system

Abstract

An object of a system according to an embodiment is to provide accurate position information even in an indoor environment where GPS does not reach.SOLUTION: A system according to an embodiment includes an image acquisition unit, a position identification unit, a map collation unit, and a route guidance unit. The image acquisition unit acquires image data captured by a user. The position identification unit identifies position information by analyzing the image data acquired by the image acquisition unit. The map collator collates the position information specified by the position specifier with indoor map data prepared in advance. The route guidance unit guides a route to a destination based on the position information collated by the map collation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 the problem that it is difficult to provide accurate location information in indoor environments where GPS cannot be received.

[0005] The system according to the embodiment aims to provide accurate location information even in an indoor environment where GPS is not available. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, a position identification unit, a map matching unit, and a route guidance unit. The image acquisition unit acquires image data captured by a user. The position identification unit analyzes the image data acquired by the image acquisition unit to identify position information. The map matching unit matches the position information identified by the position identification unit with indoor map data prepared in advance. The route guidance unit provides guidance on a route to a destination based on the position information matched by the map matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide accurate location information even in indoor environments where GPS is not available. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The location information providing system according to an embodiment of the present invention is a system that uses image analysis technology to provide accurate location information in indoor environments where GPS cannot be received. As a result, the location information providing system can provide accurate location information even in indoor environments, supporting the user's comfortable movement.

[0029] A location information providing system according to an embodiment includes an image acquisition unit, a location identification unit, a map matching unit, and a route guidance unit. The image acquisition unit acquires image data captured by a user. For example, the user may capture images of the surrounding area using a smartphone camera. The image acquisition unit can acquire image data in the form of still images or videos. The location identification unit analyzes the image data acquired by the image acquisition unit to identify location information. For example, the generation AI analyzes image data captured by the user to identify the location of a specific store or landmark. The generation AI can also identify the location by extracting features from the image data and matching them with a pre-registered database. The map matching unit matches the location information identified by the location identification unit with pre-prepared indoor map data. For example, the generation AI matches it with map data of a large commercial facility to identify the user's current location on which floor. The map matching unit can also integrate and match map data corresponding to multiple floors or areas. The route guidance unit provides route guidance to a destination based on the location information matched by the map matching unit. For example, the generation AI calculates the optimal route to a user-specified destination and displays it on a smartphone screen. The route guidance unit can also recalculate the route based on information updated in real time and guide the user. As a result, the location information providing system according to the embodiment can provide accurate location information to the user even in indoor environments where GPS cannot be received, and support comfortable travel. For example, the user can reach their destination without getting lost in a large commercial facility, and can travel smoothly even in an underground shopping mall.

[0030] The image acquisition unit can provide more accurate location information by combining the user's walking pattern and speed information. For example, when a user walks with a smartphone, the image acquisition unit captures images of the surroundings with the camera while acquiring walking pattern and speed information using an acceleration sensor and gyro sensor. The generation AI integrates this data to more accurately identify the user's location. The image acquisition unit can also analyze changes in walking pattern and speed fluctuations in real time to improve the accuracy of location information. For example, if the user suddenly stops or changes direction, the location is recalculated based on that information. In this way, the accuracy of location information is improved by combining walking pattern and speed information.

[0031] The image acquisition unit can identify specific locations more accurately by adding surrounding audio information and performing acoustic analysis. For example, when a user is walking through an underground mall, the image acquisition unit combines image data captured by a camera with surrounding audio information. For example, it can detect music or announcements from a specific store and identify the location based on that information. The image acquisition unit can also use voice recognition technology to analyze surrounding audio information and identify specific locations. For example, it can identify the location of a specific sound source and recalculate the location based on that information. In this way, adding audio information improves the accuracy of the location information.

[0032] The image acquisition unit can combine environmental data such as temperature and humidity to provide location information according to environmental conditions. For example, when a user walks through an underground mall, the image acquisition unit combines image data captured by a camera with data from a temperature sensor and humidity sensor. For example, it can detect temperature changes in a specific area and identify the location based on that information. The image acquisition unit can also analyze humidity changes to provide location information according to environmental conditions. For example, it can identify areas with high or low humidity and recalculate the location based on that information. In this way, the accuracy of location information is improved by combining environmental data.

