system

Abstract

The system according to the embodiment aims to provide waiting times and queues in real time. [Solution] A system according to an embodiment includes a reception unit, a calculation unit, and a confirmation unit. The reception unit receives reception information. The calculation unit calculates a waiting time based on the information received by the reception unit. The confirmation unit provides a user with the waiting time and turn calculated by the calculation unit.

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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that waiting times are unclear, causing stress to users.

[0005] The system according to the embodiment aims to provide waiting times and queues in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a calculation unit, and a confirmation unit. The reception unit receives reception information. The calculation unit calculates a waiting time based on the information received by the reception unit. The confirmation unit provides the user with the waiting time and turn calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide waiting times and queues in real time. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A waiting time management system according to an embodiment of the present invention is a system for making effective use of waiting times at government offices, medical institutions, restaurants, and other facilities. In this waiting time management system, a user checks in, and AI analyzes various factors based on the check-in information to calculate the waiting time and queue in real time. Because users can check their waiting time and queue through the app, they can use the time for other activities. This reduces the stress of waiting and allows users to spend their time more meaningfully. For example, a user checks in at a government office, medical institution, restaurant, or other facility. At this time, the check-in information is entered into the app. For example, when a user checks in for a medical appointment at a hospital, the information is entered into the app. Next, AI analyzes various factors based on the check-in information. For example, it calculates the waiting time and queue by taking into account the number of people currently waiting, the processing time for each person, and past data. This allows users to know in real time how far ahead their turn is and how long the wait will be. Users can check their waiting time and queue through the app. For example, while waiting for their appointment, users can check the waiting time on the app and use the time to do other errands. This allows users to make effective use of their waiting time. This system reduces the stress of waiting. Users can use their waiting time for other activities, turning it into meaningful time. For example, they can read or go shopping while they wait. Also, by making the waiting time clear, users can relax without feeling irritated. In this way, a waiting time management system can make effective use of users' waiting time and reduce stress.

[0029] A waiting time management system according to an embodiment includes a reception unit, a calculation unit, and a confirmation unit. The reception unit receives reception information when a user checks in. The reception information includes, but is not limited to, the user's name, reservation number, and reception time. For example, when a user checks in for a medical examination at a hospital, the reception unit inputs the information into an app. The reception unit can also similarly receive information when a user checks in at a government office or restaurant. The calculation unit calculates the waiting time based on the information received by the reception unit. The calculation unit calculates the waiting time by taking into account, for example, the current number of people waiting, the processing time of each person, past data, and the like. The calculation unit can analyze this data using AI and calculate the waiting time in real time. For example, the calculation unit calculates the waiting time based on the current number of people waiting. The calculation unit can also calculate the waiting time by taking into account the processing time of each person. Furthermore, the calculation unit can calculate the waiting time based on past data. The confirmation unit provides the user with the waiting time and the order calculated by the calculation unit. The confirmation unit provides the user with the waiting time and order in real time, for example, via an app. Through the app, the user can know in real time how far their turn is and how long the wait will be. For example, the confirmation unit can check the wait time on the app while the user is waiting for their turn to be examined. The confirmation unit can also check the wait time at a government office or a restaurant. In this way, the waiting time management system according to the embodiment can make effective use of the user's waiting time and reduce stress.

[0030] The waiting time management system includes a data collection unit that collects data on the number of people currently waiting and the processing time of each person. The data collection unit, for example, measures the number of people currently waiting in real time. For example, the data collection unit works in conjunction with a reception system to automatically count the number of people currently waiting. The data collection unit can also measure the processing time of each person. For example, the data collection unit can calculate the average processing time of each person based on past data. Furthermore, the data collection unit can measure the processing time of each person in real time. For example, the data collection unit measures the processing time of each person using a timer and collects that data. In this way, the data collection unit can improve the accuracy of calculating waiting times by collecting data on the number of people currently waiting and the processing time of each person.

[0031] The analysis unit can analyze waiting times based on data collected by the data collection unit. The analysis unit analyzes waiting times based on, for example, data on the number of people currently waiting and the processing time of each person collected by the data collection unit. For example, the analysis unit can analyze waiting times using statistical methods. The analysis unit can also analyze waiting times using machine learning algorithms. For example, the analysis unit can grasp trends in waiting times based on past data and predict waiting times. Furthermore, the analysis unit can analyze waiting times based on data collected in real time. For example, the analysis unit analyzes waiting times in real time based on the number of people currently waiting and the processing time of each person. In this way, the analysis unit can improve the accuracy of waiting time predictions by analyzing waiting times based on collected data.

[0032] The calculation unit can calculate the waiting time based on past data. The calculation unit, for example, calculates the waiting time based on past waiting time data. For example, the calculation unit can analyze past waiting time data and grasp the trend of waiting times. The calculation unit can also calculate the waiting time based on past reception data. For example, the calculation unit can analyze past reception data and improve the accuracy of waiting time prediction. Furthermore, the calculation unit can calculate the waiting time in real time based on past data. For example, the calculation unit predicts the current waiting time based on past data and calculates the waiting time in real time. In this way, the calculation unit can improve the accuracy of calculating the waiting time by taking past data into consideration.

[0033] The confirmation unit can provide the user with the waiting time and the line in real time. The confirmation unit can provide the user with the waiting time and the line in real time, for example, through an app. For example, the confirmation unit can allow the user to check the waiting time on the app while waiting for their turn to see a doctor. The confirmation unit can also allow the user to check the waiting time at a government office or a restaurant. Furthermore, the confirmation unit can update the waiting time and the line in real time. For example, the confirmation unit can notify the user in real time if the waiting time changes. In this way, the confirmation unit can enable the user to make effective use of their waiting time by providing the waiting time and the line in real time.

