system

Abstract

The system according to the embodiment aims to efficiently detect items left behind on buses and taxis and notify their owners. [Solution] A system according to an embodiment includes a recognition unit, a detection unit, a notification unit, and a guidance unit. The recognition unit uses a camera to recognize people and their belongings. The detection unit detects whether the belongings have been left on the seat based on the information recognized by the recognition unit. The notification unit notifies the owner of the information detected by the detection unit. The guidance unit guides the person to the location of the belongings based on the information notified by the notification 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 of making it difficult to efficiently detect items left behind on buses and taxis and notify their owners.

[0005] The system according to the embodiment aims to efficiently detect items left behind on buses and taxis and notify their owners. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, a detection unit, a notification unit, and a guidance unit. The recognition unit uses a camera to recognize people and their belongings. The detection unit detects whether the belongings are left on the seat based on the information recognized by the recognition unit. The notification unit notifies the owner of the information detected by the detection unit. The guidance unit guides the person to the location of the belongings based on the information notified by the notification unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently detect items left behind on a bus or taxi and notify the owner. [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 lost-item prevention system according to an embodiment of the present invention automatically detects items left behind on buses and taxis and uses an interactive generative AI to notify and guide the passenger. This lost-item prevention system uses cameras installed inside buses and taxis to recognize people and their belongings, and detects when passengers leave their belongings on the seat when they exit the vehicle. For example, if a passenger leaves their bag on the seat, the camera recognizes this. Next, if the belongings are detected as being left behind, the interactive generative AI notifies the owner. The generative AI notifies the passenger of the lost item via their smartphone or an in-vehicle display. For example, it displays a message such as, "Your bag has been left on the seat." When the owner notices the lost item, the interactive generative AI guides them to the location of the item. The generative AI provides specific instructions to the owner, guiding them to the location of the lost item. For example, it may issue an instruction such as, "Your bag is in the back seat. Please retrieve it." This system automatically detects items left behind on buses and taxis, and uses the interactive generative AI to notify and guide the passenger, thereby preventing lost items. This allows the lost property prevention system to automatically detect items left behind on buses and taxis, and use interactive generative AI to notify and guide the user, thereby preventing items from being lost.

[0029] A lost property prevention system according to an embodiment includes a recognition unit, a detection unit, a notification unit, and a guidance unit. The recognition unit recognizes people and their belongings using a camera. The recognition unit recognizes passengers and their belongings using a camera installed in, for example, a bus or taxi. The camera may be installed, for example, on the ceiling or in front of the seat. The recognition unit analyzes the shape of the passenger and their belongings using image analysis technology. For example, the recognition unit can recognize belongings such as a bag or coat placed by the passenger on the seat. The detection unit detects whether the belongings have been left on the seat based on the information recognized by the recognition unit. For example, the detection unit can detect whether the belongings have been left on the seat for a certain period of time or longer. The detection unit uses an algorithm to detect the position and movement of the object. The notification unit notifies the owner of the information detected by the detection unit. For example, the notification unit can send a push notification to the passenger's smartphone. The notification unit can also display a message on an in-vehicle display. The notification unit can adjust the timing and means of notification. The guidance unit guides the owner to the location of the belongings based on the information notified by the notification unit. The guidance unit can, for example, give specific instructions to the owner and guide them to the location of the lost item. The guidance unit can guide the owner using audio guidance or visual guides. As a result, the lost item prevention system according to the embodiment can automatically detect items left behind on buses and taxis, and notify and guide the owner using an interactive generation AI, thereby preventing items from being lost.

[0030] The recognition unit can use a camera to analyze the shapes of people and their belongings and detect whether the belongings have been left on the seat. The recognition unit, for example, uses a camera to analyze the shapes of people and their belongings. For example, the recognition unit can recognize belongings such as bags and coats that passengers have placed on their seats. The recognition unit uses image analysis technology to extract shape characteristics of the belongings. For example, the recognition unit can analyze characteristics such as the outline, color, and texture of the belongings. This allows the use of a camera to analyze the shape of the belongings and improve the accuracy of detecting lost items. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the recognition unit can input image data acquired by a camera into a generation AI and have the generation AI perform shape analysis of the belongings from the image data.

[0031] The notification unit can send a push notification to the passenger's smartphone. The notification unit, for example, sends a push notification to the passenger's smartphone. For example, if the notification unit detects a lost item, it can send a message to the passenger's smartphone such as "Your bag has been left on your seat." The notification unit can adjust the trigger conditions for the notification and the format of the notification content. For example, the notification unit can send a notification if it detects that a belonging has been left on a seat for more than a certain period of time. The notification unit can also customize the notification content and send an appropriate message to the passenger. As a result, by sending a push notification to the passenger's smartphone, the passenger can be promptly notified of the lost item. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the lost item detection information to a generation AI and have the generation AI generate the notification content.

[0032] The notification unit can display a message on an in-vehicle display. The notification unit, for example, displays a message on an in-vehicle display. For example, if the notification unit detects a lost item, it can display a message such as "Your bag has been left on the seat" on an in-vehicle display. The notification unit can adjust the display timing and format of the display content. For example, the notification unit can display a message if it detects that a belonging has been left on a seat for more than a certain period of time. The notification unit can also customize the display content and display an appropriate message to passengers. As a result, displaying a message on an in-vehicle display makes it easier for passengers to notice a lost item. Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information about the detection of the lost item to a generation AI and cause the generation AI to generate the display content.

[0033] The guidance unit can issue instructions to the owner and guide them to the location of the lost item. For example, the guidance unit can issue specific instructions to the owner and guide them to the location of the lost item. For example, the guidance unit can issue an instruction such as, "Your bag is in the back seat. Please take it." The guidance unit can adjust the content and means of communication of the instructions. For example, the guidance unit can guide the owner using audio guidance or visual guides. The guidance unit can also update the location information of the belongings in real time and provide an optimal guidance route. For example, if the location of the belongings changes, the location information can be updated in real time and an optimal guidance route can be provided. This allows the owner to be quickly guided to the location of the lost item by issuing specific instructions. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without AI. For example, the guidance unit can input the location information of the belongings into a generation AI and have the generation AI generate a guidance route.

[0034] The recognition unit can use cameras installed on the ceiling of a bus or taxi, or in front of the seats. For example, the recognition unit can use cameras installed on the ceiling of a bus or taxi, or in front of the seats, to recognize passengers and their belongings over a wide area. Alternatively, the recognition unit can use cameras installed in front of the seats to recognize passengers' belongings around their seats in detail. The camera's installation position and method affect the recognition range and accuracy, so they must be selected appropriately. For example, the recognition range can be optimized by adjusting the camera's installation angle and height. This expands the recognition range and improves the accuracy of detecting lost items when using cameras installed on the ceiling or in front of the seats. Some or all of the above processing in the recognition unit may be performed using AI, or not. For example, the recognition unit can input image data acquired by the camera into a generating AI, and have the generating AI perform recognition of belongings from the image data.