[0033] The image acquisition unit can combine the user's past movement history and provide location information based on past behavioral patterns. For example, when the user walks through an underground mall, the image acquisition unit combines image data captured by a camera with the user's past movement history. For example, the current location of the user can be determined based on the locations of stores the user previously visited. The image acquisition unit can also analyze past behavioral patterns and provide location information based on the user's frequently visited places and routes. For example, if the user frequently visits a particular store, the location can be recalculated based on that information. In this way, by combining the user's past movement history, the accuracy of the location information can be improved.

[0034] The map matching unit can combine people flow data that is updated in real time and provide location information according to the congestion situation. For example, the map matching unit collects people flow data within a large commercial facility in real time and combines it with indoor map data. For example, it can provide location information that avoids crowded areas. The map matching unit can also analyze people flow data using cameras and sensors to evaluate the degree of congestion. For example, it can measure the density of people in a specific area and recalculate the location based on that information. In this way, by combining people flow data, it can provide location information according to the congestion situation.

[0035] The map matching unit can combine sensor information within the facility and provide location information that corresponds to environmental changes. For example, the map matching unit collects data from lighting sensors and temperature sensors within a large commercial facility and combines it with indoor map data. For example, it can provide location information based on changes in lighting brightness and temperature. The map matching unit can also analyze sensor information in real time and provide location information that corresponds to environmental changes. For example, it can detect fluctuations in illuminance and temperature in a specific area and recalculate the location based on that information. In this way, by combining sensor information, it can provide location information that corresponds to environmental changes.

[0036] The map matching unit can combine event information within a facility and provide specific location information at the time of the event. For example, the map matching unit collects event information within a large commercial facility and combines it with indoor map data. For example, it can provide specific location information at the time of the event. The map matching unit can also update event information in real time and evaluate the impact range of the event. For example, it can analyze the congestion situation and access routes at the event venue and recalculate the location based on that information. In this way, by combining event information, it can provide specific location information at the time of the event.

[0037] The map matching unit can combine advertising information within a facility and provide location information based on the location of the advertisement. For example, the map matching unit collects advertising information within a large commercial facility and combines it with indoor map data. For example, it provides location information based on the location of the advertisement. The map matching unit can also update the advertising information in real time and evaluate the influence range of the advertisement. For example, it can analyze the location of advertising billboards and digital signage and recalculate the location based on that information. In this way, by combining the advertising information, it is possible to provide location information based on the location of the advertisement.

[0038] The route guidance unit can combine the user's walking speed and physical strength information to provide an individually optimized route. For example, when a user moves through a large commercial facility, the route guidance unit combines image data captured by a camera with the user's walking speed. For example, the route guidance unit measures the user's walking speed in real time and provides an optimal route based on that speed. The route guidance unit can also evaluate the user's fatigue level and calorie consumption based on the user's physical strength information and propose an individually optimized route. For example, if the user is tired, the route guidance unit proposes a route that passes through a rest area or cafe. In this way, by combining the walking speed and physical strength information, an individually optimized route can be provided.

[0039] The route guidance unit can provide efficient routes by combining the operation status of elevators and escalators within a facility. The route guidance unit collects information on the operation status of elevators and escalators within a large commercial facility in real time, for example, and reflects this information in route guidance. For example, if an elevator is crowded, it will suggest a route that uses the escalator. The route guidance unit can also recalculate efficient routes based on information on elevator and escalator malfunctions. For example, if an elevator is broken, it will suggest an alternative route. In this way, efficient routes can be provided by combining the operation status of elevators and escalators.

[0040] The route guidance unit can combine the business hours information of stores within the facility and provide a route that matches the business hours. For example, the route guidance unit collects business hours information of stores within a large commercial facility and reflects this in the route guidance. For example, it can propose a route that passes through stores that are open. The route guidance unit can also update the business hours information of stores in real time and recalculate a route that matches the business hours. For example, if a specific store is closed, it can propose a different route. In this way, by combining the business hours information of stores, it is possible to provide a route that matches the business hours.