[0034] The reception unit can select an appropriate reception method by referring to the user's past reception history at the time of reception. The reception unit, for example, selects the optimal reception method based on the user's past reception history. For example, the reception unit automatically displays reception methods that the user has frequently used in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest a reception method to be used in a specific time period based on the user's past reception history. For example, the reception unit suggests the optimal reception method based on reception methods that the user has used in a specific time period in the past. In this way, the reception unit can provide the optimal reception method to the user by referring to the past reception history.

[0035] The reception unit can filter reception information based on the user's current situation and areas of interest at the time of reception. The reception unit, for example, preferentially displays reception information that is highly relevant depending on the user's current situation. For example, the reception unit displays highly relevant reception information based on the current number of people waiting and processing time. The reception unit can also filter reception information based on the user's areas of interest. For example, the reception unit displays relevant reception information based on the user's past selection history and survey results. Furthermore, the reception unit can also suggest an optimal reception method based on the user's current situation and areas of interest. For example, the reception unit suggests an optimal reception method based on the user's current situation and areas of interest. In this way, the reception unit can improve user convenience by providing reception information that is tailored to the user's situation and areas of interest.

[0036] The reception unit can preferentially acquire highly relevant reception information by taking into account the user's geographical location information at the time of reception. The reception unit, for example, preferentially displays highly relevant reception information based on the user's current location. For example, the reception unit displays reception information for locations close to the user's current location. The reception unit can also filter related reception information based on the user's geographical location information. For example, the reception unit suggests an optimal reception method based on the user's current location. In this way, the reception unit can provide highly relevant reception information by taking into account the user's geographical location information.

[0037] The reception unit can analyze the user's social media activity at the time of reception and acquire related reception information. The reception unit, for example, analyzes the user's social media activity and preferentially displays related reception information. For example, the reception unit suggests an optimal reception method based on the user's social media activity. The reception unit can also filter related reception information based on the user's social media activity. For example, the reception unit suggests an optimal reception method based on the user's social media activity. In this way, the reception unit can provide highly relevant reception information by analyzing the user's social media activity.

[0038] The calculation unit can adjust the calculation algorithm by referring to past waiting time data during calculation. The calculation unit, for example, calculates an optimal waiting time based on past waiting time data. For example, the calculation unit analyzes past data, understands waiting time trends, and optimizes the calculation algorithm. The calculation unit can also improve the accuracy of waiting time prediction by referring to past waiting time data. For example, the calculation unit predicts the current waiting time based on past data and calculates the waiting time in real time. In this way, the calculation unit can improve the accuracy of waiting time calculation by referring to past data.

[0039] The calculation unit can customize the waiting time based on the user's current situation and areas of interest at the time of calculation. The calculation unit customizes the waiting time according to the user's current situation, for example. For example, the calculation unit adjusts the waiting time based on the current number of people waiting and the processing time. The calculation unit can also adjust the waiting time based on the user's areas of interest. For example, the calculation unit adjusts the waiting time based on the user's past selection history and survey results. Furthermore, the calculation unit can calculate an optimal waiting time based on the user's current situation and areas of interest. For example, the calculation unit calculates the optimal waiting time based on the user's current situation and areas of interest. In this way, the calculation unit can improve user convenience by providing a waiting time that suits the user's situation and areas of interest.

[0040] The calculation unit can calculate the optimal waiting time by taking into account the geographical location information of the user during calculation. The calculation unit calculates the optimal waiting time based on, for example, the user's current location. For example, the calculation unit adjusts the waiting time based on the user's geographical location information. The calculation unit can also customize the waiting time based on the user's current location. For example, the calculation unit calculates the optimal waiting time based on the user's current location. In this way, the calculation unit can provide the optimal waiting time by taking into account the user's geographical location information.

[0041] The calculation unit may analyze the user's social media activity during the calculation and calculate the relevant waiting time. For example, the calculation unit may analyze the user's social media activity and calculate the relevant waiting time. For example, the calculation unit may adjust the waiting time based on the user's social media activity. The calculation unit may also calculate an optimal waiting time based on the user's social media activity. For example, the calculation unit may calculate an optimal waiting time based on the user's social media activity. In this way, the calculation unit can provide a highly relevant waiting time by analyzing the user's social media activity.

[0042] The confirmation unit can select the optimal display method by referring to the user's past confirmation history at the time of confirmation. The confirmation unit selects the optimal display method based on the user's past confirmation history, for example. For example, the confirmation unit suggests the optimal display method based on display methods used by the user in the past. The confirmation unit can also select a display method with high visibility based on the user's past confirmation history. Furthermore, the confirmation unit can provide the optimal display method by referring to the user's past confirmation history. For example, the confirmation unit suggests the optimal display method based on display methods used by the user in a specific time period in the past. In this way, the confirmation unit can provide the optimal display method to the user by referring to the past confirmation history.

[0043] The confirmation unit can customize the display information based on the user's current situation and areas of interest at the time of confirmation. The confirmation unit customizes the display information according to the user's current situation, for example. For example, the confirmation unit adjusts the display information based on the current number of people waiting and the processing time. The confirmation unit can also adjust the display information based on the user's areas of interest. For example, the confirmation unit adjusts the display information based on the user's past selection history and survey results. Furthermore, the confirmation unit can also provide optimal display information based on the user's current situation and areas of interest. For example, the confirmation unit provides optimal display information based on the user's current situation and areas of interest. In this way, the confirmation unit can improve user convenience by providing display information according to the user's situation and areas of interest.

[0044] The confirmation unit can select the optimal display method by taking into account the user's geographical location information at the time of confirmation. The confirmation unit selects the optimal display method based on, for example, the user's current location. For example, the confirmation unit adjusts the display information based on the user's geographical location information. The confirmation unit can also customize the display information based on the user's current location. For example, the confirmation unit selects the optimal display method based on the user's current location. In this way, the confirmation unit can provide the optimal display method by taking into account the user's geographical location information.