[0035] The recognition unit can optimize its recognition algorithm by referring to the passenger's past travel history during recognition. For example, the recognition unit can prioritize recognizing items that the passenger has frequently used in the past. The recognition unit can also analyze the passenger's past travel history to determine if they tend to forget items at certain times of the day and adjust the recognition accuracy accordingly. Furthermore, the recognition unit can enhance the recognition of specific items based on the passenger's past travel history. As a result, the recognition algorithm is optimized by referring to past travel history, improving recognition accuracy. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the passenger's past travel history data into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0036] The recognition unit can improve recognition accuracy by taking into account the material or color of the passenger's belongings during recognition. For example, the recognition unit can improve recognition accuracy by taking into account the material and color of the passenger's belongings during recognition. For example, if the material of the belongings is metal, the recognition unit can adjust the recognition algorithm by taking into account reflection. Furthermore, if the color of the belongings easily blends in with the background, the recognition unit can improve recognition accuracy by performing color correction. Furthermore, if the material of the belongings is cloth, the recognition unit can optimize the recognition algorithm by taking into account wrinkles and folds. In this way, recognition accuracy is improved by taking into account the material and color of the belongings. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input data on the material and color of the belongings to the generation AI and cause the generation AI to adjust the recognition algorithm.

[0037] The recognition unit can improve recognition accuracy based on the passenger's geographical location information during recognition. For example, the recognition unit can improve recognition accuracy based on the passenger's geographical location information during recognition. For example, when a passenger is in a specific area, the recognition unit can prioritize recognizing belongings unique to that area. Furthermore, when a passenger is in a tourist destination, the recognition unit can prioritize recognizing belongings that tourists are likely to carry. Furthermore, when a passenger is in a commercial area, the recognition unit can prioritize recognizing belongings such as shopping bags. This improves recognition accuracy by taking geographical location information into consideration. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input the passenger's geographical location information to the generation AI and cause the generation AI to adjust the recognition algorithm.

[0038] During recognition, the recognition unit can analyze the passenger's social media activity and prioritize recognition of related belongings. For example, during recognition, the recognition unit can analyze the passenger's social media activity and prioritize recognition of related belongings. For example, the recognition unit can analyze photos posted by the passenger on social media to identify the passenger's belongings. The recognition unit can also prioritize recognition of belongings mentioned by the passenger on social media. Furthermore, the recognition unit can prioritize recognition of belongings related to a specific event from the passenger's social media activity. This improves the accuracy of recognition of related belongings by analyzing the social media activity. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the passenger's social media data into a generation AI and have the generation AI recognize the passenger's belongings.

[0039] The detection unit can improve detection accuracy by taking into account the shape or size of the belongings during detection. The detection unit can improve detection accuracy by taking into account the shape and size of the belongings during detection, for example. For example, if the belongings have a complex shape, the detection unit can improve detection accuracy by performing shape analysis. Furthermore, if the belongings are small, the detection unit can improve detection accuracy by enlarging the display. Furthermore, if the shape of the belongings is prone to change, the detection unit can improve detection accuracy by dynamically tracking the shape. This improves detection accuracy by taking into account the shape and size of the belongings. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data on the shape and size of the belongings to the generation AI and cause the generation AI to adjust the detection algorithm.

[0040] The detection unit can optimize the detection algorithm by analyzing the passenger's movement pattern during detection. For example, the detection unit can optimize the detection algorithm by analyzing the passenger's movement pattern during detection. For example, if the passenger moves frequently, the detection unit can analyze the movement pattern and adjust the detection algorithm. Also, if the passenger is stationary, the detection unit can analyze the movement pattern and optimize the detection algorithm. Furthermore, if the passenger performs a specific movement, the detection unit can analyze the movement pattern and optimize the detection algorithm. In this way, by analyzing the movement pattern, the detection algorithm is optimized and detection accuracy is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI or without AI. For example, the detection unit can input the passenger's movement data to a generation AI and cause the generation AI to analyze the movement pattern and optimize the detection algorithm.

[0041] The detection unit can improve detection accuracy by taking into account the weight or material of the belongings during detection. The detection unit can improve detection accuracy by taking into account the weight and material of the belongings during detection, for example. For example, if the belongings are light, the detection unit can adjust the detection algorithm by taking the weight into account. Furthermore, if the belongings are made of metal, the detection unit can adjust the detection algorithm by taking into account reflection. Furthermore, if the belongings are made of cloth, the detection unit can optimize the detection algorithm by taking into account wrinkles and folds. This improves detection accuracy by taking into account the weight and material of the belongings. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data on the weight and material of the belongings into the generation AI and have the generation AI adjust the detection algorithm.

[0042] The detection unit can optimize the detection algorithm by referring to the passenger's past lost item history during detection. For example, the detection unit can optimize the detection algorithm by referring to the passenger's past lost item history during detection. For example, the detection unit can prioritize detecting belongings that the passenger has frequently left behind in the past. The detection unit can also analyze belongings that are likely to be forgotten at specific time periods from the passenger's past lost item history and adjust the detection accuracy. Furthermore, the detection unit can enhance detection of specific belongings based on the passenger's past lost item history. By referring to the past lost item history, the detection algorithm is optimized and the detection accuracy is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input the passenger's past lost item history data into the generation AI and cause the generation AI to optimize the detection algorithm.

[0043] The notification unit can determine the priority of notifications based on the importance of the belongings when notifying. For example, the notification unit can determine the priority of notifications based on the importance of the belongings when notifying. For example, if the belongings are important documents, the notification unit can give the highest priority to notifications. Furthermore, if the belongings are everyday items, the notification unit can give the highest priority to notifications. Furthermore, if the belongings are valuables, the notification unit can give the highest priority to notifications. In this way, by determining the priority of notifications based on the importance of the belongings, notifications of important belongings are given priority. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the importance data of the belongings to a generation AI and have the generation AI determine the priority of notifications.

[0044] The notification unit can optimize the notification algorithm by referring to the passenger's past notification history when issuing a notification. For example, the notification unit can optimize the notification algorithm by referring to the passenger's past notification history when issuing a notification. For example, the notification unit can suggest an optimal notification method based on the content of notifications that the passenger frequently received in the past. The notification unit can also analyze the passenger's tendency to receive notifications during specific time periods based on the passenger's past notification history and adjust the timing of notifications. Furthermore, the notification unit can enhance notifications regarding specific belongings based on the passenger's past notification history. By referring to the past notification history, the notification algorithm is optimized and the accuracy of notifications is improved. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input the passenger's past notification history data into the generation AI and cause the generation AI to optimize the notification algorithm.