[0041] The route guidance unit can combine location information of restrooms and rest areas within the facility to provide a comfortable route. The route guidance unit collects location information of restrooms and rest areas within a large commercial facility, for example, and reflects this in route guidance. For example, it can propose a route that passes through restrooms and rest areas. The route guidance unit can also propose a comfortable route based on the available hours and facility information of the restrooms and rest areas. For example, it recalculates the route taking into account the congestion status and type of facilities of the restrooms. In this way, a comfortable route can be provided by combining location information of restrooms and rest areas.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The location information providing system can further include a health management unit that monitors the user's health condition. The health management unit acquires vital data such as the user's heart rate, blood pressure, and body temperature, and evaluates the user's health condition based on this data. For example, if the user's heart rate suddenly rises, the health management unit can send a notification urging the user to take a break. The health management unit can also suggest optimal routes based on the user's health condition. For example, if the user is tired, the health management unit can suggest a route that passes through rest areas and cafes. This makes it possible to support comfortable and safe travel based on the user's health condition.

[0044] The location information providing system may further include a learning unit that learns the user's preferences and interests. The learning unit collects data on places the user has visited in the past and routes selected by the user, and analyzes the user's preferences and interests. For example, if the user frequently visits stores of a particular genre, the learning unit can suggest similar stores based on that information. The learning unit can also suggest optimal routes based on the user's interests. For example, if the user is interested in art galleries, the learning unit can suggest routes that pass through galleries. This makes it possible to provide more personalized location information based on the user's preferences and interests.

[0045] The location information providing system can further combine the user's past purchase history and provide location information based on the purchase history. For example, the system can provide the location information of a specific store based on product information of products the user previously purchased at that store. It can also suggest similar stores based on the location information of stores the user previously visited. This makes it possible to provide more personalized location information based on the user's purchase history.

[0046] The location information providing system can also analyze a user's social media posts and provide location information based on the content of the posts. For example, it can analyze photos and comments posted by a user on social media and suggest places of interest based on the content. It can also provide location information for specific events or stores posted by a user. This allows for more personalized location information to be provided based on the user's social media posts.

[0047] The location information providing system can also combine the user's calendar information to provide location information based on the schedule. For example, it can provide the location information of the destination based on the schedule registered in the user's calendar. It can also suggest the optimal route based on the schedule time. This can support efficient travel based on the user's calendar information.

[0048] The location information providing system can also combine the user's weather information to provide location information according to the weather. For example, if it is raining, it can suggest an indoor route. On a hot day, it can also suggest a route that passes through cooler places. This allows the system to support a comfortable journey based on the user's weather information.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The image acquisition unit acquires image data captured by a user. For example, the user captures an image of the surroundings using a smartphone camera. The image acquisition unit can also acquire image data in the form of a still image or a video. Step 2: The location identification unit analyzes the image data acquired by the image acquisition unit to identify location information. For example, the generation AI analyzes image data taken by the user and identifies the location of a specific store or landmark. The generation AI can also identify the location by extracting features from the image data and comparing them with a pre-registered database. Step 3: The map matching unit compares the location information identified by the location identification unit with pre-prepared indoor map data. For example, the generation AI compares it with map data for a large commercial facility to determine which floor and location the user is currently on. The map matching unit can also integrate and compare map data corresponding to multiple floors or areas. Step 4: The route guidance unit provides guidance on the route to the destination based on the location information collated by the map collation unit. For example, the generation AI calculates the optimal route to the destination specified by the user and displays it on the smartphone screen. The route guidance unit can also recalculate the route based on information updated in real time and provide guidance to the user. Step 5: The system combines information from sensors within the facility and provides location information that adapts to environmental changes. This allows the system to provide accurate location information to users and support comfortable mobility even in indoor environments where GPS is not available.

[0051] (Example 2) The location information providing system according to an embodiment of the present invention is a system that uses image analysis technology to provide accurate location information in indoor environments where GPS cannot be received. As a result, the location information providing system can provide accurate location information even in indoor environments, supporting the user's comfortable movement.