[0045] The verification unit may analyze the user's social media activity during verification and provide relevant display information. For example, the verification unit may analyze the user's social media activity and provide relevant display information. For example, the verification unit may adjust the display information based on the user's social media activity. The verification unit may also provide optimal display information based on the user's social media activity. For example, the verification unit may provide optimal display information based on the user's social media activity. In this way, the verification unit may provide highly relevant display information by analyzing the user's social media activity.

[0046] The data collection unit can select the optimal collection method by referring to past data collection history when collecting data. The data collection unit, for example, selects the optimal collection method based on the past data collection history. For example, the data collection unit analyzes the past data collection history and optimizes the collection method. The data collection unit can also provide the optimal collection method by referring to the past data collection history. For example, the data collection unit suggests the optimal collection method based on the past data collection history. In this way, the data collection unit can provide the optimal data collection method by referring to the past data collection history.

[0047] The data collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. The data collection unit, for example, prioritizes collecting highly relevant data based on the user's current location. For example, the data collection unit adjusts the order of data collection based on the user's geographical location information. The data collection unit can also provide an optimal data collection method based on the user's current location. For example, the data collection unit prioritizes collecting highly relevant data based on the user's current location. In this way, the data collection unit can provide highly relevant data by taking into account the user's geographical location information.

[0048] The analysis unit can select the optimal analysis method by referring to past data analysis history during analysis. The analysis unit, for example, selects the optimal analysis method based on past data analysis history. For example, the analysis unit analyzes past data analysis history and optimizes the analysis method. The analysis unit can also provide the optimal analysis method by referring to past data analysis history. For example, the analysis unit proposes the optimal analysis method based on past data analysis history. In this way, the analysis unit can provide the optimal data analysis method by referring to past data analysis history.

[0049] The analysis unit can customize the analysis information based on the user's current situation and areas of interest at the time of analysis. The analysis unit customizes the analysis information according to the user's current situation, for example. For example, the analysis unit adjusts the analysis information based on the current number of people waiting and the processing time. The analysis unit can also adjust the analysis information based on the user's areas of interest. For example, the analysis unit adjusts the analysis information based on the user's past selection history and survey results. Furthermore, the analysis unit can provide optimal analysis information based on the user's current situation and areas of interest. For example, the analysis unit provides optimal analysis information based on the user's current situation and areas of interest. In this way, the analysis unit can improve user convenience by providing analysis information according to the user's situation and areas of interest.

[0050] The analysis unit can select the optimal analysis method by taking into account the user's geographical location information during analysis. The analysis unit selects the optimal analysis method based on, for example, the user's current location. For example, the analysis unit adjusts the analysis information based on the user's geographical location information. The analysis unit can also customize the analysis information based on the user's current location. For example, the analysis unit selects the optimal analysis method based on the user's current location. In this way, the analysis unit can provide the optimal analysis method by taking into account the user's geographical location information.

[0051] The analysis unit can analyze the user's social media activity during the analysis and provide related analysis information. For example, the analysis unit analyzes the user's social media activity and provides related analysis information. For example, the analysis unit adjusts the analysis information based on the user's social media activity. The analysis unit can also provide optimal analysis information based on the user's social media activity. For example, the analysis unit provides optimal analysis information based on the user's social media activity. This allows the analysis unit to provide highly relevant analysis information by analyzing the user's social media activity.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The waiting time management system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit, for example, measures the user's heart rate and blood pressure and collects this data in real time. This allows the waiting time to be adjusted according to the user's health condition. For example, the waiting time can be adjusted to be shorter for a user with a high heart rate. The health monitoring unit may also provide appropriate advice based on the user's health condition. For example, it may suggest breathing techniques or stretches to help the user relax during the waiting time. Furthermore, the health monitoring unit may analyze the user's health data over the long term and support health management. As a result, the waiting time management system can reduce the user's stress and support health management by adjusting the waiting time taking the user's health condition into consideration.

[0054] The waiting time management system may further include a content providing unit that provides content that can be enjoyed during waiting time based on the user's hobbies and interests. The content providing unit may suggest related videos or articles based on the user's past browsing history or survey results, for example. This allows the user to enjoy content that matches their interests during waiting time. The content providing unit may also customize content according to the user's current situation. For example, if the user wants to relax, it may provide relaxation music or a meditation guide. Furthermore, the content providing unit may improve the quality of the content based on user feedback. In this way, the waiting time management system may provide content that matches the user's hobbies and interests, allowing the user to spend their waiting time meaningfully.

[0055] The waiting time management system may further include a navigation unit that provides guidance to nearby facilities and services based on the user's location information. The navigation unit, for example, can obtain the user's current location and provide information on nearby cafes, shops, public facilities, etc. This allows the user to use nearby facilities while waiting. The navigation unit can also filter facilities and services based on the user's interests and needs. For example, if the user wants to relax, it can suggest a quiet cafe or park. Furthermore, the navigation unit can improve the guidance content based on user feedback. This allows the waiting time management system to make effective use of waiting time by providing guidance to nearby facilities and services based on the user's location information.

[0056] The waiting time management system may further include a pattern analysis unit that analyzes users' past waiting time data and identifies waiting time patterns. The pattern analysis unit, for example, collects past waiting time data and analyzes waiting time trends and patterns. This can improve the accuracy of waiting time predictions. For example, if there is a tendency for waiting times to be longer on certain days of the week or during certain times of the day, this information can be provided to the user. The pattern analysis unit can also adjust waiting times based on waiting time patterns. For example, measures such as limiting the number of people allowed to enter during times when waiting times are long can be taken. Furthermore, the pattern analysis unit can improve the analysis content based on user feedback. This allows the waiting time management system to utilize past data to improve the accuracy of waiting time predictions and improve user convenience.