[0045] The notification unit can select the optimal notification method based on the passenger's device information when providing notification. For example, the notification unit can select the optimal notification method based on the passenger's device information when providing notification. For example, if the passenger is using a smartphone, the notification unit can send a push notification. Furthermore, if the passenger is using a tablet, the notification unit can provide a notification method optimized for a large screen. Furthermore, if the passenger is using a smartwatch, the notification unit can provide a simple and highly visible notification method. This allows the optimal notification method to be selected by taking device information into consideration, thereby improving the effectiveness of the notification. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the passenger's device information into the generation AI and cause the generation AI to select the optimal notification method.

[0046] The notification unit can analyze the passenger's current activity status when sending a notification and prioritize sending relevant notifications. For example, the notification unit can prioritize sending important notifications if the passenger is traveling. It can also send regular notifications if the passenger is resting. Furthermore, if the passenger is in a meeting, the notification unit can downplay the notification and allow them to review it later. This improves the effectiveness of notifications by prioritizing relevant notifications through analysis of current activity status. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input passenger activity data into a generating AI and have the generating AI determine the priority of relevant notifications.

[0047] The guidance unit can update the location information of the belongings in real time during guidance to provide the optimal guidance route. For example, if the location of the belongings changes, the guidance unit can update the location information in real time to provide the optimal guidance route. Furthermore, if the belongings are in motion, the guidance unit can track their location in real time to provide the optimal guidance route. In addition, if the location information of the belongings is unclear, the guidance unit can present multiple candidate routes and allow the user to select the optimal route. This improves the effectiveness of guidance by providing the optimal guidance route through real-time updates of the location information of the belongings. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the location information of the belongings into a generating AI and have the generating AI generate the optimal guidance route.

[0048] The guidance unit can optimize the guidance algorithm by referring to the passenger's past guidance history when guiding. For example, the guidance unit can optimize the guidance algorithm by referring to the passenger's past guidance history when guiding. For example, the guidance unit can propose an optimal guidance route based on guidance routes used by the passenger in the past. The guidance unit can also propose a route that avoids congestion based on the passenger's past guidance history. Furthermore, the guidance unit can analyze the passenger's past guidance history and propose the most efficient guidance route. By referring to the past guidance history, the guidance algorithm is optimized and the accuracy of guidance is improved. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the passenger's past guidance history data into the generation AI and cause the generation AI to optimize the guidance algorithm.

[0049] The guidance unit can provide an optimal guidance route based on the passenger's geographical location information when guiding. For example, when guiding, the guidance unit can provide an optimal guidance route based on the passenger's geographical location information. For example, if the passenger is in a specific area, the guidance unit can preferentially provide a route specific to that area. Furthermore, if the passenger is in a tourist destination, the guidance unit can provide a route that is easy for tourists to use. Furthermore, if the passenger is in a commercial area, the guidance unit can provide a route that passes through commercial facilities. In this way, by taking the geographical location information into consideration, an optimal guidance route is provided, improving the effectiveness of guidance. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the passenger's geographical location information into a generation AI and cause the generation AI to generate an optimal guidance route.

[0050] The guidance unit can analyze the passenger's current activity status and prioritize providing relevant guidance information when guiding the passenger. For example, the guidance unit can prioritize providing the shortest route when the passenger is traveling. Furthermore, the guidance unit can provide detailed guidance information when the passenger is taking a break. Furthermore, the guidance unit can reduce notifications when the passenger is in a meeting and allow the passenger to check later. This allows the relevant guidance information to be prioritized by analyzing the current activity status, improving the effectiveness of guidance. Some or all of the above-described processing in the guidance unit may be performed using, or without, AI. For example, the guidance unit can input passenger activity status data into a generation AI and have the generation AI determine the priority of relevant guidance information.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The recognition unit can improve recognition accuracy by taking into account not only the shape of a passenger's belongings but also the frequency with which the belongings are used. For example, the recognition unit can prioritize recognition of belongings that are frequently used by passengers. The recognition unit can also evaluate the importance of belongings based on the frequency with which the belongings are used, and prioritize recognition of belongings with high importance. Furthermore, the recognition unit can accumulate data on the frequency with which belongings are used and optimize the recognition algorithm based on past data. By taking into account the frequency with which belongings are used, recognition accuracy is improved, making the detection of lost items more effective.

[0053] The notification unit can send notifications not only to passengers' smartphones but also to other devices such as smartwatches and tablets. For example, if a passenger is using a smartwatch, the notification unit can notify them by vibration or sound. Also, if a passenger is using a tablet, the notification unit can display a notification optimized for a large screen. Furthermore, if a passenger is using multiple devices, the notification unit can send the notification to the most appropriate device. This improves the effectiveness of notifications by supporting multiple devices, making it easier for passengers to notice forgotten items.

[0054] The guidance unit can provide a guidance route by taking into account not only the location information of the passenger's belongings but also the passenger's current location information. For example, the guidance unit can determine where the passenger is located in the vehicle and suggest the optimal route. Also, when the passenger is moving, the guidance unit can update the location information in real time and suggest the optimal route. Furthermore, the guidance unit can suggest a route that avoids congestion based on the passenger's location information. In this way, by taking the passenger's location information into consideration, the accuracy of guidance is improved and lost items can be quickly retrieved.

[0055] The recognition unit can improve recognition accuracy by taking into account not only the shape of the passenger's belongings but also the material and color of the belongings. For example, if the belongings are made of metal, the recognition unit can adjust the recognition algorithm to take into account reflection. Also, if the color of the belongings easily blends in with the background, the recognition unit can perform color correction to improve recognition accuracy. Furthermore, if the belongings are made of cloth, the recognition unit can optimize the recognition algorithm to take into account wrinkles and folds. By taking into account the material and color of the belongings, recognition accuracy is improved and left-behind items can be detected more effectively.

[0056] The recognition unit can optimize the recognition algorithm by referring to the passenger's past riding history. For example, the recognition unit can prioritize recognition of belongings that the passenger has frequently used in the past. The recognition unit can also analyze the passenger's past riding history to determine whether the passenger tends to forget belongings at certain times of the day, and adjust the recognition accuracy. Furthermore, the recognition unit can strengthen recognition of specific belongings based on the passenger's past riding history. In this way, by referring to the passenger's past riding history, the recognition algorithm is optimized and the recognition accuracy is improved.

