing of data collection based on the user's emotions, more appropriate data collection becomes possible.

[0108] The display unit can estimate the user's emotions and adjust the displayed content based on those emotions. For example, if the user is stressed, it can provide concise information. If the user is relaxed, it can provide detailed information. Furthermore, if the user is in a hurry, it can provide quick and easy information. By adjusting the displayed content based on the user's emotions, a more appropriate display can be provided.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The proposal department proposes a sales schedule based on each employee's situation. The proposal department generates a sales schedule considering factors such as employees' working hours, performance, and skill sets. The proposal department collects past behavioral and performance data and proposes the optimal schedule based on it. Furthermore, it can also propose which customers should be visited and at what time of day, taking into account past sales performance and customer reactions. Step 2: The analysis department analyzes the results of employee actions based on the sales schedule proposed by the proposal department. The analysis department compares the proposed schedule with the actual actions and evaluates whether the actions were ideal. Furthermore, it evaluates the degree to which the actions were ideal by comparing the results of following the proposed schedule with the results of the actual actions. Step 3: The evaluation unit displays the analysis results obtained by the analysis unit in a format that is easy for managers to understand. The evaluation unit is equipped with a display unit that visually displays the analysis results, using graphs and charts to display employee behavior results and evaluations. This makes it possible to display the analysis results in an easy-to-understand manner, enabling managers to conduct evaluations quickly.

[0111] 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.

[0112] 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 text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] 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.

[0114] Each of the multiple elements described above, including the proposal unit, analysis unit, evaluation unit, collection unit, and display unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the smart device 14 and generates an optimal sales schedule based on employee working hours and performance data. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and evaluates the proposed schedule by comparing it with actual behavior. The evaluation unit is implemented by, for example, the control unit 46A of the smart device 14 and displays the analysis results visually. The collection unit collects employee behavior data using, for example, the camera 42 and microphone 38B of the smart device 14. The display unit is implemented by, for example, the display 40A of the smart device 14 and displays the analysis results using graphs and charts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the proposal unit, analysis unit, evaluation unit, collection unit, and display unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the smart glasses 214 and generates an optimal sales schedule based on employee working hours and performance data. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and evaluates the proposed schedule by comparing it with actual behavior. The evaluation unit is implemented by, for example, the control unit 46A of the smart glasses 214 and visually displays the analysis results. The collection unit collects employee behavior data using, for example, the camera 42 and microphone 238 of the smart glasses 214. The display unit is implemented by, for example, the display of the smart glasses 214 and displays the analysis results using graphs and charts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the proposal unit, analysis unit, evaluation unit, collection unit, and display unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the headset terminal 314 and generates an optimal sales schedule based on employee working hours and performance data. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and evaluates the proposed schedule by comparing it with actual actions. The evaluation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and visually displays the analysis results. The collection unit collects employee behavior data using, for example, the camera 42 and microphone 238 of the headset terminal 314. The display unit is implemented by, for example, the display 343 of the headset terminal 314 and displays the analysis results using graphs and charts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the proposal unit, analysis unit, evaluation unit, collection unit, and display unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the robot 414 and generates an optimal sales schedule based on employee working hours and performance data. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and evaluates the proposed schedule by comparing it with actual actions. The evaluation unit is implemented by, for example, the control unit 46A of the robot 414 and visually displays the analysis results. The collection unit collects employee behavior data using, for example, the camera 42 and microphone 238 of the robot 414. The display unit is implemented by, for example, the display of the robot 414 and displays the analysis results using graphs and charts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The proposal department proposes sales schedules based on the circumstances of each employee, The analysis department analyzes the results of employee actions based on the sales schedule proposed by the aforementioned proposal department, The system includes an evaluation unit that displays the analysis results obtained by the analysis unit in a format that is easy for the administrator to understand. A system characterized by the following features. (Note 2) It includes a data collection unit that collects past behavioral and performance data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, It is equipped with a display unit that visually displays the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, The optimal schedule is generated by considering past sales performance and customer feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Compare and evaluate the proposed schedule with the actual actions taken. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, Display employee behavior results and evaluations using graphs and charts. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the proposed sales schedule based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, We analyze employees' past behavioral patterns and propose the optimal order of visits. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, Optimize the timing of visits by considering past customer response data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of the sales schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, We propose a schedule that minimizes travel time, taking into account the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, We analyze employees' social media activity and propose schedules that prioritize contact with relevant customers. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method of behavioral results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We will conduct a detailed analysis of the correlation between employee behavioral data and proposed schedules. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is We evaluate the results of our actions by taking customer feedback data into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is We will compare behavioral results by region, taking into account the geographical range of employees' activities. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is Refer to employees' past performance data to adjust the criteria for evaluating their actions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, Optimize evaluation criteria by referring to employees' past evaluation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, Customize evaluation results by taking into account the employee's skill set. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, The system estimates the user's emotions and prioritizes evaluation results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, We will compare evaluation results by region, taking into account the geographical range of employees' activities. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, Analyze employees' social media activity and display relevant evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned collection unit is We will conduct a detailed analysis of employees' past behavioral data to select the optimal data collection method. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned collection unit is We prioritize collecting highly relevant data, taking into account employees' geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned display unit is It estimates the user's emotions and adjusts the displayed content based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned display unit is Refer to the administrator's past operation history to select the optimal display method. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned display unit is The optimal display method is selected, taking into account the administrator's device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 proposal department proposes sales schedules based on the circumstances of each employee, The analysis department analyzes the results of employee actions based on the sales schedule proposed by the aforementioned proposal department, The system includes an evaluation unit that displays the analysis results obtained by the analysis unit in a format that is easy for the administrator to understand. A system characterized by the following features.

2. It includes a data collection unit that collects past behavioral and performance data. The system according to feature 1.

3. The evaluation unit, It is equipped with a display unit that visually displays the analysis results. The system according to feature 1.

4. The aforementioned proposal section is, The optimal schedule is generated by considering past sales performance and customer feedback. The system according to feature 1.

5. The aforementioned analysis unit is Compare and evaluate the proposed schedule with the actual actions taken. The system according to feature 1.

6. The evaluation unit, Display employee behavior results and evaluations using graphs and charts. The system according to feature 1.

7. The aforementioned proposal section is, The system estimates the user's emotions and adjusts the proposed sales schedule based on those emotions. The system according to feature 1.

8. The aforementioned proposal section is, We analyze employees' past behavioral patterns and propose the optimal order of visits. The system according to feature 1.

9. The aforementioned proposal section is, Optimize the timing of visits by considering past customer response data. The system according to feature 1.

10. The aforementioned proposal section is, It estimates the user's emotions and determines the priority of the sales schedule based on those estimated emotions. The system according to feature 1.

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