System

Abstract

To provide a system according to an embodiment that grasps in real time the situation of stress in a sales person while serving customers and give appropriate advice or notification.SOLUTION: A system according to an embodiment comprises an acquisition unit, a determination unit, a providing unit, and a notification unit. The acquisition unit acquires information on a sales person while serving customers. The determination unit determines the situation of stress in the sales person on the basis of the information acquired by the acquisition unit. The providing unit provides advice to the sales person on the basis of the situation of stress determined by the determination unit. The notification unit notifies a manager of the sales person of the situation of the stress in the sales person on the basis of the situation of stress determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to grasp the stress level of salespeople while serving customers in real time and take appropriate measures.

[0005] The system according to the embodiment aims to grasp the stress level of a salesperson while serving customers in real time and provide appropriate advice and notifications. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a determination unit, a provision unit, and a notification unit. The acquisition unit acquires information about the salesperson while serving customers. The determination unit determines the stress state of the salesperson based on the information acquired by the acquisition unit. The provision unit provides advice to the salesperson based on the stress state determined by the determination unit. The notification unit notifies the salesperson's manager of the stress state of the salesperson based on the stress state determined by the determination unit. Effect of the Invention

[0007] The system according to the embodiment can grasp the stress level of a salesperson while serving customers in real time and provide appropriate advice and notifications. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A stress management system according to an embodiment of the present invention is a system that acquires data of a salesperson serving a customer, determines the stress state of the salesperson based on the data, and provides advice. This stress management system acquires information about the salesperson serving a customer, determines the stress state of the salesperson based on the information, and provides advice. For example, the stress management system acquires information such as facial expressions, voices, remarks, and attributes of the salesperson and the customer. Next, the stress management system determines the stress state of the salesperson based on the acquired information. At this time, the heart rate, electrodermal activity, tone and tempo of the voice, etc. are also analyzed. Based on the determined stress state, the stress management system suggests advice to the salesperson to relax, specific breathing techniques, and postures. The stress management system also notifies the manager of the stress state of the salesperson, and schedules a meeting as necessary. In this way, the stress management system can efficiently determine the stress state of the salesperson and provide appropriate advice and notifications.

[0029] The stress management system according to the embodiment includes an acquisition unit, a determination unit, a provision unit, and a notification unit. The acquisition unit acquires information about the salesperson serving customers. The information about the salesperson serving customers includes, for example, facial expressions, voices, remarks, attributes, etc. of the salesperson and the customer, but is not limited to such examples. The acquisition unit acquires the facial expressions and voices of the salesperson and the customer in real time, for example, using a camera or a microphone. The acquisition unit can also analyze the remarks of the salesperson and the customer using natural language processing technology and acquire them as text data. Furthermore, the acquisition unit can acquire attribute information of the salesperson and the customer from a database. For example, the acquisition unit captures the facial expressions of the salesperson and the customer in real time using a camera, and analyzes the facial expressions using face recognition technology. The voices of the salesperson and the customer are collected using a microphone, and the tone and tempo of the voice are analyzed using voice recognition technology. The remarks of the salesperson and the customer are acquired as text data using natural language processing technology, and important information is extracted using keyword extraction technology. The determination unit judges the stress state of the salesperson based on the information acquired by the acquisition unit. The determination unit analyzes data such as heart rate, skin activity, and tone and tempo of voice to calculate a stress index. The determination unit measures the heart rate of the salesperson using a heart rate sensor, and calculates a stress index using an analysis algorithm. The determination unit measures the skin activity of the salesperson using an electrodermal sensor, and calculates a stress index using an analysis algorithm. The tone and tempo of voice are analyzed to calculate a stress index. The provision unit provides advice to the salesperson based on the stress situation determined by the determination unit. The provision unit provides advice to relax to a salesperson who is experiencing high stress, for example. The provision unit suggests relaxation methods such as deep breathing, stretching, and short breaks. It is also possible to suggest specific breathing methods and postures. For example, the provision unit advises the salesperson to take deep breaths. Advises the salesperson to stretch. Advises the salesperson to take a short break. The notification unit notifies the manager of the salesperson of the stress situation of the salesperson based on the stress situation determined by the determination unit. The notification unit notifies the manager by a method such as email notification or alert display.For example, if the stress level of the salesperson is high, the notification unit schedules an emergency meeting for the manager. If the stress level is medium, the notification unit schedules a regular meeting for the manager. If the stress level is low, the notification unit schedules meetings as needed for the manager. In this way, the stress management system according to the embodiment can efficiently determine the stress level of the salesperson and provide appropriate advice and notifications.

[0030] The acquisition unit acquires information about the salesperson during customer service. The information about the salesperson during customer service includes, for example, the facial expressions, voices, remarks, attributes, etc. of the salesperson and the customer, but is not limited to such examples. The acquisition unit acquires the facial expressions and voices of the salesperson and the customer in real time, for example, using a camera and a microphone. Specifically, the camera has high resolution and uses face recognition technology to analyze the facial expressions of the salesperson and the customer in detail. This makes it possible to capture subtle changes in facial expressions and emotional movements. The microphone has high sensitivity and uses voice recognition technology to analyze the tone and tempo of the voices of the salesperson and the customer. This makes it possible to evaluate the intonation and tension of the voices. Furthermore, the acquisition unit can also analyze the remarks of the salesperson and the customer using natural language processing technology and acquire them as text data. Natural language processing technology understands the context of the remarks and extracts important keywords and phrases. This makes it possible to grasp the details of the conversation between the salesperson and the customer and identify the cause of stress and the needs of the customer. Furthermore, the acquisition unit can also acquire attribute information of the salesperson and the customer from a database. For example, the acquisition unit acquires information such as the salesperson's years of experience, past customer service history, the customer's age, gender, and purchase history, and comprehensively evaluates the situation during customer service. This allows the acquisition unit to collect information on the salesperson and the customer from multiple perspectives and provide basic data for stress management.

[0031] The determination unit determines the stress state of the salesperson based on the information acquired by the acquisition unit. The determination unit, for example, analyzes data such as heart rate, electrodermal activity, tone and tempo of voice, and calculates a stress index. Specifically, the heart rate of the salesperson is measured using a heart rate sensor, and a stress index is calculated using an analysis algorithm. The stress level can be evaluated by analyzing the fluctuation of the heart rate and the irregularity of the heart rate interval. The electrodermal activity of the salesperson is measured using an electrodermal sensor, and a stress index is calculated using an analysis algorithm. Since the electrodermal activity changes depending on stress and tension, the stress level can be evaluated by analyzing this. Furthermore, the tone and tempo of the voice are analyzed, and a stress index is calculated. Since the tone and tempo of the voice reflect emotions and tension, the stress level can be evaluated by analyzing this. The determination unit comprehensively analyzes these data and determines the stress state of the salesperson in real time. Furthermore, the determination unit can also analyze long-term stress tendencies and trends by utilizing past data and statistical information. For example, based on past stress data, the stress fluctuation in a specific time period or situation is predicted, and future measures are proposed. In addition, the judgment unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data and issue an early warning. This enables the judgment unit to handle not only real-time stress judgment, but also long-term stress management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The providing unit provides advice to the salesperson based on the stress situation determined by the determining unit. Specifically, the providing unit provides advice to relax to the salesperson who is under increasing stress. The providing unit suggests relaxation methods such as deep breathing, stretching, and short breaks. As a method of deep breathing, it is recommended to take a deep breath, hold the breath for a few seconds, and then slowly exhale. As a method of stretching, it is suggested to perform simple movements to loosen the muscles of the shoulders and neck. As a method of short breaks, it is recommended to take a break for a few minutes and spend time in a relaxing environment. Furthermore, the providing unit can also suggest specific breathing methods and postures. For example, the relaxation effect can be enhanced by adopting abdominal breathing and yoga poses. The providing unit provides these pieces of advice to the salesperson in real time and encourages them to take specific actions to reduce stress. In addition, the providing unit can collect feedback from the salesperson and evaluate the effectiveness of the advice. For example, it can monitor changes in stress levels after the advice is implemented and continuously provide effective advice. In this way, the providing unit can support the stress management of the salesperson and improve the efficiency and quality of work.

[0033] The notification unit notifies the manager of the salesperson of the stress state of the salesperson based on the stress state determined by the determination unit. Specifically, the manager is notified by a method such as an email notification or an alert display. For example, if the stress state of the salesperson is high, an emergency meeting is scheduled for the manager. If the stress state is medium, a regular meeting is scheduled for the manager. If the stress state is low, a meeting as needed is scheduled for the manager. The notification unit performs these notifications in real time, so that the manager can respond quickly. Furthermore, the notification unit can generate a detailed report on the stress state of the salesperson and provide it to the manager. The report includes fluctuations and trends in the stress level, specific stress factors, and the like. This allows the manager to comprehensively grasp the stress state of the salesperson and take appropriate measures. Furthermore, the notification unit can collect feedback from the manager and continuously improve the accuracy and effectiveness of the notification content. For example, the timing and content of the notification can be adjusted based on the feedback from the manager to perform a more effective notification. This allows the notification unit to provide the manager with prompt and accurate information and support stress management of the salesperson.