[0057] When making a notification, the notification unit can determine the priority of the notification based on the importance of the belongings. For example, if the belongings are important documents, the notification unit can give the highest priority to the notification. Also, if the belongings are everyday items, the notification unit can give the notification with normal priority. Furthermore, if the belongings are valuables, the notification unit can give the highest priority to the notification. In this way, by determining the priority of notifications based on the importance of the belongings, notifications for important belongings are given priority, making it easier for passengers to notice forgotten items.

[0058] When guiding a passenger, the guiding unit can provide the optimal guidance route based on the passenger's geographical location information. For example, if the passenger is in a specific area, the guiding unit can provide a route specific to that area with priority. Also, if the passenger is in a tourist spot, the guiding unit can provide a route that is easy for tourists to use. Furthermore, if the passenger is in a commercial area, the guiding unit can provide a route that passes through commercial facilities. In this way, by taking the geographical location information into consideration, the optimal guidance route is provided, the effectiveness of guidance is improved, and passengers can quickly retrieve their lost items.

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

[0060] Step 1: The recognition unit uses cameras to recognize people and their belongings. The recognition unit uses cameras installed inside, for example, buses or taxis to recognize passengers and their belongings. Cameras may be installed, for example, on the ceiling or in front of the seats. The recognition unit uses image analysis technology to analyze the shape of passengers and their belongings. For example, the recognition unit can recognize belongings such as bags and coats that passengers place on their seats. Step 2: The detection unit detects whether the belongings have been left on the seat based on the information recognized by the recognition unit. For example, the detection unit can detect whether the belongings have been left on the seat for a certain period of time. The detection unit uses an algorithm to detect the position information and movement of the object. Step 3: The notification unit informs the owner of the information detected by the detection unit. The notification unit can, for example, send a push notification to the passenger's smartphone. It can also display a message on the in-vehicle display. The notification unit can adjust the timing and method of notification. Step 4: The guiding unit guides the owner to the location of the lost item based on the information notified by the notification unit. For example, the guiding unit can provide specific instructions to the owner and guide them to the location of the lost item. The guiding unit can guide the owner using audio guidance or visual guides.

[0061] (Example 2) A lost-item prevention system according to an embodiment of the present invention automatically detects items left behind on buses and taxis and uses an interactive generative AI to notify and guide the passenger. This lost-item prevention system uses cameras installed inside buses and taxis to recognize people and their belongings, and detects when passengers leave their belongings on the seat when they exit the vehicle. For example, if a passenger leaves their bag on the seat, the camera recognizes this. Next, if the belongings are detected as being left behind, the interactive generative AI notifies the owner. The generative AI notifies the passenger of the lost item via their smartphone or an in-vehicle display. For example, it displays a message such as, "Your bag has been left on the seat." When the owner notices the lost item, the interactive generative AI guides them to the location of the item. The generative AI provides specific instructions to the owner, guiding them to the location of the lost item. For example, it may issue an instruction such as, "Your bag is in the back seat. Please retrieve it." This system automatically detects items left behind on buses and taxis, and uses the interactive generative AI to notify and guide the passenger, thereby preventing lost items. This allows the lost property prevention system to automatically detect items left behind on buses and taxis, and use interactive generative AI to notify and guide the user, thereby preventing items from being lost.

[0062] A lost property prevention system according to an embodiment includes a recognition unit, a detection unit, a notification unit, and a guidance unit. The recognition unit recognizes people and their belongings using a camera. The recognition unit recognizes passengers and their belongings using a camera installed in, for example, a bus or taxi. The camera may be installed, for example, on the ceiling or in front of the seat. The recognition unit analyzes the shape of the passenger and their belongings using image analysis technology. For example, the recognition unit can recognize belongings such as a bag or coat placed by the passenger on the seat. The detection unit detects whether the belongings have been left on the seat based on the information recognized by the recognition unit. For example, the detection unit can detect whether the belongings have been left on the seat for a certain period of time or longer. The detection unit uses an algorithm to detect the position and movement of the object. The notification unit notifies the owner of the information detected by the detection unit. For example, the notification unit can send a push notification to the passenger's smartphone. The notification unit can also display a message on an in-vehicle display. The notification unit can adjust the timing and means of notification. The guidance unit guides the owner to the location of the belongings based on the information notified by the notification unit. The guidance unit can, for example, give specific instructions to the owner and guide them to the location of the lost item. The guidance unit can guide the owner using audio guidance or visual guides. As a result, the lost item prevention system according to the embodiment can automatically detect items left behind on buses and taxis, and notify and guide the owner using an interactive generation AI, thereby preventing items from being lost.

[0063] The recognition unit can use a camera to analyze the shapes of people and their belongings and detect whether the belongings have been left on the seat. The recognition unit, for example, uses a camera to analyze the shapes of people and their belongings. For example, the recognition unit can recognize belongings such as bags and coats that passengers have placed on their seats. The recognition unit uses image analysis technology to extract shape characteristics of the belongings. For example, the recognition unit can analyze characteristics such as the outline, color, and texture of the belongings. This allows the use of a camera to analyze the shape of the belongings and improve the accuracy of detecting lost items. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the recognition unit can input image data acquired by a camera into a generation AI and have the generation AI perform shape analysis of the belongings from the image data.

[0064] The notification unit can send a push notification to the passenger's smartphone. The notification unit, for example, sends a push notification to the passenger's smartphone. For example, if the notification unit detects a lost item, it can send a message to the passenger's smartphone such as "Your bag has been left on your seat." The notification unit can adjust the trigger conditions for the notification and the format of the notification content. For example, the notification unit can send a notification if it detects that a belonging has been left on a seat for more than a certain period of time. The notification unit can also customize the notification content and send an appropriate message to the passenger. As a result, by sending a push notification to the passenger's smartphone, the passenger can be promptly notified of the lost item. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the lost item detection information to a generation AI and have the generation AI generate the notification content.

[0065] The notification unit can display a message on an in-vehicle display. The notification unit, for example, displays a message on an in-vehicle display. For example, if the notification unit detects a lost item, it can display a message such as "Your bag has been left on the seat" on an in-vehicle display. The notification unit can adjust the display timing and format of the display content. For example, the notification unit can display a message if it detects that a belonging has been left on a seat for more than a certain period of time. The notification unit can also customize the display content and display an appropriate message to passengers. As a result, displaying a message on an in-vehicle display makes it easier for passengers to notice a lost item. Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information about the detection of the lost item to a generation AI and cause the generation AI to generate the display content.

