system

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring current locations of users using QR codes or NFC tags installed at a plurality of positions in a station yard; means for transmitting the current location information and destination information input by the users to a server; means for the server to calculate an optimal route based on the current location information and the destination information; means for the server to generate a route guidance message using a generation AI; and means for the server to transmit the route guidance message to terminals of the users.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] Large urban stations are complex and easy to get lost in, especially for first-time visitors and tourists. This can take unnecessary time to reach your destination and can be stressful. Traditional paper maps and guide signs are not intuitive and difficult to use, posing a major barrier, especially for foreign tourists and people who are not familiar with maps. There is a need for a system that can solve these issues and provide easy and accurate routes to your destination. [Means for solving the problem]

[0005] This invention provides a system that acquires a user's current location using a QR code (registered trademark) or NFC tag and transmits that information, along with the destination information entered by the user, to a server. The server calculates the optimal route based on the received current location and destination information, and generates a route guidance message using a generation AI. The generated guidance message is sent to the user's device, allowing the user to refer to it and reach their destination easily and accurately. This enables users to move around complex stations without getting lost, reducing stress for the user.

[0006] A "QR code" is a type of two-dimensional barcode that expresses information using black and white square modules.

[0007] An "NFC tag" is a small electronic device that can acquire or write information using non-contact wireless communication.

[0008] "Current location information" refers to the location information of the location obtained by the user using a QR code or NFC tag.

[0009] "Destination information" is information relating to a desired destination point specified by the user within the system.

[0010] A "server" is a central processing unit that provides services to computers and terminals on a network.

[0011] "Calculating the optimal route" refers to the process of using an algorithm to determine the shortest and most efficient route from the current location to the destination.

[0012] "Generative AI" refers to artificial intelligence technology that generates sentences in natural language based on specific data.

[0013] The "route guidance message" is guidance information provided in the form of text and illustrations to explain to the user the route from the current location to the destination in an easy-to-understand manner.

[0014] "Terminal" refers to a smartphone or dedicated device that a user uses to read a QR code or NFC tag.

[0015] "Real-time route guidance" refers to a feature that constantly updates routes and provides appropriate guidance while the user is on the move. [Brief explanation of the drawings]

[0016] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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, a 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), and an APU (Accelerated Processing Unit).

[0020] 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.

[0021] 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.

[0022] 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), Bluetooth (registered trademark), etc.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] 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.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention relates to a navigation system that enables users to reach their destinations without getting lost in a complex and vast station premises. Specific embodiments of this system will be described below.

[0038] System configuration

[0039] The system consists of a station where QR codes or NFC tags are installed, a terminal for reading them, and a server that generates route guidance. The terminal is a smartphone or a dedicated reading device. The server is located in the cloud and processes information in real time.

[0040] Operational Overview

[0041] 1. The user scans a QR code or NFC tag

[0042] The user scans QR codes or NFC tags installed at various points within the station with their device, which then acquires their current location information.

[0043] 2. The device sends the current location and destination information to the server.

[0044] The device sends the acquired current location information and the destination information entered by the user to the server, which then receives all the necessary information.

[0045] 3. The server calculates the optimal route and generates a route guidance message.

[0046] The server calculates the optimal route based on the received current location and destination information, referencing the station's map data. Based on the calculation results, the generation AI generates a route guidance message in natural language that is easy for the user to understand. For example, a message might say, "Go to Exit 5, take the escalator and go through the underground passage, and you will arrive at the north exit."

[0047] 4. The server generates a route guidance message and sends it to the terminal.

[0048] The generated message and accompanying illustrated maps and guide maps are sent from the server to the user's terminal.

[0049] 5. The device displays a route guidance message.

[0050] The user's device will display the received route guidance message, allowing the user to easily understand what to do next.

[0051] Specific examples

[0052] For example, suppose tourist B from overseas arrives at a station in a large city in Japan for the first time and is looking for the tourist information center on the other side of the station as his destination.