[0034] The determination unit can determine the stress index of the salesperson as a stress situation based on the information acquired by the acquisition unit. The determination unit, for example, analyzes data such as heart rate, electrodermal activity, tone and tempo of voice, and calculates a stress index. The determination unit, for example, measures the heart rate of the salesperson using a heart rate sensor, and calculates a stress index using an analysis algorithm. The determination unit measures the electrodermal activity of the salesperson using an electrodermal sensor, and calculates a stress index using an analysis algorithm. The tone and tempo of voice are analyzed, and a stress index is calculated. In this way, the stress index can be determined, and the stress situation of the salesperson can be quantified and more accurately grasped. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the data acquired by the acquisition unit to the generation AI, and cause the generation AI to calculate the stress index.

[0035] The acquisition unit can acquire information on the facial expressions, voices, and remarks of the salesperson and the customer as information during the salesperson's service. The acquisition unit acquires the facial expressions and voices of the salesperson and the customer in real time, for example, using a camera or a microphone. The acquisition unit captures the facial expressions of the salesperson and the customer in real time using a camera, and analyzes the facial expressions using face recognition technology. The acquisition unit collects the voices of the salesperson and the customer using a microphone, and analyzes the tone and tempo of the voices using voice recognition technology. The acquisition unit can also analyze the remarks of the salesperson and the customer using natural language processing technology and acquire them as text data. For example, the natural language processing technology is used to acquire the remarks of the salesperson and the customer as text data, and key information is extracted using keyword extraction technology. In this way, by acquiring the facial expressions, voices, and remarks of the salesperson and the customer, it is possible to determine the stress state based on more detailed information. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input facial expression data acquired by a camera to a generation AI and cause the generation AI to execute an analysis of the facial expressions.

[0036] The providing unit can provide specific advice for relaxing to a salesperson experiencing increasing stress. The providing unit provides, for example, advice for relaxing to a salesperson experiencing increasing stress. The providing unit suggests relaxation methods such as deep breathing, stretching, and short breaks. The providing unit can also suggest specific breathing techniques and postures. For example, the providing unit advises the salesperson to take a deep breath. Advises the salesperson to stretch. Advises the salesperson to take a short break. In this way, it is possible to reduce stress by providing an appropriate relaxation method to a salesperson experiencing increasing stress. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the stress situation of the salesperson to the generating AI and cause the generating AI to execute a suggestion of a relaxation method.

[0037] The acquisition unit can acquire attribute information of the salesperson and the customer as information during the salesperson's service. The acquisition unit, for example, acquires attribute information of the salesperson and the customer from a database. The acquisition unit acquires attribute information such as the age, sex, and work history of the salesperson and the customer from the database. The acquisition unit can also acquire attribute information of the salesperson and the customer in real time. For example, the acquisition unit acquires attribute information of the salesperson and the customer in real time and stores it in a database. By acquiring attribute information of the salesperson and the customer, it is possible to determine the stress state based on more detailed information. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input attribute information of the salesperson and the customer to the generation AI and cause the generation AI to execute analysis of the attribute information.

[0038] The determination unit can analyze the heart rate and electrodermal activity of the salesperson to determine the stress index. The determination unit, for example, measures the heart rate of the salesperson using a heart rate sensor and calculates the stress index using an analysis algorithm. The determination unit, for example, measures the electrodermal activity of the salesperson using an electrodermal sensor and calculates the stress index using an analysis algorithm. In this way, a more accurate stress index can be determined by analyzing the heart rate and electrodermal activity. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input data on the heart rate and electrodermal activity to the generation AI and cause the generation AI to calculate the stress index.

[0039] The providing unit can suggest a specific breathing method according to the stress state of the salesperson. The providing unit suggests a specific breathing method, for example, according to the stress state of the salesperson. The providing unit suggests a specific breathing method, such as abdominal breathing or deep breathing. In this way, the salesperson's stress can be reduced by suggesting a breathing method according to the stress state. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the stress state of the salesperson to the generating AI and cause the generating AI to execute the suggestion of a breathing method.

[0040] The providing unit can suggest a specific posture to be taken depending on the stress state of the salesperson. The providing unit suggests a specific posture, for example, depending on the stress state of the salesperson. The providing unit suggests a specific posture, for example, straightening the back or taking a relaxed posture. In this way, by suggesting a posture according to the stress state, it is possible to reduce the stress of the salesperson. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the stress state of the salesperson to the generating AI and cause the generating AI to execute the posture suggestion.

[0041] The determination unit can analyze the tone and tempo of the salesperson's voice to determine the stress index. The determination unit, for example, analyzes the tone and tempo of the salesperson's voice to calculate the stress index. The determination unit, for example, uses voice recognition technology to analyze the tone of the salesperson's voice to calculate the stress index. The determination unit analyzes the tempo of the salesperson's voice to calculate the stress index. In this way, a more accurate stress index can be determined by analyzing the tone and tempo of the voice. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input data of the salesperson's voice to the generation AI and cause the generation AI to analyze the tone and tempo of the voice.

[0042] The notification unit can automatically schedule a meeting for the manager based on the stress state of the salesperson. For example, when the stress state of the salesperson is high, the notification unit schedules an emergency meeting for the manager. For example, when the stress state of the salesperson is medium, the notification unit schedules a regular meeting for the manager. When the stress state of the salesperson is low, the notification unit schedules a meeting as needed for the manager. In this way, by scheduling a meeting based on the stress state, the manager can respond at an appropriate time. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the stress state of the salesperson to the generation AI and cause the generation AI to execute the schedule of the meeting.

[0043] The acquisition unit can analyze the salesperson's past customer service history and select an appropriate acquisition method. For example, the acquisition unit selects an information acquisition method for a similar situation based on a customer service method that the salesperson has been successful in the past. For example, the acquisition unit selects a different information acquisition method to avoid a customer service method that the salesperson has failed in the past. The acquisition unit selects an optimal information acquisition method for a specific time period or day of the week from the salesperson's past customer service history. In this way, the optimal information acquisition method can be selected by analyzing the past customer service history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the salesperson's past customer service history data to the generation AI and cause the generation AI to select the optimal acquisition method.

[0044] The acquisition unit can perform filtering based on the salesperson's current work situation and field of interest when acquiring information. For example, when the salesperson is busy, the acquisition unit acquires only important information and filters unnecessary information. For example, when the salesperson is interested in a specific product, the acquisition unit preferentially acquires information related to the product. When the salesperson is dealing with a specific customer, the acquisition unit preferentially acquires information related to the customer. This makes it possible to acquire only necessary information by filtering information based on the work situation and field of interest. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the salesperson's work situation and field of interest to the generation AI and cause the generation AI to execute information filtering.

[0045] When acquiring information, the acquisition unit can preferentially acquire highly relevant information taking into account the geographical location information of the salesperson. For example, when the salesperson is in a specific store, the acquisition unit preferentially acquires information related to the store. For example, when the salesperson is in a specific area, the acquisition unit preferentially acquires information related to the area. When the salesperson is on the move, the acquisition unit preferentially acquires optimal information based on the current location. In this way, highly relevant information can be preferentially acquired by taking into account the geographical location information. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the salesperson to the generation AI and cause the generation AI to acquire highly relevant information.

[0046] The acquisition unit can analyze the social media activity of the salesperson when acquiring information and acquire related information. For example, when the salesperson mentions a specific product on social media, the acquisition unit acquires information related to the product. For example, when the salesperson interacts with a specific customer on social media, the acquisition unit acquires information related to the customer. For example, when the salesperson participates in a specific event on social media, the acquisition unit acquires information related to the event. In this way, by analyzing social media activity, related information can be acquired efficiently. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the social media activity data of the salesperson to the generation AI and cause the generation AI to acquire related information.

[0047] The judgment unit can optimize the judgment algorithm by referring to the salesperson's past stress data at the time of judgment. The judgment unit judges the current stress state based on the salesperson's past stress data, for example. The judgment unit extracts a specific pattern from the salesperson's past stress data, for example, and optimizes the judgment algorithm. The judgment unit analyzes the salesperson's past stress data and predicts fluctuations in the stress index. By this means, by referring to the past stress data, the judgment algorithm is optimized, and more accurate stress judgment is possible. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit inputs the salesperson's past stress data to the generation AI, and causes the generation AI to execute optimization of the judgment algorithm.