[0066] The guidance unit can issue instructions to the owner and guide them to the location of the lost item. For example, the guidance unit can issue specific instructions to the owner and guide them to the location of the lost item. For example, the guidance unit can issue an instruction such as, "Your bag is in the back seat. Please take it." The guidance unit can adjust the content and means of communication of the instructions. For example, the guidance unit can guide the owner using audio guidance or visual guides. The guidance unit can also update the location information of the belongings in real time and provide an optimal guidance route. For example, if the location of the belongings changes, the location information can be updated in real time and an optimal guidance route can be provided. This allows the owner to be quickly guided to the location of the lost item by issuing specific instructions. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without AI. For example, the guidance unit can input the location information of the belongings into a generation AI and have the generation AI generate a guidance route.

[0067] The recognition unit can use cameras installed on the ceiling of a bus or taxi, or in front of the seats. For example, the recognition unit can use cameras installed on the ceiling of a bus or taxi, or in front of the seats, to recognize passengers and their belongings over a wide area. Alternatively, the recognition unit can use cameras installed in front of the seats to recognize passengers' belongings around their seats in detail. The camera's installation position and method affect the recognition range and accuracy, so they must be selected appropriately. For example, the recognition range can be optimized by adjusting the camera's installation angle and height. This expands the recognition range and improves the accuracy of detecting lost items when using cameras installed on the ceiling or in front of the seats. Some or all of the above processing in the recognition unit may be performed using AI, or not. For example, the recognition unit can input image data acquired by the camera into a generating AI, and have the generating AI perform recognition of belongings from the image data.

[0068] The recognition unit can estimate the passenger's emotions and adjust the recognition accuracy based on the estimated emotions. For example, if the passenger is tense, the recognition unit can increase the camera frame rate to improve recognition accuracy. If the passenger is relaxed, the recognition unit can decrease the frame rate to reduce processing load while maintaining recognition accuracy. Furthermore, if the passenger is in a hurry, the recognition unit can increase the camera resolution to improve recognition accuracy. This improves recognition accuracy by adjusting the recognition accuracy based on the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI or not using AI. For example, the recognition unit can input passenger emotion data into the generative AI and have the generative AI perform emotion-based adjustments to recognition accuracy.

[0069] The recognition unit can optimize its recognition algorithm by referring to the passenger's past travel history during recognition. For example, the recognition unit can prioritize recognizing items that the passenger has frequently used in the past. The recognition unit can also analyze the passenger's past travel history to determine if they tend to forget items at certain times of the day and adjust the recognition accuracy accordingly. Furthermore, the recognition unit can enhance the recognition of specific items based on the passenger's past travel history. As a result, the recognition algorithm is optimized by referring to past travel history, improving recognition accuracy. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the passenger's past travel history data into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0070] The recognition unit can improve recognition accuracy by taking into account the material or color of the passenger's belongings during recognition. For example, the recognition unit can improve recognition accuracy by taking into account the material and color of the passenger's belongings during recognition. For example, if the material of the belongings is metal, the recognition unit can adjust the recognition algorithm by taking into account reflection. Furthermore, if the color of the belongings easily blends in with the background, the recognition unit can improve recognition accuracy by performing color correction. Furthermore, if the material of the belongings is cloth, the recognition unit can optimize the recognition algorithm by taking into account wrinkles and folds. In this way, recognition accuracy is improved by taking into account the material and color of the belongings. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input data on the material and color of the belongings to the generation AI and cause the generation AI to adjust the recognition algorithm.

[0071] The recognition unit can estimate the passenger's emotion and adjust the display method of the recognition result based on the estimated passenger's emotion. For example, the recognition unit can estimate the passenger's emotion and adjust the display method of the recognition result based on the estimated passenger's emotion. For example, if the passenger is nervous, the recognition unit can provide a simple, highly visible display method. If the passenger is relaxed, the recognition unit can provide a display method including detailed information. If the passenger is in a hurry, the recognition unit can provide a display method that focuses on the main points. This improves the visibility of the recognition result by adjusting the display method based on the passenger's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the recognition unit can be performed using AI, for example, or without AI. For example, the recognition unit can input passenger emotion data to the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0072] The recognition unit can improve recognition accuracy based on the passenger's geographical location information during recognition. For example, the recognition unit can improve recognition accuracy based on the passenger's geographical location information during recognition. For example, when a passenger is in a specific area, the recognition unit can prioritize recognizing belongings unique to that area. Furthermore, when a passenger is in a tourist destination, the recognition unit can prioritize recognizing belongings that tourists are likely to carry. Furthermore, when a passenger is in a commercial area, the recognition unit can prioritize recognizing belongings such as shopping bags. This improves recognition accuracy by taking geographical location information into consideration. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input the passenger's geographical location information to the generation AI and cause the generation AI to adjust the recognition algorithm.

[0073] During recognition, the recognition unit can analyze the passenger's social media activity and prioritize recognition of related belongings. For example, during recognition, the recognition unit can analyze the passenger's social media activity and prioritize recognition of related belongings. For example, the recognition unit can analyze photos posted by the passenger on social media to identify the passenger's belongings. The recognition unit can also prioritize recognition of belongings mentioned by the passenger on social media. Furthermore, the recognition unit can prioritize recognition of belongings related to a specific event from the passenger's social media activity. This improves the accuracy of recognition of related belongings by analyzing the social media activity. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the passenger's social media data into a generation AI and have the generation AI recognize the passenger's belongings.

[0074] The detection unit can estimate the passenger's emotions and adjust the detection timing based on the estimated emotions. For example, the detection unit can speed up the detection timing if the passenger is tense. If the passenger is relaxed, the detection unit can maintain the normal detection timing. Furthermore, if the passenger is in a hurry, the detection unit can speed up the detection timing. This improves detection accuracy by adjusting the detection timing based on the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input passenger emotion data into the generative AI and have the generative AI perform the adjustment of the detection timing based on the emotions.

[0075] The detection unit can improve detection accuracy by taking into account the shape or size of the belongings during detection. The detection unit can improve detection accuracy by taking into account the shape and size of the belongings during detection, for example. For example, if the belongings have a complex shape, the detection unit can improve detection accuracy by performing shape analysis. Furthermore, if the belongings are small, the detection unit can improve detection accuracy by enlarging the display. Furthermore, if the shape of the belongings is prone to change, the detection unit can improve detection accuracy by dynamically tracking the shape. This improves detection accuracy by taking into account the shape and size of the belongings. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data on the shape and size of the belongings to the generation AI and cause the generation AI to adjust the detection algorithm.

[0076] The detection unit can optimize the detection algorithm by analyzing the passenger's movement pattern during detection. For example, the detection unit can optimize the detection algorithm by analyzing the passenger's movement pattern during detection. For example, if the passenger moves frequently, the detection unit can analyze the movement pattern and adjust the detection algorithm. Also, if the passenger is stationary, the detection unit can analyze the movement pattern and optimize the detection algorithm. Furthermore, if the passenger performs a specific movement, the detection unit can analyze the movement pattern and optimize the detection algorithm. In this way, by analyzing the movement pattern, the detection algorithm is optimized and detection accuracy is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI or without AI. For example, the detection unit can input the passenger's movement data to a generation AI and cause the generation AI to analyze the movement pattern and optimize the detection algorithm.