[0057] The waiting time management system may further include an event information providing unit that provides information about nearby events that can be used during waiting time based on the user's location information. The event information providing unit may, for example, acquire the user's current location and provide information about events being held in the vicinity. This allows the user to find events that they can participate in while waiting. The event information providing unit may also filter event information according to the user's interests. For example, if the user is interested in music events, it may preferentially display music events being held in the vicinity. Furthermore, the event information providing unit may improve the content provided based on user feedback. In this way, the waiting time management system may provide information about nearby events based on the user's location information, allowing the user to spend their waiting time meaningfully.

[0058] The waiting time management system may further include a prediction accuracy improvement unit that improves the accuracy of waiting time predictions based on users' past waiting time data. The prediction accuracy improvement unit, for example, collects past waiting time data and analyzes waiting time trends and patterns. This improves the accuracy of waiting time predictions. For example, if there is a tendency for waiting times to be longer on certain days of the week or during certain time periods, this information can be provided to the user. The prediction accuracy improvement unit can also adjust waiting times based on waiting time patterns. For example, measures such as limiting the number of people allowed to enter during times when waiting times are long can be taken. Furthermore, the prediction accuracy improvement unit can improve the analysis content based on user feedback. This allows the waiting time management system to improve the accuracy of waiting time predictions by utilizing past data, thereby improving user convenience.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit accepts reception information when a user checks in. The reception information includes the user's name, reservation number, reception time, etc. For example, when a user checks in for a medical examination at a hospital, they enter that information into the app. Reception information can also be accepted at government offices and restaurants in the same way. Step 2: The calculation unit calculates the waiting time based on the information received by the reception unit. The calculation unit calculates the waiting time in real time, taking into account the number of people currently waiting, the processing time for each person, past data, etc. The calculation unit uses AI to analyze the data and provide a more accurate waiting time. Step 3: The confirmation unit provides the user with the waiting time and turn calculated by the calculation unit. The confirmation unit provides the user with the waiting time and turn in real time through the app, allowing the user to check their turn and waiting time in real time.

[0061] (Example 2) A waiting time management system according to an embodiment of the present invention is a system for making effective use of waiting times at government offices, medical institutions, restaurants, and other facilities. In this waiting time management system, a user checks in, and AI analyzes various factors based on the check-in information to calculate the waiting time and queue in real time. Because users can check their waiting time and queue through the app, they can use the time for other activities. This reduces the stress of waiting and allows users to spend their time more meaningfully. For example, a user checks in at a government office, medical institution, restaurant, or other facility. At this time, the check-in information is entered into the app. For example, when a user checks in for a medical appointment at a hospital, the information is entered into the app. Next, AI analyzes various factors based on the check-in information. For example, it calculates the waiting time and queue by taking into account the number of people currently waiting, the processing time for each person, and past data. This allows users to know in real time how far ahead their turn is and how long the wait will be. Users can check their waiting time and queue through the app. For example, while waiting for their appointment, users can check the waiting time on the app and use the time to do other errands. This allows users to make effective use of their waiting time. This system reduces the stress of waiting. Users can use their waiting time for other activities, turning it into meaningful time. For example, they can read or go shopping while they wait. Also, by making the waiting time clear, users can relax without feeling irritated. In this way, a waiting time management system can make effective use of users' waiting time and reduce stress.

[0062] A waiting time management system according to an embodiment includes a reception unit, a calculation unit, and a confirmation unit. The reception unit receives reception information when a user checks in. The reception information includes, but is not limited to, the user's name, reservation number, and reception time. For example, when a user checks in for a medical examination at a hospital, the reception unit inputs the information into an app. The reception unit can also similarly receive information when a user checks in at a government office or restaurant. The calculation unit calculates the waiting time based on the information received by the reception unit. The calculation unit calculates the waiting time by taking into account, for example, the current number of people waiting, the processing time of each person, past data, and the like. The calculation unit can analyze this data using AI and calculate the waiting time in real time. For example, the calculation unit calculates the waiting time based on the current number of people waiting. The calculation unit can also calculate the waiting time by taking into account the processing time of each person. Furthermore, the calculation unit can calculate the waiting time based on past data. The confirmation unit provides the user with the waiting time and the order calculated by the calculation unit. The confirmation unit provides the user with the waiting time and order in real time, for example, via an app. Through the app, the user can know in real time how far their turn is and how long the wait will be. For example, the confirmation unit can check the wait time on the app while the user is waiting for their turn to be examined. The confirmation unit can also check the wait time at a government office or a restaurant. In this way, the waiting time management system according to the embodiment can make effective use of the user's waiting time and reduce stress.

[0063] The waiting time management system includes a data collection unit that collects data on the number of people currently waiting and the processing time of each person. The data collection unit, for example, measures the number of people currently waiting in real time. For example, the data collection unit works in conjunction with a reception system to automatically count the number of people currently waiting. The data collection unit can also measure the processing time of each person. For example, the data collection unit can calculate the average processing time of each person based on past data. Furthermore, the data collection unit can measure the processing time of each person in real time. For example, the data collection unit measures the processing time of each person using a timer and collects that data. In this way, the data collection unit can improve the accuracy of calculating waiting times by collecting data on the number of people currently waiting and the processing time of each person.

[0064] The analysis unit can analyze waiting times based on data collected by the data collection unit. The analysis unit analyzes waiting times based on, for example, data on the number of people currently waiting and the processing time of each person collected by the data collection unit. For example, the analysis unit can analyze waiting times using statistical methods. The analysis unit can also analyze waiting times using machine learning algorithms. For example, the analysis unit can grasp trends in waiting times based on past data and predict waiting times. Furthermore, the analysis unit can analyze waiting times based on data collected in real time. For example, the analysis unit analyzes waiting times in real time based on the number of people currently waiting and the processing time of each person. In this way, the analysis unit can improve the accuracy of waiting time predictions by analyzing waiting times based on collected data.