[0052] A location information providing system according to an embodiment includes an image acquisition unit, a location identification unit, a map matching unit, and a route guidance unit. The image acquisition unit acquires image data captured by a user. For example, the user may capture images of the surrounding area using a smartphone camera. The image acquisition unit can acquire image data in the form of still images or videos. The location identification unit analyzes the image data acquired by the image acquisition unit to identify location information. For example, the generation AI analyzes image data captured by the user to identify the location of a specific store or landmark. The generation AI can also identify the location by extracting features from the image data and matching them with a pre-registered database. The map matching unit matches the location information identified by the location identification unit with pre-prepared indoor map data. For example, the generation AI matches it with map data of a large commercial facility to identify the user's current location on which floor. The map matching unit can also integrate and match map data corresponding to multiple floors or areas. The route guidance unit provides route guidance to a destination based on the location information matched by the map matching unit. For example, the generation AI calculates the optimal route to a user-specified destination and displays it on a smartphone screen. The route guidance unit can also recalculate the route based on information updated in real time and guide the user. As a result, the location information providing system according to the embodiment can provide accurate location information to the user even in indoor environments where GPS cannot be received, and support comfortable travel. For example, the user can reach their destination without getting lost in a large commercial facility, and can travel smoothly even in an underground shopping mall.

[0053] The image acquisition unit can provide more accurate location information by combining the user's walking pattern and speed information. For example, when a user walks with a smartphone, the image acquisition unit captures images of the surroundings with the camera while acquiring walking pattern and speed information using an acceleration sensor and gyro sensor. The generation AI integrates this data to more accurately identify the user's location. The image acquisition unit can also analyze changes in walking pattern and speed fluctuations in real time to improve the accuracy of location information. For example, if the user suddenly stops or changes direction, the location is recalculated based on that information. In this way, the accuracy of location information is improved by combining walking pattern and speed information.

[0054] The image acquisition unit can identify specific locations more accurately by adding surrounding audio information and performing acoustic analysis. For example, when a user is walking through an underground mall, the image acquisition unit combines image data captured by a camera with surrounding audio information. For example, it can detect music or announcements from a specific store and identify the location based on that information. The image acquisition unit can also use voice recognition technology to analyze surrounding audio information and identify specific locations. For example, it can identify the location of a specific sound source and recalculate the location based on that information. In this way, adding audio information improves the accuracy of the location information.

[0055] The image acquisition unit estimates the user's emotions, and the route guidance unit can suggest a less stressful route based on the emotion estimation. The image acquisition unit, for example, analyzes image data captured by a camera when the user is walking through an underground mall and estimates the user's emotions from the user's facial expressions. For example, if the user is tired, the image acquisition unit can suggest a route to a rest area or cafe. The image acquisition unit can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate the user's emotions. The route guidance unit suggests a less stressful route for the user based on the emotion estimation. For example, it can select a route that avoids crowded areas or a quiet route. This makes it possible to suggest a less stressful route based on the user's emotions.

[0056] The image acquisition unit can combine environmental data such as temperature and humidity to provide location information according to environmental conditions. For example, when a user walks through an underground mall, the image acquisition unit combines image data captured by a camera with data from a temperature sensor and humidity sensor. For example, it can detect temperature changes in a specific area and identify the location based on that information. The image acquisition unit can also analyze humidity changes to provide location information according to environmental conditions. For example, it can identify areas with high or low humidity and recalculate the location based on that information. In this way, the accuracy of location information is improved by combining environmental data.

[0057] The image acquisition unit can combine the user's past movement history and provide location information based on past behavioral patterns. For example, when the user walks through an underground mall, the image acquisition unit combines image data captured by a camera with the user's past movement history. For example, the current location of the user can be determined based on the locations of stores the user previously visited. The image acquisition unit can also analyze past behavioral patterns and provide location information based on the user's frequently visited places and routes. For example, if the user frequently visits a particular store, the location can be recalculated based on that information. In this way, by combining the user's past movement history, the accuracy of the location information can be improved.

[0058] The image acquisition unit can estimate the user's emotions and provide location information according to the emotions. For example, when the user is walking through an underground mall, the image acquisition unit analyzes image data captured by a camera and estimates the user's emotions from the user's facial expressions. For example, if the user is tired, the image acquisition unit can provide location information of rest areas and cafes. The image acquisition unit can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate the user's emotions. This makes it possible to provide location information according to the user's emotions.