[0048] The determination unit can apply different stress assessment methods depending on the work content of the salesperson at the time of assessment. For example, when the salesperson is serving customers, the determination unit determines stress based on the interaction with the customer. For example, when the salesperson is managing inventory, the determination unit determines stress based on the workload. When the salesperson is taking a break, the determination unit determines stress based on the relaxation level. This allows for more appropriate stress assessment by applying a stress assessment method according to the work content. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on the work content of the salesperson to the generation AI and cause the generation AI to apply the stress assessment method.

[0049] At the time of judgment, the judgment unit can analyze the fluctuation of the stress index based on the working hours of the salesperson. The judgment unit, for example, analyzes the stress index immediately after the salesperson starts working and predicts the fluctuation. The judgment unit, for example, analyzes the stress index immediately before the salesperson finishes working and predicts the fluctuation. The judgment unit analyzes the fluctuation of the stress index of the salesperson while working in real time. This enables more accurate stress judgment by analyzing the fluctuation of the stress index based on the working hours. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the working hours data of the salesperson to the generation AI and cause the generation AI to execute an analysis of the fluctuation of the stress index.

[0050] The determination unit can analyze the stress index by referring to the salesperson's related market data at the time of determination. For example, if the salesperson works in a specific market, the determination unit analyzes the stress index based on the market data of the market. For example, if the salesperson deals in a specific product, the determination unit analyzes the stress index based on the market data of the product. For example, if the salesperson targets a specific customer segment, the determination unit analyzes the stress index based on the market data of the customer segment. This allows for more accurate analysis of the stress index by referring to the related market data. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can input the salesperson's related market data to the generation AI and cause the generation AI to execute the analysis of the stress index.

[0051] The providing unit can adjust the level of detail of the advice based on the stress level of the salesperson when providing the advice. For example, when the stress level of the salesperson is high, the providing unit provides concise advice that is to the point. For example, when the stress level of the salesperson is low, the providing unit provides advice that includes detailed explanations. The providing unit adjusts the content of the advice in stages according to the stress level of the salesperson. In this way, by adjusting the level of detail of the advice based on the stress level, it is possible to provide advice that is appropriate for the salesperson. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the stress level data of the salesperson to the generating AI and cause the generating AI to adjust the level of detail of the advice.

[0052] The providing unit can apply different advice algorithms depending on the work content of the salesperson when providing advice. For example, when the salesperson is serving customers, the providing unit provides advice on customer service. For example, when the salesperson is managing inventory, the providing unit provides advice on efficient work methods. When the salesperson is taking a break, the providing unit provides advice on relaxation methods. In this way, by applying an advice algorithm according to the work content, more appropriate advice can be provided. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the work content data of the salesperson to the generating AI and cause the generating AI to apply the advice algorithm.

[0053] The providing unit can determine the priority of advice based on the working hours of the salesperson when providing advice. For example, the providing unit provides important advice with priority immediately after the salesperson starts working. For example, the providing unit provides advice on relaxation methods with priority when the salesperson is about to finish working. The providing unit provides advice to improve work efficiency with priority while the salesperson is working. In this way, by determining the priority of advice based on working hours, advice can be provided at a more appropriate time. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the working hours data of the salesperson to the generating AI and cause the generating AI to determine the priority of advice.

[0054] The providing unit can adjust the order of advice by referring to the relevant market data of the salesperson when providing advice. For example, if the salesperson works in a specific market, the providing unit adjusts the order of advice based on the market data of the market. For example, if the salesperson handles a specific product, the providing unit adjusts the order of advice based on the market data of the product. For example, if the salesperson targets a specific customer segment, the providing unit adjusts the order of advice based on the market data of the customer segment. In this way, by referring to the relevant market data, a more appropriate order of advice can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the relevant market data of the salesperson to the generating AI and cause the generating AI to adjust the order of advice.

[0055] The notification unit can optimize the notification algorithm by referring to the salesperson's past stress data at the time of notification. The notification unit performs notification according to the current stress state based on, for example, the salesperson's past stress data. The notification unit, for example, extracts a specific pattern from the salesperson's past stress data and optimizes the notification algorithm. The notification unit analyzes the salesperson's past stress data, predicts fluctuations in the stress index, and performs notification. In this way, by referring to the past stress data, the notification algorithm can be optimized and more appropriate notifications can be provided. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the salesperson's past stress data to the generation AI and cause the generation AI to optimize the notification algorithm.

[0056] The notification unit can apply different notification methods depending on the work content of the salesperson at the time of notification. For example, when the salesperson is serving customers, the notification unit issues a notification regarding customer service. For example, when the salesperson is managing inventory, the notification unit issues a notification regarding an efficient work method. When the salesperson is on a break, the notification unit issues a notification regarding a relaxation method. In this way, by applying a notification method according to the work content, it is possible to provide a more appropriate notification. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the work content data of the salesperson to the generation AI and cause the generation AI to apply the notification method.

[0057] The notification unit can adjust the timing of the notification based on the working hours of the salesperson at the time of notification. For example, the notification unit prioritizes important notifications immediately after the salesperson starts working. For example, the notification unit prioritizes notifications about relaxation methods just before the salesperson finishes working. The notification unit prioritizes notifications that improve work efficiency while the salesperson is working. In this way, by adjusting the timing of the notification based on the working hours, it is possible to provide notifications at more appropriate timing. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the working hours data of the salesperson to the generation AI and cause the generation AI to adjust the timing of the notification.

[0058] The notification unit can adjust the content of the notification by referring to the relevant market data of the salesperson at the time of notification. For example, if the salesperson works in a specific market, the notification unit adjusts the content of the notification based on the market data of the market. For example, if the salesperson deals in a specific product, the notification unit adjusts the content of the notification based on the market data of the product. For example, if the salesperson targets a specific customer segment, the notification unit adjusts the content of the notification based on the market data of the customer segment. In this way, by referring to the relevant market data, more appropriate notification content can be provided. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the relevant market data of the salesperson to the generation AI and cause the generation AI to adjust the content of the notification.

[0059] The notification unit can automatically schedule a meeting for the manager based on the stress state of the salesperson at the time of notification. For example, if the stress state of the salesperson is high, the notification unit schedules an emergency meeting for the manager. For example, if the stress state of the salesperson is medium, the notification unit schedules a regular meeting for the manager. If the stress state of the salesperson is low, the notification unit schedules a meeting as needed for the manager. In this way, by scheduling a meeting based on the stress state, the manager can respond at an appropriate time. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the stress state of the salesperson to the generation AI and cause the generation AI to execute the schedule of the meeting.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When acquiring information about the salesperson while serving customers, the acquisition unit can optimize the method of acquiring information by referring to the salesperson's past stress data. For example, in situations where the salesperson has felt high stress in the past, the frequency of acquiring information can be reduced to reduce the burden on the salesperson. Also, in situations where the salesperson has felt relaxed in the past, the frequency of acquiring information can be increased to collect detailed data. Furthermore, based on the past stress data, the acquisition unit can select the optimal information acquisition method for a specific time period or day of the week. This makes it possible to acquire information more effectively by utilizing past stress data.

[0062] When determining the stress index of a salesperson, the determination unit can analyze the fluctuation of the stress index based on the working hours of the salesperson. For example, the stress index immediately after the start of a shift can be analyzed and the fluctuation can be predicted. The stress index just before the end of a shift can also be analyzed and the fluctuation can be predicted. Furthermore, it is also possible to analyze the fluctuation of the stress index during work in real time. In this way, by analyzing the fluctuation of the stress index based on the working hours, more accurate stress determination can be made.

[0063] When acquiring information on attributes of a salesperson and a customer, the acquisition unit can preferentially acquire highly relevant information by taking into consideration the geographical location information of the salesperson. For example, when a salesperson is in a particular store, information related to the store can be preferentially acquired. Also, when a salesperson is in a particular area, it is also possible to preferentially acquire information related to the area. Furthermore, when a salesperson is on the move, it is also possible to preferentially acquire optimal information based on the current location. In this way, by taking into consideration the geographical location information, highly relevant information can be preferentially acquired.

[0064] When the judgment unit analyzes the heart rate and electrodermal activity of the salesperson to judge the stress index, the judgment unit can optimize the judgment algorithm by referring to the salesperson's past stress data. For example, the current stress state can be judged based on the past stress data. It is also possible to extract a specific pattern from the past stress data and optimize the judgment algorithm. Furthermore, it is also possible to analyze the past stress data and predict fluctuations in the stress index. In this way, by referring to the past stress data, the judgment algorithm can be optimized and more accurate stress judgment can be made.