[0065] The calculation unit can calculate the waiting time based on past data. The calculation unit, for example, calculates the waiting time based on past waiting time data. For example, the calculation unit can analyze past waiting time data and grasp the trend of waiting times. The calculation unit can also calculate the waiting time based on past reception data. For example, the calculation unit can analyze past reception data and improve the accuracy of waiting time prediction. Furthermore, the calculation unit can calculate the waiting time in real time based on past data. For example, the calculation unit predicts the current waiting time based on past data and calculates the waiting time in real time. In this way, the calculation unit can improve the accuracy of calculating the waiting time by taking past data into consideration.

[0066] The confirmation unit can provide the user with the waiting time and the line in real time. The confirmation unit can provide the user with the waiting time and the line in real time, for example, through an app. For example, the confirmation unit can allow the user to check the waiting time on the app while waiting for their turn to see a doctor. The confirmation unit can also allow the user to check the waiting time at a government office or a restaurant. Furthermore, the confirmation unit can update the waiting time and the line in real time. For example, the confirmation unit can notify the user in real time if the waiting time changes. In this way, the confirmation unit can enable the user to make effective use of their waiting time by providing the waiting time and the line in real time.

[0067] The reception unit can estimate the user's emotion and adjust the reception method based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows the reception unit to adjust the reception method according to the user's emotion, thereby reducing the user's stress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0068] The reception unit can select an appropriate reception method by referring to the user's past reception history at the time of reception. The reception unit, for example, selects the optimal reception method based on the user's past reception history. For example, the reception unit automatically displays reception methods that the user has frequently used in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest a reception method to be used in a specific time period based on the user's past reception history. For example, the reception unit suggests the optimal reception method based on reception methods that the user has used in a specific time period in the past. In this way, the reception unit can provide the optimal reception method to the user by referring to the past reception history.

[0069] The reception unit can filter reception information based on the user's current situation and areas of interest at the time of reception. The reception unit, for example, preferentially displays reception information that is highly relevant depending on the user's current situation. For example, the reception unit displays highly relevant reception information based on the current number of people waiting and processing time. The reception unit can also filter reception information based on the user's areas of interest. For example, the reception unit displays relevant reception information based on the user's past selection history and survey results. Furthermore, the reception unit can also suggest an optimal reception method based on the user's current situation and areas of interest. For example, the reception unit suggests an optimal reception method based on the user's current situation and areas of interest. In this way, the reception unit can improve user convenience by providing reception information that is tailored to the user's situation and areas of interest.

[0070] The reception unit can estimate the user's emotions and determine the priority of reception based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on heart rate fluctuations. This allows the reception unit to determine the priority of reception based on the user's emotions, thereby reducing the user's stress. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0071] The reception unit can preferentially acquire highly relevant reception information by taking into account the user's geographical location information at the time of reception. The reception unit, for example, preferentially displays highly relevant reception information based on the user's current location. For example, the reception unit displays reception information for locations close to the user's current location. The reception unit can also filter related reception information based on the user's geographical location information. For example, the reception unit suggests an optimal reception method based on the user's current location. In this way, the reception unit can provide highly relevant reception information by taking into account the user's geographical location information.

[0072] The reception unit can analyze the user's social media activity at the time of reception and acquire related reception information. The reception unit, for example, analyzes the user's social media activity and preferentially displays related reception information. For example, the reception unit suggests an optimal reception method based on the user's social media activity. The reception unit can also filter related reception information based on the user's social media activity. For example, the reception unit suggests an optimal reception method based on the user's social media activity. In this way, the reception unit can provide highly relevant reception information by analyzing the user's social media activity.

[0073] The calculation unit can estimate the user's emotion and adjust the waiting time calculation method based on the estimated user's emotion. For example, the calculation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the calculation unit calculates an emotion score based on changes in facial expression. The calculation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the calculation unit analyzes the tone and speed of the voice and calculates the emotion score. The calculation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the calculation unit calculates the emotion score based on heart rate fluctuations. This allows the calculation unit to adjust the waiting time calculation method according to the user's emotion, thereby reducing user stress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0074] The calculation unit can adjust the calculation algorithm by referring to past waiting time data during calculation. The calculation unit, for example, calculates an optimal waiting time based on past waiting time data. For example, the calculation unit analyzes past data, understands waiting time trends, and optimizes the calculation algorithm. The calculation unit can also improve the accuracy of waiting time prediction by referring to past waiting time data. For example, the calculation unit predicts the current waiting time based on past data and calculates the waiting time in real time. In this way, the calculation unit can improve the accuracy of waiting time calculation by referring to past data.

[0075] The calculation unit can customize the waiting time based on the user's current situation and areas of interest at the time of calculation. The calculation unit customizes the waiting time according to the user's current situation, for example. For example, the calculation unit adjusts the waiting time based on the current number of people waiting and the processing time. The calculation unit can also adjust the waiting time based on the user's areas of interest. For example, the calculation unit adjusts the waiting time based on the user's past selection history and survey results. Furthermore, the calculation unit can calculate an optimal waiting time based on the user's current situation and areas of interest. For example, the calculation unit calculates the optimal waiting time based on the user's current situation and areas of interest. In this way, the calculation unit can improve user convenience by providing a waiting time that suits the user's situation and areas of interest.

[0076] The calculation unit can estimate the user's emotions and determine the priority of waiting times based on the estimated user emotions. For example, the calculation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the calculation unit calculates an emotion score based on changes in facial expression. The calculation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the calculation unit analyzes the tone and speed of the voice and calculates the emotion score. The calculation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the calculation unit calculates the emotion score based on heart rate fluctuations. This allows the calculation unit to determine the priority of waiting times based on the user's emotions, thereby reducing the user's stress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0077] The calculation unit can calculate the optimal waiting time by taking into account the geographical location information of the user during calculation. The calculation unit calculates the optimal waiting time based on, for example, the user's current location. For example, the calculation unit adjusts the waiting time based on the user's geographical location information. The calculation unit can also customize the waiting time based on the user's current location. For example, the calculation unit calculates the optimal waiting time based on the user's current location. In this way, the calculation unit can provide the optimal waiting time by taking into account the user's geographical location information.