[0077] The detection unit can estimate the passenger's emotion and adjust the display method of the detection result based on the estimated passenger's emotion. For example, the detection unit can estimate the passenger's emotion and adjust the display method of the detection result based on the estimated passenger's emotion. For example, if the passenger is nervous, the detection unit can provide a simple, highly visible display method. If the passenger is relaxed, the detection unit can provide a display method including detailed information. If the passenger is in a hurry, the detection unit can provide a display method that focuses on the main points. This improves the visibility of the detection result by adjusting the display method based on the passenger's emotion. Emotion estimation is realized using an emotion estimation function, for example, 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 detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input passenger's emotion data to the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0078] The detection unit can improve detection accuracy by taking into account the weight or material of the belongings during detection. The detection unit can improve detection accuracy by taking into account the weight and material of the belongings during detection, for example. For example, if the belongings are light, the detection unit can adjust the detection algorithm by taking the weight into account. Furthermore, if the belongings are made of metal, the detection unit can adjust the detection algorithm by taking into account reflection. Furthermore, if the belongings are made of cloth, the detection unit can optimize the detection algorithm by taking into account wrinkles and folds. This improves detection accuracy by taking into account the weight and material of the belongings. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data on the weight and material of the belongings into the generation AI and have the generation AI adjust the detection algorithm.

[0079] The detection unit can optimize the detection algorithm by referring to the passenger's past lost item history during detection. For example, the detection unit can optimize the detection algorithm by referring to the passenger's past lost item history during detection. For example, the detection unit can prioritize detecting belongings that the passenger has frequently left behind in the past. The detection unit can also analyze belongings that are likely to be forgotten at specific time periods from the passenger's past lost item history and adjust the detection accuracy. Furthermore, the detection unit can enhance detection of specific belongings based on the passenger's past lost item history. By referring to the past lost item history, the detection algorithm is optimized and the detection accuracy is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input the passenger's past lost item history data into the generation AI and cause the generation AI to optimize the detection algorithm.

[0080] The notification unit can estimate the passenger's emotions and adjust the way the notification is expressed based on the estimated emotions. For example, if the passenger is tense, the notification unit can deliver the notification in a calm tone. If the passenger is relaxed, the notification unit can deliver the notification in a bright tone. Furthermore, if the passenger is in a hurry, the notification unit can deliver a quick and concise notification. This improves the effectiveness of the notification by adjusting the way it is expressed based on the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input passenger emotion data into the generative AI and have the generative AI perform the emotion-based adjustment of the notification expression.

[0081] The notification unit can determine the priority of notifications based on the importance of the belongings when notifying. For example, the notification unit can determine the priority of notifications based on the importance of the belongings when notifying. For example, if the belongings are important documents, the notification unit can give the highest priority to notifications. Furthermore, if the belongings are everyday items, the notification unit can give the highest priority to notifications. Furthermore, if the belongings are valuables, the notification unit can give the highest priority to notifications. In this way, by determining the priority of notifications based on the importance of the belongings, notifications of important belongings are given priority. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the importance data of the belongings to a generation AI and have the generation AI determine the priority of notifications.

[0082] The notification unit can optimize the notification algorithm by referring to the passenger's past notification history when issuing a notification. For example, the notification unit can optimize the notification algorithm by referring to the passenger's past notification history when issuing a notification. For example, the notification unit can suggest an optimal notification method based on the content of notifications that the passenger frequently received in the past. The notification unit can also analyze the passenger's tendency to receive notifications during specific time periods based on the passenger's past notification history and adjust the timing of notifications. Furthermore, the notification unit can enhance notifications regarding specific belongings based on the passenger's past notification history. By referring to the past notification history, the notification algorithm is optimized and the accuracy of notifications is improved. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input the passenger's past notification history data into the generation AI and cause the generation AI to optimize the notification algorithm.

[0083] The notification unit can estimate the passenger's emotion and adjust the timing of the notification based on the estimated passenger's emotion. The notification unit, for example, estimates the passenger's emotion and adjusts the timing of the notification based on the estimated passenger's emotion. For example, the notification unit can advance the timing of the notification if the passenger is nervous. Furthermore, the notification unit can normalize the timing of the notification if the passenger is relaxed. Furthermore, the notification unit can advance the timing of the notification if the passenger is in a hurry. This improves the effectiveness of the notification by adjusting the timing of the notification based on the passenger's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using an AI, for example, or without an AI. For example, the notification unit can input passenger's emotion data into the generation AI and cause the generation AI to adjust the timing of the notification based on the emotion.

[0084] The notification unit can select the optimal notification method based on the passenger's device information when providing notification. For example, the notification unit can select the optimal notification method based on the passenger's device information when providing notification. For example, if the passenger is using a smartphone, the notification unit can send a push notification. Furthermore, if the passenger is using a tablet, the notification unit can provide a notification method optimized for a large screen. Furthermore, if the passenger is using a smartwatch, the notification unit can provide a simple and highly visible notification method. This allows the optimal notification method to be selected by taking device information into consideration, thereby improving the effectiveness of the notification. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the passenger's device information into the generation AI and cause the generation AI to select the optimal notification method.

[0085] The notification unit can analyze the passenger's current activity status when sending a notification and prioritize sending relevant notifications. For example, the notification unit can prioritize sending important notifications if the passenger is traveling. It can also send regular notifications if the passenger is resting. Furthermore, if the passenger is in a meeting, the notification unit can downplay the notification and allow them to review it later. This improves the effectiveness of notifications by prioritizing relevant notifications through analysis of current activity status. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input passenger activity data into a generating AI and have the generating AI determine the priority of relevant notifications.

[0086] The guidance unit can estimate the passenger's emotions and adjust the way in which guidance is expressed based on the estimated passenger's emotions. For example, the guidance unit can estimate the passenger's emotions and adjust the way in which guidance is expressed based on the estimated passenger's emotions. For example, if the passenger is nervous, the guidance unit can provide guidance in a calm tone. If the passenger is relaxed, the guidance unit can provide guidance in a bright tone. If the passenger is in a hurry, the guidance unit can provide quick and concise guidance. This improves the effectiveness of guidance by adjusting the way in which guidance is expressed based on the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the guidance unit can be performed using AI, for example, or without AI. For example, the guidance unit can input passenger's emotion data into the generation AI and cause the generation AI to adjust the guidance expression based on the emotion.