[0065] When the determination unit determines the stress index by analyzing the tone and tempo of the salesperson's voice, the determination unit can analyze the stress index by referring to market data related to the salesperson. For example, if the salesperson works in a specific market, the stress index can be analyzed based on data from that market. Also, if the salesperson deals in a specific product, the stress index can be analyzed based on market data for that product. Furthermore, if the salesperson targets a specific customer segment, the stress index can be analyzed based on market data for that customer segment. This allows for a more accurate analysis of the stress index by referring to the related market data.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The acquisition unit acquires information about the salesperson while serving customers. The information about the salesperson while serving customers includes the facial expressions, voices, remarks, attributes, etc. of the salesperson and customer. The acquisition unit acquires the facial expressions and voices of the salesperson and customer in real time using a camera and microphone. The acquisition unit can also analyze the remarks of the salesperson and customer using natural language processing technology and acquire them as text data. Furthermore, the acquisition unit can acquire attribute information of the salesperson and customer from a database. Step 2: The determination unit determines the stress state of the salesperson based on the information acquired by the acquisition unit. The determination unit analyzes data such as heart rate, electrodermal activity, and tone and tempo of voice, and calculates a stress index. For example, the heart rate of the salesperson is measured using a heart rate sensor, and a stress index is calculated using an analysis algorithm. The electrodermal activity of the salesperson is measured using an electrodermal sensor, and a stress index is calculated using an analysis algorithm. The tone and tempo of voice are analyzed, and a stress index is calculated. Step 3: The providing unit provides advice to the salesperson based on the stress state determined by the determining unit. The providing unit provides relaxation advice to the salesperson experiencing increasing stress. For example, the providing unit suggests relaxation methods such as deep breathing, stretching, and short breaks. It is also possible to suggest specific breathing techniques and postures. Step 4: The notification unit notifies the manager of the salesperson of the stress state of the salesperson based on the stress state determined by the determination unit. The notification unit notifies the manager by a method such as email notification or alert display. For example, if the stress state of the salesperson is high, an emergency meeting is scheduled for the manager. If the stress state is medium, a regular meeting is scheduled for the manager. If the stress state is low, a meeting as needed is scheduled for the manager.

[0068] (Example 2) A stress management system according to an embodiment of the present invention is a system that acquires data of a salesperson serving a customer, determines the stress state of the salesperson based on the data, and provides advice. This stress management system acquires information about the salesperson serving a customer, determines the stress state of the salesperson based on the information, and provides advice. For example, the stress management system acquires information such as facial expressions, voices, remarks, and attributes of the salesperson and the customer. Next, the stress management system determines the stress state of the salesperson based on the acquired information. At this time, the heart rate, electrodermal activity, tone and tempo of the voice, etc. are also analyzed. Based on the determined stress state, the stress management system suggests advice to the salesperson to relax, specific breathing techniques, and postures. The stress management system also notifies the manager of the stress state of the salesperson, and schedules a meeting as necessary. In this way, the stress management system can efficiently determine the stress state of the salesperson and provide appropriate advice and notifications.

[0069] The stress management system according to the embodiment includes an acquisition unit, a determination unit, a provision unit, and a notification unit. The acquisition unit acquires information about the salesperson serving customers. The information about the salesperson serving customers includes, for example, facial expressions, voices, remarks, attributes, etc. of the salesperson and the customer, but is not limited to such examples. The acquisition unit acquires the facial expressions and voices of the salesperson and the customer in real time, for example, using a camera or a microphone. The acquisition unit can also analyze the remarks of the salesperson and the customer using natural language processing technology and acquire them as text data. Furthermore, the acquisition unit can acquire attribute information of the salesperson and the customer from a database. For example, the acquisition unit captures the facial expressions of the salesperson and the customer in real time using a camera, and analyzes the facial expressions using face recognition technology. The voices of the salesperson and the customer are collected using a microphone, and the tone and tempo of the voice are analyzed using voice recognition technology. The remarks of the salesperson and the customer are acquired as text data using natural language processing technology, and important information is extracted using keyword extraction technology. The determination unit judges the stress state of the salesperson based on the information acquired by the acquisition unit. The determination unit analyzes data such as heart rate, skin activity, and tone and tempo of voice to calculate a stress index. The determination unit measures the heart rate of the salesperson using a heart rate sensor, and calculates a stress index using an analysis algorithm. The determination unit measures the skin activity of the salesperson using an electrodermal sensor, and calculates a stress index using an analysis algorithm. The tone and tempo of voice are analyzed to calculate a stress index. The provision unit provides advice to the salesperson based on the stress situation determined by the determination unit. The provision unit provides advice to relax to a salesperson who is experiencing high stress, for example. The provision unit suggests relaxation methods such as deep breathing, stretching, and short breaks. It is also possible to suggest specific breathing methods and postures. For example, the provision unit advises the salesperson to take deep breaths. Advises the salesperson to stretch. Advises the salesperson to take a short break. The notification unit notifies the manager of the salesperson of the stress situation of the salesperson based on the stress situation determined by the determination unit. The notification unit notifies the manager by a method such as email notification or alert display.For example, if the stress level of the salesperson is high, the notification unit schedules an emergency meeting for the manager. If the stress level is medium, the notification unit schedules a regular meeting for the manager. If the stress level is low, the notification unit schedules meetings as needed for the manager. In this way, the stress management system according to the embodiment can efficiently determine the stress level of the salesperson and provide appropriate advice and notifications.

[0070] The acquisition unit acquires information about the salesperson during customer service. The information about the salesperson during customer service includes, for example, the facial expressions, voices, remarks, attributes, etc. of the salesperson and the customer, but is not limited to such examples. The acquisition unit acquires the facial expressions and voices of the salesperson and the customer in real time, for example, using a camera and a microphone. Specifically, the camera has high resolution and uses face recognition technology to analyze the facial expressions of the salesperson and the customer in detail. This makes it possible to capture subtle changes in facial expressions and emotional movements. The microphone has high sensitivity and uses voice recognition technology to analyze the tone and tempo of the voices of the salesperson and the customer. This makes it possible to evaluate the intonation and tension of the voices. Furthermore, the acquisition unit can also analyze the remarks of the salesperson and the customer using natural language processing technology and acquire them as text data. Natural language processing technology understands the context of the remarks and extracts important keywords and phrases. This makes it possible to grasp the details of the conversation between the salesperson and the customer and identify the cause of stress and the needs of the customer. Furthermore, the acquisition unit can also acquire attribute information of the salesperson and the customer from a database. For example, the acquisition unit acquires information such as the salesperson's years of experience, past customer service history, the customer's age, gender, and purchase history, and comprehensively evaluates the situation during customer service. This allows the acquisition unit to collect information on the salesperson and the customer from multiple perspectives and provide basic data for stress management.

[0071] The determination unit determines the stress state of the salesperson based on the information acquired by the acquisition unit. The determination unit, for example, analyzes data such as heart rate, electrodermal activity, tone and tempo of voice, and calculates a stress index. Specifically, the heart rate of the salesperson is measured using a heart rate sensor, and a stress index is calculated using an analysis algorithm. The stress level can be evaluated by analyzing the fluctuation of the heart rate and the irregularity of the heart rate interval. The electrodermal activity of the salesperson is measured using an electrodermal sensor, and a stress index is calculated using an analysis algorithm. Since the electrodermal activity changes depending on stress and tension, the stress level can be evaluated by analyzing this. Furthermore, the tone and tempo of the voice are analyzed, and a stress index is calculated. Since the tone and tempo of the voice reflect emotions and tension, the stress level can be evaluated by analyzing this. The determination unit comprehensively analyzes these data and determines the stress state of the salesperson in real time. Furthermore, the determination unit can also analyze long-term stress tendencies and trends by utilizing past data and statistical information. For example, based on past stress data, the stress fluctuation in a specific time period or situation is predicted, and future measures are proposed. In addition, the judgment unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data and issue an early warning. This enables the judgment unit to handle not only real-time stress judgment, but also long-term stress management and anomaly detection, improving the reliability and safety of the entire system.

[0072] The providing unit provides advice to the salesperson based on the stress situation determined by the determining unit. Specifically, the providing unit provides advice to relax to the salesperson who is under increasing stress. The providing unit suggests relaxation methods such as deep breathing, stretching, and short breaks. As a method of deep breathing, it is recommended to take a deep breath, hold the breath for a few seconds, and then slowly exhale. As a method of stretching, it is suggested to perform simple movements to loosen the muscles of the shoulders and neck. As a method of short breaks, it is recommended to take a break for a few minutes and spend time in a relaxing environment. Furthermore, the providing unit can also suggest specific breathing methods and postures. For example, the relaxation effect can be enhanced by adopting abdominal breathing and yoga poses. The providing unit provides these pieces of advice to the salesperson in real time and encourages them to take specific actions to reduce stress. In addition, the providing unit can collect feedback from the salesperson and evaluate the effectiveness of the advice. For example, it can monitor changes in stress levels after the advice is implemented and continuously provide effective advice. In this way, the providing unit can support the stress management of the salesperson and improve the efficiency and quality of work.