[0078] The calculation unit may analyze the user's social media activity during the calculation and calculate the relevant waiting time. For example, the calculation unit may analyze the user's social media activity and calculate the relevant waiting time. For example, the calculation unit may adjust the waiting time based on the user's social media activity. The calculation unit may also calculate an optimal waiting time based on the user's social media activity. For example, the calculation unit may calculate an optimal waiting time based on the user's social media activity. In this way, the calculation unit can provide a highly relevant waiting time by analyzing the user's social media activity.

[0079] The confirmation unit can estimate the user's emotions and adjust the waiting time and queue display method based on the estimated user emotions. The confirmation unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the confirmation unit calculates an emotion score based on changes in facial expression. The confirmation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the confirmation unit analyzes the tone and speed of the voice and calculates an emotion score. The confirmation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the confirmation unit calculates an emotion score based on heart rate fluctuations. This allows the confirmation unit to reduce the user's stress by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0080] The confirmation unit can select the optimal display method by referring to the user's past confirmation history at the time of confirmation. The confirmation unit selects the optimal display method based on the user's past confirmation history, for example. For example, the confirmation unit suggests the optimal display method based on display methods used by the user in the past. The confirmation unit can also select a display method with high visibility based on the user's past confirmation history. Furthermore, the confirmation unit can provide the optimal display method by referring to the user's past confirmation history. For example, the confirmation unit suggests the optimal display method based on display methods used by the user in a specific time period in the past. In this way, the confirmation unit can provide the optimal display method to the user by referring to the past confirmation history.

[0081] The confirmation unit can customize the display information based on the user's current situation and areas of interest at the time of confirmation. The confirmation unit customizes the display information according to the user's current situation, for example. For example, the confirmation unit adjusts the display information based on the current number of people waiting and the processing time. The confirmation unit can also adjust the display information based on the user's areas of interest. For example, the confirmation unit adjusts the display information based on the user's past selection history and survey results. Furthermore, the confirmation unit can also provide optimal display information based on the user's current situation and areas of interest. For example, the confirmation unit provides optimal display information based on the user's current situation and areas of interest. In this way, the confirmation unit can improve user convenience by providing display information according to the user's situation and areas of interest.

[0082] The confirmation unit can estimate the user's emotions and determine the waiting time and display priority order based on the estimated user emotions. The confirmation unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the confirmation unit calculates an emotion score based on changes in facial expression. The confirmation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the confirmation unit analyzes the tone and speed of the voice and calculates an emotion score. The confirmation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the confirmation unit calculates an emotion score based on heart rate fluctuations. This allows the confirmation unit to determine the display priority order based on the user's emotions, thereby reducing the user's stress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0083] The confirmation unit can select the optimal display method by taking into account the user's geographical location information at the time of confirmation. The confirmation unit selects the optimal display method based on, for example, the user's current location. For example, the confirmation unit adjusts the display information based on the user's geographical location information. The confirmation unit can also customize the display information based on the user's current location. For example, the confirmation unit selects the optimal display method based on the user's current location. In this way, the confirmation unit can provide the optimal display method by taking into account the user's geographical location information.

[0084] The verification unit may analyze the user's social media activity during verification and provide relevant display information. For example, the verification unit may analyze the user's social media activity and provide relevant display information. For example, the verification unit may adjust the display information based on the user's social media activity. The verification unit may also provide optimal display information based on the user's social media activity. For example, the verification unit may provide optimal display information based on the user's social media activity. In this way, the verification unit may provide highly relevant display information by analyzing the user's social media activity.

[0085] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user's emotions. For example, the data collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on changes in facial expressions. The data collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the data collection unit analyzes the tone and speed of the voice and calculates an emotion score. The data collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on heart rate fluctuations. This allows the data collection unit to adjust the data collection method according to the user's emotions, thereby reducing the user's stress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0086] The data collection unit can select the optimal collection method by referring to past data collection history when collecting data. The data collection unit, for example, selects the optimal collection method based on the past data collection history. For example, the data collection unit analyzes the past data collection history and optimizes the collection method. The data collection unit can also provide the optimal collection method by referring to the past data collection history. For example, the data collection unit suggests the optimal collection method based on the past data collection history. In this way, the data collection unit can provide the optimal data collection method by referring to the past data collection history.

[0087] The data collection unit can estimate the user's emotions and determine the priority of data collection based on the estimated user emotions. For example, the data collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on changes in facial expressions. The data collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the data collection unit analyzes the tone and speed of the voice and calculates the emotion score. The data collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the data collection unit calculates the emotion score based on heart rate fluctuations. This allows the data collection unit to determine the priority of data collection according to the user's emotions, thereby reducing the user's stress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0088] The data collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. The data collection unit, for example, prioritizes collecting highly relevant data based on the user's current location. For example, the data collection unit adjusts the order of data collection based on the user's geographical location information. The data collection unit can also provide an optimal data collection method based on the user's current location. For example, the data collection unit prioritizes collecting highly relevant data based on the user's current location. In this way, the data collection unit can provide highly relevant data by taking into account the user's geographical location information.

[0089] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the analysis unit to adjust the data analysis method according to the user's emotions, thereby reducing the user's stress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0090] The analysis unit can select the optimal analysis method by referring to past data analysis history during analysis. The analysis unit, for example, selects the optimal analysis method based on past data analysis history. For example, the analysis unit analyzes past data analysis history and optimizes the analysis method. The analysis unit can also provide the optimal analysis method by referring to past data analysis history. For example, the analysis unit proposes the optimal analysis method based on past data analysis history. In this way, the analysis unit can provide the optimal data analysis method by referring to past data analysis history.