[0059] The map matching unit can combine people flow data that is updated in real time and provide location information according to the congestion situation. For example, the map matching unit collects people flow data within a large commercial facility in real time and combines it with indoor map data. For example, it can provide location information that avoids crowded areas. The map matching unit can also analyze people flow data using cameras and sensors to evaluate the degree of congestion. For example, it can measure the density of people in a specific area and recalculate the location based on that information. In this way, by combining people flow data, it can provide location information according to the congestion situation.

[0060] The map matching unit can combine sensor information within the facility and provide location information that corresponds to environmental changes. For example, the map matching unit collects data from lighting sensors and temperature sensors within a large commercial facility and combines it with indoor map data. For example, it can provide location information based on changes in lighting brightness and temperature. The map matching unit can also analyze sensor information in real time and provide location information that corresponds to environmental changes. For example, it can detect fluctuations in illuminance and temperature in a specific area and recalculate the location based on that information. In this way, by combining sensor information, it can provide location information that corresponds to environmental changes.

[0061] The map matching unit can optimize indoor map data to suggest a comfortable route based on the user's emotions. For example, when a user is walking through an underground mall, the map matching unit analyzes image data captured by a camera and estimates the user's emotions from their facial expressions. For example, if the user is tired, the map matching unit suggests a route to a rest area or a cafe. The map matching unit also uses the emotion estimation function to optimize indoor map data to suggest a comfortable route based on the user's emotions. For example, a route that avoids crowds or a quiet route can be selected. This makes it possible to suggest a comfortable route based on the user's emotions.

[0062] The map matching unit can combine event information within a facility and provide specific location information at the time of the event. For example, the map matching unit collects event information within a large commercial facility and combines it with indoor map data. For example, it can provide specific location information at the time of the event. The map matching unit can also update event information in real time and evaluate the impact range of the event. For example, it can analyze the congestion situation and access routes at the event venue and recalculate the location based on that information. In this way, by combining event information, it can provide specific location information at the time of the event.

[0063] The map matching unit can combine advertising information within a facility and provide location information based on the location of the advertisement. For example, the map matching unit collects advertising information within a large commercial facility and combines it with indoor map data. For example, it provides location information based on the location of the advertisement. The map matching unit can also update the advertising information in real time and evaluate the influence range of the advertisement. For example, it can analyze the location of advertising billboards and digital signage and recalculate the location based on that information. In this way, by combining the advertising information, it is possible to provide location information based on the location of the advertisement.

[0064] The map matching unit can optimize indoor map data to suggest interesting places based on the user's emotions. For example, the map matching unit analyzes image data captured by a camera when the user is walking through an underground mall and estimates the user's emotions from the user's facial expressions. For example, the map matching unit suggests stores and tourist spots that the user may be interested in. The map matching unit also uses an emotion estimation function to optimize indoor map data to suggest interesting places based on the user's emotions. For example, the map matching unit can select places where the user can relax or places that interest the user. This makes it possible to suggest interesting places based on the user's emotions.

[0065] The route guidance unit can combine the user's walking speed and physical strength information to provide an individually optimized route. For example, when a user moves through a large commercial facility, the route guidance unit combines image data captured by a camera with the user's walking speed. For example, the route guidance unit measures the user's walking speed in real time and provides an optimal route based on that speed. The route guidance unit can also evaluate the user's fatigue level and calorie consumption based on the user's physical strength information and propose an individually optimized route. For example, if the user is tired, the route guidance unit proposes a route that passes through a rest area or cafe. In this way, by combining the walking speed and physical strength information, an individually optimized route can be provided.

[0066] The route guidance unit can provide efficient routes by combining the operation status of elevators and escalators within a facility. The route guidance unit collects information on the operation status of elevators and escalators within a large commercial facility in real time, for example, and reflects this information in route guidance. For example, if an elevator is crowded, it will suggest a route that uses the escalator. The route guidance unit can also recalculate efficient routes based on information on elevator and escalator malfunctions. For example, if an elevator is broken, it will suggest an alternative route. In this way, efficient routes can be provided by combining the operation status of elevators and escalators.