[0073] The notification unit notifies the manager of the salesperson of the stress state of the salesperson based on the stress state determined by the determination unit. Specifically, the manager is notified by a method such as an email notification or an alert display. For example, if the stress state of the salesperson is high, an emergency meeting is scheduled for the manager. If the stress state is medium, a regular meeting is scheduled for the manager. If the stress state is low, a meeting as needed is scheduled for the manager. The notification unit performs these notifications in real time, so that the manager can respond quickly. Furthermore, the notification unit can generate a detailed report on the stress state of the salesperson and provide it to the manager. The report includes fluctuations and trends in the stress level, specific stress factors, and the like. This allows the manager to comprehensively grasp the stress state of the salesperson and take appropriate measures. Furthermore, the notification unit can collect feedback from the manager and continuously improve the accuracy and effectiveness of the notification content. For example, the timing and content of the notification can be adjusted based on the feedback from the manager to perform a more effective notification. This allows the notification unit to provide the manager with prompt and accurate information and support stress management of the salesperson.

[0074] The determination unit can determine the stress index of the salesperson as a stress situation based on the information acquired by the acquisition unit. The determination unit, for example, analyzes data such as heart rate, electrodermal activity, tone and tempo of voice, and calculates a stress index. The determination unit, for example, measures the heart rate of the salesperson using a heart rate sensor, and calculates a stress index using an analysis algorithm. The determination unit measures the electrodermal activity of the salesperson using an electrodermal sensor, and calculates a stress index using an analysis algorithm. The tone and tempo of voice are analyzed, and a stress index is calculated. In this way, the stress index can be determined, and the stress situation of the salesperson can be quantified and more accurately grasped. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the data acquired by the acquisition unit to the generation AI, and cause the generation AI to calculate the stress index.

[0075] The acquisition unit can acquire information on the facial expressions, voices, and remarks of the salesperson and the customer as information during the salesperson's service. The acquisition unit acquires the facial expressions and voices of the salesperson and the customer in real time, for example, using a camera or a microphone. The acquisition unit captures the facial expressions of the salesperson and the customer in real time using a camera, and analyzes the facial expressions using face recognition technology. The acquisition unit collects the voices of the salesperson and the customer using a microphone, and analyzes the tone and tempo of the voices using voice recognition technology. The acquisition unit can also analyze the remarks of the salesperson and the customer using natural language processing technology and acquire them as text data. For example, the natural language processing technology is used to acquire the remarks of the salesperson and the customer as text data, and key information is extracted using keyword extraction technology. In this way, by acquiring the facial expressions, voices, and remarks of the salesperson and the customer, it is possible to determine the stress state based on more detailed information. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input facial expression data acquired by a camera to a generation AI and cause the generation AI to execute an analysis of the facial expressions.

[0076] The providing unit can provide specific advice for relaxing to a salesperson experiencing increasing stress. The providing unit provides, for example, advice for relaxing to a salesperson experiencing increasing stress. The providing unit suggests relaxation methods such as deep breathing, stretching, and short breaks. The providing unit can also suggest specific breathing techniques and postures. For example, the providing unit advises the salesperson to take a deep breath. Advises the salesperson to stretch. Advises the salesperson to take a short break. In this way, it is possible to reduce stress by providing an appropriate relaxation method to a salesperson experiencing increasing stress. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the stress situation of the salesperson to the generating AI and cause the generating AI to execute a suggestion of a relaxation method.

[0077] The acquisition unit can acquire attribute information of the salesperson and the customer as information during the salesperson's service. The acquisition unit, for example, acquires attribute information of the salesperson and the customer from a database. The acquisition unit acquires attribute information such as the age, sex, and work history of the salesperson and the customer from the database. The acquisition unit can also acquire attribute information of the salesperson and the customer in real time. For example, the acquisition unit acquires attribute information of the salesperson and the customer in real time and stores it in a database. By acquiring attribute information of the salesperson and the customer, it is possible to determine the stress state based on more detailed information. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input attribute information of the salesperson and the customer to the generation AI and cause the generation AI to execute analysis of the attribute information.

[0078] The determination unit can analyze the heart rate and electrodermal activity of the salesperson to determine the stress index. The determination unit, for example, measures the heart rate of the salesperson using a heart rate sensor and calculates the stress index using an analysis algorithm. The determination unit, for example, measures the electrodermal activity of the salesperson using an electrodermal sensor and calculates the stress index using an analysis algorithm. In this way, a more accurate stress index can be determined by analyzing the heart rate and electrodermal activity. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input data on the heart rate and electrodermal activity to the generation AI and cause the generation AI to calculate the stress index.

[0079] The providing unit can suggest a specific breathing method according to the stress state of the salesperson. The providing unit suggests a specific breathing method, for example, according to the stress state of the salesperson. The providing unit suggests a specific breathing method, such as abdominal breathing or deep breathing. In this way, the salesperson's stress can be reduced by suggesting a breathing method according to the stress state. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the stress state of the salesperson to the generating AI and cause the generating AI to execute the suggestion of a breathing method.

[0080] The providing unit can suggest a specific posture to be taken depending on the stress state of the salesperson. The providing unit suggests a specific posture, for example, depending on the stress state of the salesperson. The providing unit suggests a specific posture, for example, straightening the back or taking a relaxed posture. In this way, by suggesting a posture according to the stress state, it is possible to reduce the stress of the salesperson. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the stress state of the salesperson to the generating AI and cause the generating AI to execute the posture suggestion.

[0081] The determination unit can analyze the tone and tempo of the salesperson's voice to determine the stress index. The determination unit, for example, analyzes the tone and tempo of the salesperson's voice to calculate the stress index. The determination unit, for example, uses voice recognition technology to analyze the tone of the salesperson's voice to calculate the stress index. The determination unit analyzes the tempo of the salesperson's voice to calculate the stress index. In this way, a more accurate stress index can be determined by analyzing the tone and tempo of the voice. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input data of the salesperson's voice to the generation AI and cause the generation AI to analyze the tone and tempo of the voice.

[0082] The notification unit can automatically schedule a meeting for the manager based on the stress state of the salesperson. For example, when the stress state of the salesperson is high, the notification unit schedules an emergency meeting for the manager. For example, when the stress state of the salesperson is medium, the notification unit schedules a regular meeting for the manager. When the stress state of the salesperson is low, the notification unit schedules a meeting as needed for the manager. In this way, by scheduling a meeting based on the stress state, the manager can respond at an appropriate time. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the stress state of the salesperson to the generation AI and cause the generation AI to execute the schedule of the meeting.

[0083] The acquisition unit can estimate the emotion of the salesperson and adjust the timing of acquiring information based on the estimated emotion. For example, when the salesperson is stressed, the acquisition unit reduces the frequency of acquiring information to reduce the burden. For example, when the salesperson is relaxed, the acquisition unit increases the frequency of acquiring information and collects detailed data. When the salesperson is in a hurry, the acquisition unit shortens the timing of acquiring information and quickly collects data. This makes it possible to reduce the burden on the salesperson by adjusting the timing of acquiring information based on the emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input emotion data of the salesperson to the generation AI and cause the generation AI to adjust the timing of acquiring information.

[0084] The acquisition unit can analyze the salesperson's past customer service history and select an appropriate acquisition method. For example, the acquisition unit selects an information acquisition method for a similar situation based on a customer service method that the salesperson has been successful in the past. For example, the acquisition unit selects a different information acquisition method to avoid a customer service method that the salesperson has failed in the past. The acquisition unit selects an optimal information acquisition method for a specific time period or day of the week from the salesperson's past customer service history. In this way, the optimal information acquisition method can be selected by analyzing the past customer service history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the salesperson's past customer service history data to the generation AI and cause the generation AI to select the optimal acquisition method.

[0085] The acquisition unit can perform filtering based on the salesperson's current work situation and field of interest when acquiring information. For example, when the salesperson is busy, the acquisition unit acquires only important information and filters unnecessary information. For example, when the salesperson is interested in a specific product, the acquisition unit preferentially acquires information related to the product. When the salesperson is dealing with a specific customer, the acquisition unit preferentially acquires information related to the customer. This makes it possible to acquire only necessary information by filtering information based on the work situation and field of interest. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the salesperson's work situation and field of interest to the generation AI and cause the generation AI to execute information filtering.

[0086] The acquisition unit can estimate the emotion of the salesperson and determine the priority of information to be acquired based on the estimated emotion. For example, when the salesperson is stressed, the acquisition unit preferentially acquires only important information. For example, when the salesperson is relaxed, the acquisition unit preferentially acquires detailed information. When the salesperson is in a hurry, the acquisition unit preferentially acquires information that can be acquired quickly. In this way, important information can be preferentially acquired by determining the priority of information based on emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input emotion data of the salesperson to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0087] When acquiring information, the acquisition unit can preferentially acquire highly relevant information taking into account the geographical location information of the salesperson. For example, when the salesperson is in a specific store, the acquisition unit preferentially acquires information related to the store. For example, when the salesperson is in a specific area, the acquisition unit preferentially acquires information related to the area. When the salesperson is on the move, the acquisition unit preferentially acquires optimal information based on the current location. In this way, highly relevant information can be preferentially acquired by taking into account the geographical location information. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the salesperson to the generation AI and cause the generation AI to acquire highly relevant information.