[0091] The analysis unit can customize the analysis information based on the user's current situation and areas of interest at the time of analysis. The analysis unit customizes the analysis information according to the user's current situation, for example. For example, the analysis unit adjusts the analysis information based on the current number of people waiting and the processing time. The analysis unit can also adjust the analysis information based on the user's areas of interest. For example, the analysis unit adjusts the analysis information based on the user's past selection history and survey results. Furthermore, the analysis unit can provide optimal analysis information based on the user's current situation and areas of interest. For example, the analysis unit provides optimal analysis information based on the user's current situation and areas of interest. In this way, the analysis unit can improve user convenience by providing analysis information according to the user's situation and areas of interest.

[0092] The analysis unit can estimate the user's emotions and determine the priority of data analysis based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the analysis unit to determine the priority of data analysis based on the user's emotions, thereby reducing the user's stress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0093] The analysis unit can select the optimal analysis method by taking into account the user's geographical location information during analysis. The analysis unit selects the optimal analysis method based on, for example, the user's current location. For example, the analysis unit adjusts the analysis information based on the user's geographical location information. The analysis unit can also customize the analysis information based on the user's current location. For example, the analysis unit selects the optimal analysis method based on the user's current location. In this way, the analysis unit can provide the optimal analysis method by taking into account the user's geographical location information.

[0094] The analysis unit can analyze the user's social media activity during the analysis and provide related analysis information. For example, the analysis unit analyzes the user's social media activity and provides related analysis information. For example, the analysis unit adjusts the analysis information based on the user's social media activity. The analysis unit can also provide optimal analysis information based on the user's social media activity. For example, the analysis unit provides optimal analysis information based on the user's social media activity. This allows the analysis unit to provide highly relevant analysis information by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, confirmation unit, and data collection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives reception information from the user. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the waiting time using AI. The confirmation unit is realized, for example, by the output device 40 of the smart device 14 and provides the user with the waiting time and their turn. The data collection unit is realized, for example, by the camera 42 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and collects data on the current number of people waiting and the processing time for each person. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, calculation unit, confirmation unit, and data collection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the user's reception information. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the waiting time using AI. The confirmation unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the user with the waiting time and their turn. The data collection unit is realized, for example, by the camera 42 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and collects data on the current number of people waiting and the processing time for each person. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, confirmation unit, and data collection unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and receives reception information from the user. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the waiting time using AI. The confirmation unit is realized by the display 343 of the headset terminal 314 and provides the user with the waiting time and their turn. The data collection unit is realized by the camera 42 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and collects data on the number of people currently waiting and the processing time for each person. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, confirmation unit, and data collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives reception information from the user. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the waiting time using AI. The confirmation unit is realized, for example, by the speaker 240 of the robot 414 and provides the user with the waiting time and their turn. The data collection unit is realized, for example, by the camera 42 of the robot 414 or the specific processing unit 290 of the data processing device 12 and collects data on the current number of people waiting and the processing time for each person.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The waiting time management system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit, for example, measures the user's heart rate and blood pressure and collects this data in real time. This allows the waiting time to be adjusted according to the user's health condition. For example, the waiting time can be adjusted to be shorter for a user with a high heart rate. The health monitoring unit may also provide appropriate advice based on the user's health condition. For example, it may suggest breathing techniques or stretches to help the user relax during the waiting time. Furthermore, the health monitoring unit may analyze the user's health data over the long term and support health management. As a result, the waiting time management system can reduce the user's stress and support health management by adjusting the waiting time taking the user's health condition into consideration.

[0097] The waiting time management system may further include a content providing unit that provides content that can be enjoyed during waiting time based on the user's hobbies and interests. The content providing unit may suggest related videos or articles based on the user's past browsing history or survey results, for example. This allows the user to enjoy content that matches their interests during waiting time. The content providing unit may also customize content according to the user's current situation. For example, if the user wants to relax, it may provide relaxation music or a meditation guide. Furthermore, the content providing unit may improve the quality of the content based on user feedback. In this way, the waiting time management system may provide content that matches the user's hobbies and interests, allowing the user to spend their waiting time meaningfully.

[0098] The waiting time management system may further include an activity suggestion unit that estimates the user's emotions and suggests activities to do during waiting time based on the estimated emotions. The activity suggestion unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling stressed, it can suggest relaxing activities. Also, if the user is bored, it can suggest interesting activities. Furthermore, the activity suggestion unit can improve the suggestions based on user feedback. In this way, the waiting time management system can suggest activities that correspond to the user's emotions, allowing the user to spend their waiting time more comfortably.

[0099] The waiting time management system may further include a navigation unit that provides guidance to nearby facilities and services based on the user's location information. The navigation unit, for example, can obtain the user's current location and provide information on nearby cafes, shops, public facilities, etc. This allows the user to use nearby facilities while waiting. The navigation unit can also filter facilities and services based on the user's interests and needs. For example, if the user wants to relax, it can suggest a quiet cafe or park. Furthermore, the navigation unit can improve the guidance content based on user feedback. This allows the waiting time management system to make effective use of waiting time by providing guidance to nearby facilities and services based on the user's location information.

[0100] The waiting time management system may further include an entertainment provider that estimates the user's emotions and provides entertainment for the user's waiting time based on the estimated emotions. The entertainment provider may, for example, analyze the user's facial expressions and voice to estimate the user's emotions. For example, if the user is feeling stressed, the entertainment provider may provide relaxing movies or music. If the user is bored, the entertainment provider may provide engaging games or quizzes. Furthermore, the entertainment provider may improve the content of the entertainment provided based on user feedback. In this way, the waiting time management system can provide entertainment that matches the user's emotions, allowing the user to spend their waiting time more enjoyable.