[0087] The guidance unit can update the location information of the belongings in real time during guidance to provide the optimal guidance route. For example, if the location of the belongings changes, the guidance unit can update the location information in real time to provide the optimal guidance route. Furthermore, if the belongings are in motion, the guidance unit can track their location in real time to provide the optimal guidance route. In addition, if the location information of the belongings is unclear, the guidance unit can present multiple candidate routes and allow the user to select the optimal route. This improves the effectiveness of guidance by providing the optimal guidance route through real-time updates of the location information of the belongings. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the location information of the belongings into a generating AI and have the generating AI generate the optimal guidance route.

[0088] The guidance unit can optimize the guidance algorithm by referring to the passenger's past guidance history when guiding. For example, the guidance unit can optimize the guidance algorithm by referring to the passenger's past guidance history when guiding. For example, the guidance unit can propose an optimal guidance route based on guidance routes used by the passenger in the past. The guidance unit can also propose a route that avoids congestion based on the passenger's past guidance history. Furthermore, the guidance unit can analyze the passenger's past guidance history and propose the most efficient guidance route. By referring to the past guidance history, the guidance algorithm is optimized and the accuracy of guidance is improved. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the passenger's past guidance history data into the generation AI and cause the generation AI to optimize the guidance algorithm.

[0089] The guiding unit can estimate the passenger's emotion and adjust the timing of guidance based on the estimated passenger's emotion. The guiding unit, for example, estimates the passenger's emotion and adjusts the timing of guidance based on the estimated passenger's emotion. For example, the guiding unit can advance the timing of guidance if the passenger is nervous. Furthermore, the guiding unit can maintain the normal timing of guidance if the passenger is relaxed. Furthermore, the guiding unit can advance the timing of guidance if the passenger is in a hurry. This improves the effectiveness of guidance by adjusting the timing of guidance based on the passenger's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input passenger's emotion data into the generation AI and cause the generation AI to adjust the timing of guidance based on the emotion.

[0090] The guidance unit can provide an optimal guidance route based on the passenger's geographical location information when guiding. For example, when guiding, the guidance unit can provide an optimal guidance route based on the passenger's geographical location information. For example, if the passenger is in a specific area, the guidance unit can preferentially provide a route specific to that area. Furthermore, if the passenger is in a tourist destination, the guidance unit can provide a route that is easy for tourists to use. Furthermore, if the passenger is in a commercial area, the guidance unit can provide a route that passes through commercial facilities. In this way, by taking the geographical location information into consideration, an optimal guidance route is provided, improving the effectiveness of guidance. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the passenger's geographical location information into a generation AI and cause the generation AI to generate an optimal guidance route.

[0091] The guidance unit can analyze the passenger's current activity status and prioritize providing relevant guidance information when guiding the passenger. For example, the guidance unit can prioritize providing the shortest route when the passenger is traveling. Furthermore, the guidance unit can provide detailed guidance information when the passenger is taking a break. Furthermore, the guidance unit can reduce notifications when the passenger is in a meeting and allow the passenger to check later. This allows the relevant guidance information to be prioritized by analyzing the current activity status, improving the effectiveness of guidance. Some or all of the above-described processing in the guidance unit may be performed using, or without, AI. For example, the guidance unit can input passenger activity status data into a generation AI and have the generation AI determine the priority of relevant guidance information. === Hard Collateral 1-1 === Each of the multiple elements, including the recognition unit, detection unit, notification unit, and guidance unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recognition unit recognizes passengers and their belongings using the camera 42 of the smart device 14, and performs image analysis using the control unit 46A. For example, the detection unit detects that belongings have been left on seats using the specific processing unit 290 of the data processing device 12. For example, the notification unit sends a push notification to the passenger's smartphone using the output device 40 of the smart device 14 and displays a message on the display 40A inside the vehicle. For example, the guidance unit issues specific instructions to the owner using the specific processing unit 290 of the data processing device 12, and guides the owner to the location of the belongings using audio guidance or visual guides. === Hard Collateral 1-2 === Each of the multiple elements, including the recognition unit, detection unit, notification unit, and guidance unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit recognizes passengers and their belongings using the camera 42 of the smart glasses 214, and performs image analysis using the control unit 46A. For example, the detection unit detects that belongings have been left on a seat using the specific processing unit 290 of the data processing device 12. For example, the notification unit sends a push notification to the passenger's smartphone using the speaker 240 of the smart glasses 214 and displays a message on a display inside the vehicle. For example, the guidance unit issues specific instructions to the passenger using the specific processing unit 290 of the data processing device 12, and guides the passenger to the location of the belongings using audio guidance or visual guides. === Hard Collateral 1-3 === Each of the multiple elements including the recognition unit, detection unit, notification unit, and guidance unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the recognition unit recognizes passengers and their belongings using the camera 42 of the headset terminal 314, and performs image analysis using the control unit 46A. For example, the detection unit detects that belongings have been left on seats using the specific processing unit 290 of the data processing device 12. For example, the notification unit sends a push notification to the passenger's smartphone using the speaker 240 of the headset terminal 314 and displays a message on a display inside the vehicle. For example, the guidance unit issues specific instructions to the owner using the specific processing unit 290 of the data processing device 12, and guides the owner to the location of the belongings using audio guidance or visual guides. === Hard Collateral 1-4 === Each of the multiple elements including the recognition unit, detection unit, notification unit, and guidance unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit recognizes passengers and their belongings using the camera 42 of the robot 414, and performs image analysis using the control unit 46A. For example, the detection unit detects that belongings have been left on seats using the specific processing unit 290 of the data processing device 12. For example, the notification unit sends a push notification to the passenger's smartphone using the speaker 240 of the robot 414 and displays a message on a display inside the vehicle. For example, the guidance unit issues specific instructions to the owner using the specific processing unit 290 of the data processing device 12, and guides the owner to the location of the belongings using audio guidance or visual guides.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The recognition unit can improve recognition accuracy by taking into account not only the shape of a passenger's belongings but also the frequency with which the belongings are used. For example, the recognition unit can prioritize recognition of belongings that are frequently used by passengers. The recognition unit can also evaluate the importance of belongings based on the frequency with which the belongings are used, and prioritize recognition of belongings with high importance. Furthermore, the recognition unit can accumulate data on the frequency with which belongings are used and optimize the recognition algorithm based on past data. By taking into account the frequency with which belongings are used, recognition accuracy is improved, making the detection of lost items more effective.