[0088] The acquisition unit can analyze the social media activity of the salesperson when acquiring information and acquire related information. For example, when the salesperson mentions a specific product on social media, the acquisition unit acquires information related to the product. For example, when the salesperson interacts with a specific customer on social media, the acquisition unit acquires information related to the customer. For example, when the salesperson participates in a specific event on social media, the acquisition unit acquires information related to the event. In this way, by analyzing social media activity, related information can be acquired efficiently. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the social media activity data of the salesperson to the generation AI and cause the generation AI to acquire related information.

[0089] The determination unit can estimate the emotion of the salesperson and adjust the judgment criteria of the stress index based on the estimated emotion. For example, when the salesperson is relaxed, the determination unit relaxes the judgment criteria of the stress index. For example, when the salesperson is stressed, the determination unit tightens the judgment criteria of the stress index. When the salesperson is in a hurry, the determination unit quickly adjusts the judgment criteria of the stress index. This allows for more accurate stress judgment by adjusting the judgment criteria of the stress index based on the emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the emotion data of the salesperson to the generation AI and cause the generation AI to adjust the judgment criteria of the stress index.

[0090] The judgment unit can optimize the judgment algorithm by referring to the salesperson's past stress data at the time of judgment. The judgment unit judges the current stress state based on the salesperson's past stress data, for example. The judgment unit extracts a specific pattern from the salesperson's past stress data, for example, and optimizes the judgment algorithm. The judgment unit analyzes the salesperson's past stress data and predicts fluctuations in the stress index. By this means, by referring to the past stress data, the judgment algorithm is optimized, and more accurate stress judgment is possible. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit inputs the salesperson's past stress data to the generation AI, and causes the generation AI to execute optimization of the judgment algorithm.

[0091] The determination unit can apply different stress assessment methods depending on the work content of the salesperson at the time of assessment. For example, when the salesperson is serving customers, the determination unit determines stress based on the interaction with the customer. For example, when the salesperson is managing inventory, the determination unit determines stress based on the workload. When the salesperson is taking a break, the determination unit determines stress based on the relaxation level. This allows for more appropriate stress assessment by applying a stress assessment method according to the work content. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on the work content of the salesperson to the generation AI and cause the generation AI to apply the stress assessment method.

[0092] The determination unit can estimate the emotion of the salesperson and adjust the display method of the stress index based on the estimated emotion. For example, when the salesperson is nervous, the determination unit provides a simple and highly visible display method. For example, when the salesperson is relaxed, the determination unit provides a display method including detailed information. When the salesperson is in a hurry, the determination unit provides a display method that focuses on the main points. This allows the display method to be adjusted based on the emotion, thereby making it possible to display a stress index that is easy for the salesperson to see. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input emotion data of the salesperson to the generation AI and cause the generation AI to adjust the display method of the stress index.

[0093] At the time of judgment, the judgment unit can analyze the fluctuation of the stress index based on the working hours of the salesperson. The judgment unit, for example, analyzes the stress index immediately after the salesperson starts working and predicts the fluctuation. The judgment unit, for example, analyzes the stress index immediately before the salesperson finishes working and predicts the fluctuation. The judgment unit analyzes the fluctuation of the stress index of the salesperson while working in real time. This enables more accurate stress judgment by analyzing the fluctuation of the stress index based on the working hours. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the working hours data of the salesperson to the generation AI and cause the generation AI to execute an analysis of the fluctuation of the stress index.

[0094] The determination unit can analyze the stress index by referring to the salesperson's related market data at the time of determination. For example, if the salesperson works in a specific market, the determination unit analyzes the stress index based on the market data of the market. For example, if the salesperson deals in a specific product, the determination unit analyzes the stress index based on the market data of the product. For example, if the salesperson targets a specific customer segment, the determination unit analyzes the stress index based on the market data of the customer segment. This allows for more accurate analysis of the stress index by referring to the related market data. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can input the salesperson's related market data to the generation AI and cause the generation AI to execute the analysis of the stress index.

[0095] The providing unit can estimate the emotion of the salesperson and adjust the way of expressing the advice based on the estimated emotion. For example, when the salesperson is nervous, the providing unit provides the advice in a calm expression. For example, when the salesperson is relaxed, the providing unit provides the advice in a bright expression. When the salesperson is in a hurry, the providing unit provides the advice in a concise and quick expression. In this way, by adjusting the way of expressing the advice based on the emotion, it is possible to provide advice that is easy for the salesperson to accept. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the emotion data of the salesperson to the generation AI and cause the generation AI to adjust the way of expressing the advice.

[0096] The providing unit can adjust the level of detail of the advice based on the stress level of the salesperson when providing the advice. For example, when the stress level of the salesperson is high, the providing unit provides concise advice that is to the point. For example, when the stress level of the salesperson is low, the providing unit provides advice that includes detailed explanations. The providing unit adjusts the content of the advice in stages according to the stress level of the salesperson. In this way, by adjusting the level of detail of the advice based on the stress level, it is possible to provide advice that is appropriate for the salesperson. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the stress level data of the salesperson to the generating AI and cause the generating AI to adjust the level of detail of the advice.

[0097] The providing unit can apply different advice algorithms depending on the work content of the salesperson when providing advice. For example, when the salesperson is serving customers, the providing unit provides advice on customer service. For example, when the salesperson is managing inventory, the providing unit provides advice on efficient work methods. When the salesperson is taking a break, the providing unit provides advice on relaxation methods. In this way, by applying an advice algorithm according to the work content, more appropriate advice can be provided. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the work content data of the salesperson to the generating AI and cause the generating AI to apply the advice algorithm.

[0098] The providing unit can estimate the emotion of the salesperson and adjust the length of the advice based on the estimated emotion. For example, when the salesperson is nervous, the providing unit provides short advice that is to the point. For example, when the salesperson is relaxed, the providing unit provides longer advice that includes detailed explanations. When the salesperson is in a hurry, the providing unit provides quick and concise advice. In this way, by adjusting the length of the advice based on the emotion, it is possible to provide advice that is easy for the salesperson to accept. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the emotion data of the salesperson to the generation AI and cause the generation AI to adjust the length of the advice.

[0099] The providing unit can determine the priority of advice based on the working hours of the salesperson when providing advice. For example, the providing unit provides important advice with priority immediately after the salesperson starts working. For example, the providing unit provides advice on relaxation methods with priority when the salesperson is about to finish working. The providing unit provides advice to improve work efficiency with priority while the salesperson is working. In this way, by determining the priority of advice based on working hours, advice can be provided at a more appropriate time. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the working hours data of the salesperson to the generating AI and cause the generating AI to determine the priority of advice.

[0100] The providing unit can adjust the order of advice by referring to the relevant market data of the salesperson when providing advice. For example, if the salesperson works in a specific market, the providing unit adjusts the order of advice based on the market data of the market. For example, if the salesperson handles a specific product, the providing unit adjusts the order of advice based on the market data of the product. For example, if the salesperson targets a specific customer segment, the providing unit adjusts the order of advice based on the market data of the customer segment. In this way, by referring to the relevant market data, a more appropriate order of advice can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the relevant market data of the salesperson to the generating AI and cause the generating AI to adjust the order of advice.

[0101] The notification unit can estimate the emotion of the salesperson and adjust the content of the notification based on the estimated emotion. For example, if the salesperson is nervous, the notification unit issues a notification in a calm expression. For example, if the salesperson is relaxed, the notification unit issues a notification in a cheerful expression. If the salesperson is in a hurry, the notification unit issues a concise and quick notification. In this way, by adjusting the content of the notification based on the emotion, it is possible to provide a notification that is easy for the salesperson to accept. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the emotion data of the salesperson to the generation AI and cause the generation AI to adjust the content of the notification.

[0102] The notification unit can optimize the notification algorithm by referring to the salesperson's past stress data at the time of notification. The notification unit performs notification according to the current stress state based on, for example, the salesperson's past stress data. The notification unit, for example, extracts a specific pattern from the salesperson's past stress data and optimizes the notification algorithm. The notification unit analyzes the salesperson's past stress data, predicts fluctuations in the stress index, and performs notification. In this way, by referring to the past stress data, the notification algorithm can be optimized and more appropriate notifications can be provided. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the salesperson's past stress data to the generation AI and cause the generation AI to optimize the notification algorithm.

[0103] The notification unit can apply different notification methods depending on the work content of the salesperson at the time of notification. For example, when the salesperson is serving customers, the notification unit issues a notification regarding customer service. For example, when the salesperson is managing inventory, the notification unit issues a notification regarding an efficient work method. When the salesperson is on a break, the notification unit issues a notification regarding a relaxation method. In this way, by applying a notification method according to the work content, it is possible to provide a more appropriate notification. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the work content data of the salesperson to the generation AI and cause the generation AI to apply the notification method.