[0101] The waiting time management system may further include a pattern analysis unit that analyzes users' past waiting time data and identifies waiting time patterns. The pattern analysis unit, for example, collects past waiting time data and analyzes waiting time trends and patterns. This can improve the accuracy of waiting time predictions. For example, if there is a tendency for waiting times to be longer on certain days of the week or during certain times of the day, this information can be provided to the user. The pattern analysis unit can also adjust waiting times based on waiting time patterns. For example, measures such as limiting the number of people allowed to enter during times when waiting times are long can be taken. Furthermore, the pattern analysis unit can improve the analysis content based on user feedback. This allows the waiting time management system to utilize past data to improve the accuracy of waiting time predictions and improve user convenience.

[0102] The waiting time management system may further include a relaxation suggestion unit that estimates the user's emotions and suggests relaxation methods for the waiting time based on the estimated emotions. The relaxation suggestion unit may, for example, analyze the user's facial expressions and voice to estimate the user's emotions. For example, if the user is feeling stressed, it may provide guidance on deep breathing or meditation. If the user wants to relax, it may suggest relaxation music or aromatherapy. Furthermore, the relaxation suggestion unit may improve the suggestions based on user feedback. In this way, the waiting time management system can suggest relaxation methods according to the user's emotions, allowing the user to spend their waiting time more comfortably.

[0103] The waiting time management system may further include an event information providing unit that provides information about nearby events that can be used during waiting time based on the user's location information. The event information providing unit may, for example, acquire the user's current location and provide information about events being held in the vicinity. This allows the user to find events that they can participate in while waiting. The event information providing unit may also filter event information according to the user's interests. For example, if the user is interested in music events, it may preferentially display music events being held in the vicinity. Furthermore, the event information providing unit may improve the content provided based on user feedback. In this way, the waiting time management system may provide information about nearby events based on the user's location information, allowing the user to spend their waiting time meaningfully.

[0104] The waiting time management system may further include a communication support unit that estimates the user's emotions and supports communication during waiting time based on the estimated emotions. The communication support unit may, for example, analyze the user's facial expressions and voice to estimate emotions. For example, if the user feels lonely, it may provide a chat function with other users. Also, if the user feels stressed, it may suggest topics of conversation that will help the user relax. Furthermore, the communication support unit may improve the support content based on user feedback. In this way, the waiting time management system can provide communication support according to the user's emotions, allowing the user to spend their waiting time more comfortably.

[0105] The waiting time management system may further include a prediction accuracy improvement unit that improves the accuracy of waiting time predictions based on users' past waiting time data. The prediction accuracy improvement unit, for example, collects past waiting time data and analyzes waiting time trends and patterns. This improves the accuracy of waiting time predictions. For example, if there is a tendency for waiting times to be longer on certain days of the week or during certain time periods, this information can be provided to the user. The prediction accuracy improvement unit can also adjust waiting times based on waiting time patterns. For example, measures such as limiting the number of people allowed to enter during times when waiting times are long can be taken. Furthermore, the prediction accuracy improvement unit can improve the analysis content based on user feedback. This allows the waiting time management system to improve the accuracy of waiting time predictions by utilizing past data, thereby improving user convenience.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The reception unit accepts reception information when a user checks in. The reception information includes the user's name, reservation number, reception time, etc. For example, when a user checks in for a medical examination at a hospital, they enter that information into the app. Reception information can also be accepted at government offices and restaurants in the same way. Step 2: The calculation unit calculates the waiting time based on the information received by the reception unit. The calculation unit calculates the waiting time in real time, taking into account the number of people currently waiting, the processing time for each person, past data, etc. The calculation unit uses AI to analyze the data and provide a more accurate waiting time. Step 3: The confirmation unit provides the user with the waiting time and turn calculated by the calculation unit. The confirmation unit provides the user with the waiting time and turn in real time through the app, allowing the user to check their turn and waiting time in real time.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0109] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0110] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] 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, RAM 30, and 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.

[0115] 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.

[0116] 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 audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] 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 carried out in a secure state.

[0119] Fig. 4 shows an example of the 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.

[0120] 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.

[0121] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0125] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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, RAM 30, and 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.

[0131] 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.

[0132] 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 audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] 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 carried out in a secure state.

[0135] 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.

[0136] 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.

[0137] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0139] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0141] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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, RAM 30, and 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.

[0147] 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.

[0148] 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 audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] 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 carried out in a secure state.

[0151] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0152] 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.

[0153] 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.

[0154] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0156] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] 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 voice indicating a user input regarding 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.

[0158] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0163] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0164] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0165] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0168] 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.

[0169] 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 USB (Universal Serial Bus) 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.

[0170] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0171] 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.

[0172] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0173] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0174] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0175] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0176] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0177] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0178] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0179] [Explanation of symbols]

[0180] 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. a reception unit that receives reception information; a calculation unit that calculates a waiting time based on the information received by the reception unit; a confirmation unit that provides the user with the waiting time and turn calculated by the calculation unit. A system characterized by:

2. Equipped with a data collection unit that collects data on the number of people currently waiting and the processing time of each person 2. The system of claim 1.

3. An analysis unit that analyzes waiting times based on the data collected by the data collection unit 3. The system of claim 2.

4. The calculation unit Calculate waiting times based on past data 2. The system of claim 1.

5. The confirmation unit Provide users with real-time wait times and queue information 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the reception method based on the estimated user emotions 2. The system of claim 1.

7. The reception unit When checking in, the appropriate check-in method is selected by referring to the user's past check-in history.

2. The system of claim 1.

8. The reception unit Filtering reception information based on the user's current situation and interests at the time of reception 2. The system of claim 1.

9. The reception unit Estimate the user's emotions and determine the reception priority based on the estimated user emotions.

2. The system of claim 1.

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