[0094] The notification unit can send notifications not only to passengers' smartphones but also to other devices such as smartwatches and tablets. For example, if a passenger is using a smartwatch, the notification unit can notify them by vibration or sound. Also, if a passenger is using a tablet, the notification unit can display a notification optimized for a large screen. Furthermore, if a passenger is using multiple devices, the notification unit can send the notification to the most appropriate device. This improves the effectiveness of notifications by supporting multiple devices, making it easier for passengers to notice forgotten items.

[0095] The guidance unit can provide a guidance route by taking into account not only the location information of the passenger's belongings but also the passenger's current location information. For example, the guidance unit can determine where the passenger is located in the vehicle and suggest the optimal route. Also, when the passenger is moving, the guidance unit can update the location information in real time and suggest the optimal route. Furthermore, the guidance unit can suggest a route that avoids congestion based on the passenger's location information. In this way, by taking the passenger's location information into consideration, the accuracy of guidance is improved and lost items can be quickly retrieved.

[0096] The recognition unit can improve recognition accuracy by taking into account not only the shape of the passenger's belongings but also the material and color of the belongings. For example, if the belongings are made of metal, the recognition unit can adjust the recognition algorithm to take into account reflection. Also, if the color of the belongings easily blends in with the background, the recognition unit can perform color correction to improve recognition accuracy. Furthermore, if the belongings are made of cloth, the recognition unit can optimize the recognition algorithm to take into account wrinkles and folds. By taking into account the material and color of the belongings, recognition accuracy is improved and left-behind items can be detected more effectively.

[0097] The notification unit can estimate the passenger's emotions and adjust the way the notification is expressed based on the estimated passenger's emotions. For example, if the passenger is nervous, the notification unit can provide the notification in a calm tone. If the passenger is relaxed, the notification unit can provide the notification in a bright tone. Furthermore, if the passenger is in a hurry, the notification unit can provide a quick and concise notification. In this way, adjusting the way the notification is expressed based on the passenger's emotions improves the effectiveness of the notification and makes it easier for passengers to notice their forgotten items.

[0098] The guidance unit can estimate the passenger's emotions and adjust the way in which guidance is expressed based on the estimated passenger's emotions. For example, if the passenger is nervous, the guidance unit can provide guidance in a calm tone. If the passenger is relaxed, the guidance unit can provide guidance in a bright tone. Furthermore, if the passenger is in a hurry, the guidance unit can provide quick and concise guidance. Thus, by adjusting the way in which guidance is expressed based on the passenger's emotions, the effectiveness of guidance is improved, and passengers can quickly retrieve their lost items.

[0099] The recognition unit can optimize the recognition algorithm by referring to the passenger's past riding history. For example, the recognition unit can prioritize recognition of belongings that the passenger has frequently used in the past. The recognition unit can also analyze the passenger's past riding history to determine whether the passenger tends to forget belongings at certain times of the day, and adjust the recognition accuracy. Furthermore, the recognition unit can strengthen recognition of specific belongings based on the passenger's past riding history. In this way, by referring to the passenger's past riding history, the recognition algorithm is optimized and the recognition accuracy is improved.

[0100] The detection unit can estimate the passenger's emotions and adjust the timing of detection based on the estimated passenger's emotions. For example, the detection unit can advance the timing of detection if the passenger is nervous. Also, the detection unit can return the timing of detection to normal if the passenger is relaxed. Furthermore, the detection unit can advance the timing of detection if the passenger is in a hurry. In this way, adjusting the timing of detection based on the passenger's emotions improves detection accuracy and makes the detection of left-behind items more effective.

[0101] When making a notification, the notification unit can determine the priority of the notification based on the importance of the belongings. For example, if the belongings are important documents, the notification unit can give the highest priority to the notification. Also, if the belongings are everyday items, the notification unit can give the notification with normal priority. Furthermore, if the belongings are valuables, the notification unit can give the highest priority to the notification. In this way, by determining the priority of notifications based on the importance of the belongings, notifications for important belongings are given priority, making it easier for passengers to notice forgotten items.

[0102] When guiding a passenger, the guiding unit can provide the optimal guidance route based on the passenger's geographical location information. For example, if the passenger is in a specific area, the guiding unit can provide a route specific to that area with priority. Also, if the passenger is in a tourist spot, the guiding unit can provide a route that is easy for tourists to use. Furthermore, if the passenger is in a commercial area, the guiding unit can provide a route that passes through commercial facilities. In this way, by taking the geographical location information into consideration, the optimal guidance route is provided, the effectiveness of guidance is improved, and passengers can quickly retrieve their lost items.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The recognition unit uses cameras to recognize people and their belongings. The recognition unit uses cameras installed inside, for example, buses or taxis to recognize passengers and their belongings. Cameras may be installed, for example, on the ceiling or in front of the seats. The recognition unit uses image analysis technology to analyze the shape of passengers and their belongings. For example, the recognition unit can recognize belongings such as bags and coats that passengers place on their seats. Step 2: The detection unit detects whether the belongings have been left on the seat based on the information recognized by the recognition unit. For example, the detection unit can detect whether the belongings have been left on the seat for a certain period of time. The detection unit uses an algorithm to detect the position information and movement of the object. Step 3: The notification unit informs the owner of the information detected by the detection unit. The notification unit can, for example, send a push notification to the passenger's smartphone. It can also display a message on the in-vehicle display. The notification unit can adjust the timing and method of notification. Step 4: The guiding unit guides the owner to the location of the lost item based on the information notified by the notification unit. For example, the guiding unit can provide specific instructions to the owner and guide them to the location of the lost item. The guiding unit can guide the owner using audio guidance or visual guides.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0128] 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.

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

[0130] 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).

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

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

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

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0135] 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.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0144] 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.

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

[0146] 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).

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

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 recognition unit that uses a camera to recognize people and their belongings; a detection unit that detects a state in which belongings are left on a seat based on the information recognized by the recognition unit; a notification unit that notifies an owner of information detected by the detection unit; a guidance unit that guides the user to the location of the belongings based on the information notified by the notification unit. A system characterized by:

2. The recognition unit The camera analyzes the shape of people and their belongings and detects whether or not belongings have been left on the seat.

2. The system of claim 1.

3. The notification unit Send push notifications to passengers' smartphones 2. The system of claim 1.

4. The notification unit Display a message on the vehicle's display 2. The system of claim 1.

5. The induction section is Give instructions to the owner and guide them to the location of the lost item 2. The system of claim 1.

6. The recognition unit Using cameras mounted on the ceiling or in front of the seats of buses or taxis 2. The system of claim 1.

7. The recognition unit Estimate passenger emotions and adjust recognition accuracy based on the estimated passenger emotions 2. The system of claim 1.

8. The recognition unit During recognition, the recognition algorithm is optimized by referencing the passenger's past riding history.

2. The system of claim 1.

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