[0104] The notification unit can estimate the emotion of the salesperson and determine the priority of notifications based on the estimated emotion. For example, when the salesperson is nervous, the notification unit prioritizes important notifications. For example, when the salesperson is relaxed, the notification unit prioritizes detailed notifications. When the salesperson is in a hurry, the notification unit prioritizes notifications that require a quick response. In this way, important notifications can be provided preferentially by determining the priority of notifications based on emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input emotion data of the salesperson to the generation AI and cause the generation AI to determine the priority of notifications.

[0105] The notification unit can adjust the timing of the notification based on the working hours of the salesperson at the time of notification. For example, the notification unit prioritizes important notifications immediately after the salesperson starts working. For example, the notification unit prioritizes notifications about relaxation methods just before the salesperson finishes working. The notification unit prioritizes notifications that improve work efficiency while the salesperson is working. In this way, by adjusting the timing of the notification based on the working hours, it is possible to provide notifications at more appropriate timing. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the working hours data of the salesperson to the generation AI and cause the generation AI to adjust the timing of the notification.

[0106] The notification unit can adjust the content of the notification by referring to the relevant market data of the salesperson at the time of notification. For example, if the salesperson works in a specific market, the notification unit adjusts the content of the notification based on the market data of the market. For example, if the salesperson deals in a specific product, the notification unit adjusts the content of the notification based on the market data of the product. For example, if the salesperson targets a specific customer segment, the notification unit adjusts the content of the notification based on the market data of the customer segment. In this way, by referring to the relevant market data, more appropriate notification content can be provided. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the relevant market data of the salesperson to the generation AI and cause the generation AI to adjust the content of the notification.

[0107] The notification unit can automatically schedule a meeting for the manager based on the stress state of the salesperson at the time of notification. For example, if the stress state of the salesperson is high, the notification unit schedules an emergency meeting for the manager. For example, if the stress state of the salesperson is medium, the notification unit schedules a regular meeting for the manager. If the stress state of the salesperson is low, the notification unit schedules a meeting as needed for the manager. In this way, by scheduling a meeting based on the stress state, the manager can respond at an appropriate time. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the stress state of the salesperson to the generation AI and cause the generation AI to execute the schedule of the meeting.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] When acquiring information about the salesperson while serving customers, the acquisition unit can optimize the method of acquiring information by referring to the salesperson's past stress data. For example, in situations where the salesperson has felt high stress in the past, the frequency of acquiring information can be reduced to reduce the burden on the salesperson. Also, in situations where the salesperson has felt relaxed in the past, the frequency of acquiring information can be increased to collect detailed data. Furthermore, based on the past stress data, the acquisition unit can select the optimal information acquisition method for a specific time period or day of the week. This makes it possible to acquire information more effectively by utilizing past stress data.

[0110] When determining the stress index of a salesperson, the determination unit can analyze the fluctuation of the stress index based on the working hours of the salesperson. For example, the stress index immediately after the start of a shift can be analyzed and the fluctuation can be predicted. The stress index just before the end of a shift can also be analyzed and the fluctuation can be predicted. Furthermore, it is also possible to analyze the fluctuation of the stress index during work in real time. In this way, by analyzing the fluctuation of the stress index based on the working hours, more accurate stress determination can be made.

[0111] When acquiring information on the facial expressions, voices, and remarks of the salesperson and the customer, the acquisition unit can estimate the emotions of the salesperson and adjust the timing of acquiring information based on the estimated emotions. For example, if the salesperson is feeling stressed, the frequency of acquiring information can be reduced to reduce the burden. Also, if the salesperson is relaxed, the frequency of acquiring information can be increased to collect detailed data. Furthermore, if the salesperson is in a hurry, it is also possible to shorten the timing of acquiring information and collect data quickly. In this way, the burden on the salesperson can be reduced by adjusting the timing of acquiring information based on emotions.

[0112] When providing specific advice to relax to a salesperson experiencing increasing stress, the providing unit can estimate the salesperson's emotions and adjust the way the advice is expressed based on the estimated emotions. For example, if the salesperson is nervous, the advice can be provided in a calm manner. Also, if the salesperson is relaxed, the advice can be provided in a cheerful manner. Furthermore, if the salesperson is in a hurry, the advice can be provided in a concise and quick manner. In this way, by adjusting the way the advice is expressed based on emotions, advice that is easy for the salesperson to accept can be provided.

[0113] When acquiring information on attributes of a salesperson and a customer, the acquisition unit can preferentially acquire highly relevant information by taking into consideration the geographical location information of the salesperson. For example, when a salesperson is in a particular store, information related to the store can be preferentially acquired. Also, when a salesperson is in a particular area, it is also possible to preferentially acquire information related to the area. Furthermore, when a salesperson is on the move, it is also possible to preferentially acquire optimal information based on the current location. In this way, by taking into consideration the geographical location information, highly relevant information can be preferentially acquired.

[0114] When the judgment unit analyzes the heart rate and electrodermal activity of the salesperson to judge the stress index, the judgment unit can optimize the judgment algorithm by referring to the salesperson's past stress data. For example, the current stress state can be judged based on the past stress data. It is also possible to extract a specific pattern from the past stress data and optimize the judgment algorithm. Furthermore, it is also possible to analyze the past stress data and predict fluctuations in the stress index. In this way, by referring to the past stress data, the judgment algorithm can be optimized and more accurate stress judgment can be made.

[0115] When proposing a specific breathing method according to the stress level of the salesperson, the providing unit can estimate the emotions of the salesperson and adjust the proposed breathing method based on the estimated emotions. For example, if the salesperson is nervous, a breathing method that has a high relaxation effect can be proposed. Also, if the salesperson is relaxed, it is possible to propose a breathing method that improves concentration. Furthermore, if the salesperson is in a hurry, it is possible to propose a breathing method that is effective in a short time. In this way, by adjusting the proposed breathing method based on emotions, it is possible to provide the optimal breathing method for the salesperson.

[0116] When suggesting a specific posture according to the stress level of the salesperson, the providing unit can estimate the emotions of the salesperson and adjust the proposed posture content based on the estimated emotions. For example, if the salesperson is nervous, a posture that has a high relaxing effect can be suggested. Also, if the salesperson is relaxed, it is possible to suggest a posture that enhances concentration. Furthermore, if the salesperson is in a hurry, it is possible to suggest a posture that will be effective in a short time. In this way, by adjusting the proposed posture content based on emotions, it is possible to provide the optimal posture for the salesperson.

[0117] When the determination unit determines the stress index by analyzing the tone and tempo of the salesperson's voice, the determination unit can analyze the stress index by referring to market data related to the salesperson. For example, if the salesperson works in a specific market, the stress index can be analyzed based on data from that market. Also, if the salesperson deals in a specific product, the stress index can be analyzed based on market data for that product. Furthermore, if the salesperson targets a specific customer segment, the stress index can be analyzed based on market data for that customer segment. This allows for a more accurate analysis of the stress index by referring to the related market data.

[0118] When automatically scheduling a meeting for a manager based on the stress state of the salesperson, the notification unit can estimate the emotions of the salesperson and adjust the content of the meeting based on the estimated emotions. For example, if the salesperson is nervous, the content of the meeting can be notified using calm expressions. Also, if the salesperson is relaxed, it is possible to notify the content of the meeting using cheerful expressions. Furthermore, if the salesperson is in a hurry, it is possible to notify the content of the meeting using concise and quick expressions. In this way, by adjusting the content of the meeting based on emotions, it is possible to provide the content of the meeting that is easy for the salesperson to accept.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The acquisition unit acquires information about the salesperson while serving customers. The information about the salesperson while serving customers includes the facial expressions, voices, remarks, attributes, etc. of the salesperson and customer. The acquisition unit acquires the facial expressions and voices of the salesperson and customer in real time using a camera and microphone. The acquisition unit can also analyze the remarks of the salesperson and customer using natural language processing technology and acquire them as text data. Furthermore, the acquisition unit can acquire attribute information of the salesperson and customer from a database. Step 2: The determination unit determines the stress state of the salesperson based on the information acquired by the acquisition unit. The determination unit analyzes data such as heart rate, electrodermal activity, and tone and tempo of voice, and calculates a stress index. For example, the heart rate of the salesperson is measured using a heart rate sensor, and a stress index is calculated using an analysis algorithm. The electrodermal activity of the salesperson is measured using an electrodermal sensor, and a stress index is calculated using an analysis algorithm. The tone and tempo of voice are analyzed, and a stress index is calculated. Step 3: The providing unit provides advice to the salesperson based on the stress state determined by the determining unit. The providing unit provides relaxation advice to the salesperson experiencing increasing stress. For example, the providing unit suggests relaxation methods such as deep breathing, stretching, and short breaks. It is also possible to suggest specific breathing techniques and postures. Step 4: The notification unit notifies the manager of the salesperson of the stress state of the salesperson based on the stress state determined by the determination unit. The notification unit notifies the manager by a method such as email notification or alert display. For example, if the stress state of the salesperson is high, an emergency meeting is scheduled for the manager. If the stress state is medium, a regular meeting is scheduled for the manager. If the stress state is low, a meeting as needed is scheduled for the manager.