[0067] The route guidance unit can suggest a less stressful route based on the user's emotions. For example, when the user is walking through an underground mall, the route guidance unit analyzes image data captured by a camera and infers the user's emotions from the user's facial expressions. For example, if the user is feeling stressed, the route guidance unit suggests a route that avoids crowded areas. The route guidance unit can also use voice analysis technology to analyze the tone and speed of the user's voice and infer their emotions. This makes it possible to suggest a less stressful route based on the user's emotions.

[0068] The route guidance unit can combine the business hours information of stores within the facility and provide a route that matches the business hours. For example, the route guidance unit collects business hours information of stores within a large commercial facility and reflects this in the route guidance. For example, it can propose a route that passes through stores that are open. The route guidance unit can also update the business hours information of stores in real time and recalculate a route that matches the business hours. For example, if a specific store is closed, it can propose a different route. In this way, by combining the business hours information of stores, it is possible to provide a route that matches the business hours.

[0069] The route guidance unit can combine location information of restrooms and rest areas within the facility to provide a comfortable route. The route guidance unit collects location information of restrooms and rest areas within a large commercial facility, for example, and reflects this in route guidance. For example, it can propose a route that passes through restrooms and rest areas. The route guidance unit can also propose a comfortable route based on the available hours and facility information of the restrooms and rest areas. For example, it recalculates the route taking into account the congestion status and type of facilities of the restrooms. In this way, a comfortable route can be provided by combining location information of restrooms and rest areas.

[0070] The route guidance unit can suggest a route that passes through places where the user can relax based on the user's emotions. For example, the route guidance unit analyzes image data captured by a camera when the user is walking through an underground mall and infers the user's emotions from the user's facial expressions. For example, the route guidance unit suggests a route that passes through cafes and rest areas where the user can relax. The route guidance unit can also use voice analysis technology to analyze the tone and speed of the user's voice and infer emotions. This makes it possible to suggest a route that passes through places where the user can relax based on the user's emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The location information providing system can further include a health management unit that monitors the user's health condition. The health management unit acquires vital data such as the user's heart rate, blood pressure, and body temperature, and evaluates the user's health condition based on this data. For example, if the user's heart rate suddenly rises, the health management unit can send a notification urging the user to take a break. The health management unit can also suggest optimal routes based on the user's health condition. For example, if the user is tired, the health management unit can suggest a route that passes through rest areas and cafes. This makes it possible to support comfortable and safe travel based on the user's health condition.

[0073] The location information providing system may further include a learning unit that learns the user's preferences and interests. The learning unit collects data on places the user has visited in the past and routes selected by the user, and analyzes the user's preferences and interests. For example, if the user frequently visits stores of a particular genre, the learning unit can suggest similar stores based on that information. The learning unit can also suggest optimal routes based on the user's interests. For example, if the user is interested in art galleries, the learning unit can suggest routes that pass through galleries. This makes it possible to provide more personalized location information based on the user's preferences and interests.

[0074] The location information providing system may further include a music providing unit that estimates the user's emotions and provides music based on the estimated emotions. The music providing unit estimates the user's emotions from their facial expressions and tone of voice, and selects and plays music that matches those emotions. For example, if the user is feeling stressed, relaxing music may be provided. If the user is feeling energetic, energetic music may be provided. This allows the system to provide appropriate music based on the user's emotions, improving the user's mood while traveling.

[0075] The location information providing system may further include a notification unit that estimates the user's emotions and customizes notifications based on the estimated emotions. The notification unit estimates the user's emotions from their facial expressions and tone of voice, and sends notifications according to those emotions. For example, if the user is tired, the notification unit may send a notification encouraging the user to take a break. If the user is excited, the notification unit may provide information about interesting events or stores. This makes it possible to provide appropriate information at the appropriate time based on the user's emotions.

[0076] The location information providing system may further include a lighting adjustment unit that estimates the user's emotions and adjusts the lighting based on the estimated emotions. The lighting adjustment unit estimates the user's emotions from their facial expressions and tone of voice, and adjusts the surrounding lighting according to the emotions. For example, if the user wants to relax, the lighting may be changed to a warmer color. Alternatively, if the user wants to concentrate, the lighting may be brighter. This makes it possible to provide an optimal lighting environment based on the user's emotions.