[0121] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] For example, the acquisition unit acquires the facial expressions and voices of the salesperson and the customer in real time using the camera 42 and microphone 38B of the smart device 14. For example, the determination unit determines the stress state of the salesperson based on the acquired information by the specific processing unit 290 of the data processing device 12. For example, the provision unit provides the salesperson with advice to relax based on the determined stress state by the control unit 46A of the smart device 14. For example, the notification unit notifies the salesperson's manager of the stress state of the salesperson by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0132] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] For example, the acquisition unit acquires facial expressions and voices of the salesperson and the customer in real time using the camera 42 and microphone 238 of the smart glasses 214. For example, the determination unit determines the stress state of the salesperson based on the acquired information by the specific processing unit 290 of the data processing device 12. For example, the provision unit provides the salesperson with advice to relax based on the determined stress state by the control unit 46A of the smart glasses 214. For example, the notification unit notifies the salesperson's manager of the stress state of the salesperson by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] For example, the acquisition unit acquires the facial expressions and voices of the salesperson and the customer in real time using the camera 42 and microphone 238 of the headset type terminal 314. For example, the determination unit determines the stress state of the salesperson based on the acquired information by the specific processing unit 290 of the data processing device 12. For example, the provision unit provides the salesperson with advice for relaxing based on the determined stress state by the control unit 46A of the headset type terminal 314. For example, the notification unit notifies the salesperson's manager of the stress state of the salesperson by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a 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.

[0159] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] For example, the acquisition unit acquires the facial expressions and voices of the salesperson and the customer in real time using the camera 42 and microphone 238 of the robot 414. For example, the determination unit determines the stress state of the salesperson based on the acquired information by the specific processing unit 290 of the data processing device 12. For example, the provision unit provides the salesperson with advice to relax based on the determined stress state by the control unit 46A of the robot 414. For example, the notification unit notifies the salesperson's manager of the stress state of the salesperson by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] In addition, 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 upon request from the data processing device 12.

[0184] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) An acquisition unit that acquires information on sales staff serving customers; a determination unit that determines a stress state of the salesperson based on the information acquired by the acquisition unit; a providing unit that provides advice to the salesperson based on the stress state determined by the determining unit; a notification unit that notifies a manager of the salesperson of the stress state of the salesperson based on the stress state determined by the determination unit. A system characterized by: (Appendix 2) The determination unit is A stress index of the salesperson is determined as the stress situation based on the information acquired by the acquisition unit. 2. The system of claim 1, (Appendix 3) The acquisition unit is Information on the facial expressions, voices, and remarks of the salesperson and the customer is acquired as information on the salesperson's interactions with the customer. 2. The system of claim 1, (Appendix 4) The providing unit is Providing specific relaxation advice to salespeople experiencing increasing stress 2. The system of claim 1, (Appendix 5) The acquisition unit is Acquire information on the attributes of the salesperson and the customer as information during the salesperson's service. 2. The system of claim 1, (Appendix 6) The determination unit is The salesperson's heart rate and electrodermal activity are analyzed to determine a stress index. 2. The system of claim 1, (Appendix 7) The providing unit is Suggest specific breathing techniques according to the salesperson's stress level 2. The system of claim 1, (Appendix 8) The providing unit is Suggest specific postures for the salesperson depending on their stress level. 2. The system of claim 1, (Appendix 9) The determination unit is The tone and tempo of the salesperson's voice are analyzed to determine a stress index. 2. The system of claim 1, (Appendix 10) The notification unit is Automatically schedule meetings with managers based on the stress levels of the salespeople 2. The system of claim 1, (Appendix 11) The acquisition unit is Estimate the emotions of salespeople and adjust the timing of information acquisition based on the estimated emotions. 2. The system of claim 1, (Appendix 12) The acquisition unit is Analyze sales staff's past customer service history and select the appropriate acquisition method 2. The system of claim 1, (Appendix 13) The acquisition unit is Filter information as it is retrieved based on the salesperson's current job status and interests 2. The system of claim 1, (Appendix 14) The acquisition unit is Estimate the salesperson's emotions and prioritize the information to be retrieved based on the estimated emotions 2. The system of claim 1, (Appendix 15) The acquisition unit is When retrieving information, the geographic location of the salesperson is taken into account to prioritize retrieving the most relevant information 2. The system of claim 1, (Appendix 16) The acquisition unit is Upon information capture, analyze the salesperson's social media activity to capture relevant information 2. The system of claim 1, (Appendix 17) The determination unit is Estimating the emotions of the salesperson and adjusting the criteria for the stress index based on the estimated emotions 2. The system of claim 1, (Appendix 18) The determination unit is When making a judgment, the judgment algorithm is optimized by referring to the salesperson's past stress data. 2. The system of claim 1, (Appendix 19) The determination unit is When making the assessment, different stress assessment methods are applied depending on the salesperson's job duties. 2. The system of claim 1, (Appendix 20) The determination unit is Estimating the emotions of salespeople and adjusting the display of the stress index based on the estimated emotions 2. The system of claim 1, (Appendix 21) The determination unit is At the time of judgment, the fluctuation of the stress index is analyzed based on the working hours of the salesperson. 2. The system of claim 1, (Appendix 22) The determination unit is At the time of judgment, the stress index of the salesperson is analyzed by referring to the relevant market data. 2. The system of claim 1, (Appendix 23) The providing unit is Infer the salesperson's emotions and tailor advice based on those emotions 2. The system of claim 1, (Appendix 24) The providing unit is When providing advice, adjust the level of detail based on the salesperson's stress level 2. The system of claim 1, (Appendix 25) The providing unit is When providing advice, different advice algorithms are applied depending on the salesperson's job duties. 2. The system of claim 1, (Appendix 26) The providing unit is Estimate salesperson's emotions and adjust the length of advice based on the estimated emotions 2. The system of claim 1, (Appendix 27) The providing unit is When providing advice, prioritize advice based on salespeople's working hours 2. The system of claim 1, (Appendix 28) The providing unit is When providing advice, the salesperson references relevant market data to tailor the order of advice 2. The system of claim 1, (Appendix 29) The notification unit is Infer the salesperson's emotions and tailor the content of notifications based on the inferred emotions 2. The system of claim 1, (Appendix 30) The notification unit is When sending notifications, the notification algorithm is optimized by referencing the salesperson's past stress data. 2. The system of claim 1, (Appendix 31) The notification unit is When notifying, apply different notification methods depending on the salesperson's job duties. 2. The system of claim 1, (Appendix 32) The notification unit is Estimate salesperson sentiment and prioritize notifications based on the estimated sentiment 2. The system of claim 1, (Appendix 33) The notification unit is When notifying, adjust the timing of notifications based on salespeople's working hours 2. The system of claim 1, (Appendix 34) The notification unit is At the time of notification, the salesperson consults relevant market data to tailor the content of the notification 2. The system of claim 1, (Appendix 35) The notification unit is Upon notification, automatically schedule meetings with managers based on salespeople's stress levels 2. The system of claim 1, [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit that acquires information on sales staff serving customers; a determination unit that determines a stress state of the salesperson based on the information acquired by the acquisition unit; a providing unit that provides advice to the salesperson based on the stress state determined by the determining unit; a notification unit that notifies a manager of the salesperson of the stress state of the salesperson based on the stress state determined by the determination unit. A system characterized by:

2. The determination unit is A stress index of the salesperson is determined as the stress situation based on the information acquired by the acquisition unit.

2. The system of claim 1 .

3. The acquisition unit is Information on the facial expressions, voices, and remarks of the salesperson and the customer is acquired as information on the salesperson's interactions with the customer.

2. The system of claim 1 .

4. The providing unit is Providing specific relaxation advice to salespeople experiencing increasing stress 2. The system of claim 1 .

5. The acquisition unit is Acquire information on the attributes of the salesperson and the customer as information during the salesperson's service.

2. The system of claim 1 .

6. The determination unit is The salesperson's heart rate and electrodermal activity are analyzed to determine a stress index.

2. The system of claim 1 .

7. The providing unit is Suggest specific breathing techniques according to the salesperson's stress level 2. The system of claim 1 .

8. The providing unit is Suggest specific postures for the salesperson depending on their stress level.

2. The system of claim 1 .

9. The determination unit is The tone and tempo of the salesperson's voice are analyzed to determine a stress index.

2. The system of claim 1 .

10. The notification unit is Automatically schedule meetings with managers based on the stress levels of the salespeople 2. The system of claim 1 .

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