[0077] The location information providing system may further include a scent providing unit that estimates the user's emotions and provides a scent based on the estimated emotions. The scent providing unit estimates the user's emotions from their facial expressions and tone of voice, and selects and provides a scent that matches the emotion. For example, if the user wants to relax, a lavender scent can be provided. Alternatively, if the user wants to feel energized, a citrus scent can be provided. This allows the system to provide an appropriate scent based on the user's emotions, improving their mood while traveling.

[0078] The location information providing system can further combine the user's past purchase history and provide location information based on the purchase history. For example, the system can provide the location information of a specific store based on product information of products the user previously purchased at that store. It can also suggest similar stores based on the location information of stores the user previously visited. This makes it possible to provide more personalized location information based on the user's purchase history.

[0079] The location information providing system can also analyze a user's social media posts and provide location information based on the content of the posts. For example, it can analyze photos and comments posted by a user on social media and suggest places of interest based on the content. It can also provide location information for specific events or stores posted by a user. This allows for more personalized location information to be provided based on the user's social media posts.

[0080] The location information providing system can also combine the user's calendar information to provide location information based on the schedule. For example, it can provide the location information of the destination based on the schedule registered in the user's calendar. It can also suggest the optimal route based on the schedule time. This can support efficient travel based on the user's calendar information.

[0081] The location information providing system can also combine the user's weather information to provide location information according to the weather. For example, if it is raining, it can suggest an indoor route. On a hot day, it can also suggest a route that passes through cooler places. This allows the system to support a comfortable journey based on the user's weather information.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The image acquisition unit acquires image data captured by a user. For example, the user captures an image of the surroundings using a smartphone camera. The image acquisition unit can also acquire image data in the form of a still image or a video. Step 2: The location identification unit analyzes the image data acquired by the image acquisition unit to identify location information. For example, the generation AI analyzes image data taken by the user and identifies the location of a specific store or landmark. The generation AI can also identify the location by extracting features from the image data and comparing them with a pre-registered database. Step 3: The map matching unit compares the location information identified by the location identification unit with pre-prepared indoor map data. For example, the generation AI compares it with map data for a large commercial facility to determine which floor and location the user is currently on. The map matching unit can also integrate and compare map data corresponding to multiple floors or areas. Step 4: The route guidance unit provides guidance on the route to the destination based on the location information collated by the map collation unit. For example, the generation AI calculates the optimal route to the destination specified by the user and displays it on the smartphone screen. The route guidance unit can also recalculate the route based on information updated in real time and provide guidance to the user. Step 5: The system combines information from sensors within the facility and provides location information that adapts to environmental changes. This allows the system to provide accurate location information to users and support comfortable mobility even in indoor environments where GPS is not available.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image acquisition unit that acquires image data captured by a user; a position specifying unit that specifies position information by analyzing the image data acquired by the image acquisition unit; a map comparison unit that compares the location information identified by the location identification unit with indoor map data prepared in advance; a route guidance unit that provides guidance on a route to a destination based on the location information collated by the map collation unit; Combining sensor information within the facility to provide location information according to environmental changes A system characterized by:

2. The image acquisition unit By combining the user's walking pattern and speed information, it provides more accurate location information.

2. The system of claim 1.

3. The image acquisition unit Combines environmental data such as temperature and humidity to provide location information according to environmental conditions 2. The system of claim 1.

4. The map matching unit Combining real-time updated people flow data to provide location information according to congestion levels 2. The system of claim 1.

5. The route guidance unit Combines user walking speed and physical strength information to provide individually optimized routes 2. The system of claim 1.

6. The image acquisition unit The route guidance unit estimates the user's emotion. Suggesting a less stressful route based on emotion estimation 2. The system of claim 1.

7. The map matching unit Optimizing indoor map data to suggest comfortable routes based on user sentiment 2. The system of claim 1.

8. The route guidance unit Suggesting routes that pass through relaxing places based on the user's emotions 2. The system of claim 1.

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