[0053] First, Person B uses his smartphone to scan the QR code on a guide board inside the station. Person B's device acquires the current location ID "east_exit_01." Next, Person B enters "Tourist Information Center" as his destination on the device screen and presses the send button.

[0054] The device sends its current location and destination information to the server. The server receives the information "east_exit_01" and "tourist information center" and calculates the optimal route based on map data within the station. The AI ​​then generates a route guidance message and illustrated map, saying, "Exit the east exit, turn left, and go straight through the underground passage. You will see the tourist information center on your right."

[0055] The server sends the generated message and map to Person B's smartphone. Person B checks the message on the screen and follows the instructions to safely arrive at the tourist information center.

[0056] This system helps users reach their destinations easily and accurately, making it particularly useful in large, complex station premises.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user scans a QR code or NFC tag in the station.

[0060] The user holds their smartphone close to a QR code or NFC tag installed in the station and reads it. At this time, the device acquires the information from the QR code or NFC tag.

[0061] Step 2:

[0062] The terminal acquires current location information and prompts the user to input destination information.

[0063] The device analyzes and obtains the current location ID information from the QR code or NFC tag, and at the same time displays a destination input form on the device screen, prompting the user to enter the destination information.

[0064] Step 3:

[0065] The device sends current location information and destination information to the server.

[0066] After the user inputs the destination information, the device sends the current location information and the input destination information to the server as a single request data. This data includes, for example, "currentLocationId: 'east_exit_01', destination: 'Tourist Information Center'".

[0067] Step 4:

[0068] The server receives the request data and identifies the current location and destination.

[0069] The server receives the data sent from the terminal and identifies the current location from the station map database based on the current location ID. It also identifies the destination location in the map database based on the destination information.

[0070] Step 5:

[0071] The server calculates the optimal route.

[0072] The server uses the current and destination locations to run a route planning algorithm within the station to calculate the optimal route, taking into account obstacles, restricted areas, and current pedestrian flow data.

[0073] Step 6:

[0074] The server generates a route guidance message using the generation AI.

[0075] The server inputs the calculated route information into the generation AI, which then generates a route guidance message in natural language that is easy for users to understand. For example, a message such as, "Exit the east exit, turn left, and go straight through the underground passage; you will see the tourist information center on your right."

[0076] Step 7:

[0077] The server sends the generated guidance message and illustrated map to the terminal.

[0078] The server sends the route guidance message created by the AI ​​and related illustrated map data to the device.

[0079] Step 8:

[0080] The terminal displays an information message and an illustrated map.

[0081] The device displays the received route guidance message and illustrated map on the screen, and the user can check them and follow the instructions to head to their destination.

[0082] Step 9:

[0083] The user follows the guidance to reach the destination.

[0084] The user follows the route in real time while looking at the guidance messages and illustrated map on the device, and safely reaches the destination.

[0085] Example 1

[0086] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0087] There is a need to provide navigation that will enable users to quickly and accurately reach their destinations within vast and complex station premises without getting lost. Conventional guidance systems make it difficult for users to grasp their exact current location or the appropriate route. Furthermore, because they do not provide guidance based on information updated in real time, they are unable to respond when routes change due to construction, events, etc. Therefore, a new system is needed to solve these issues.

[0088] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0089] In this invention, the server includes means for acquiring the user's current location using codes or tags installed at multiple locations within the station, means for transmitting the current location information and destination information entered by the user to the processing device, means for the processing device to calculate an optimal route based on the current location information and the destination information, means for generating a route guidance message using the generation system, means for transmitting the generated route guidance message to the user's display device, and means for the display device to display the route guidance message. This allows users to easily and quickly check their current location and destination and receive optimal route guidance in real time.

[0090] "Code" refers to QR codes or other similar technology that are placed at various locations within the station for reading by passenger devices.

[0091] "Tag" refers to an NFC tag or other similar technology that is placed at various locations within a station to be read by a passenger's device.

[0092] "Current location information" refers to information indicating the user's current location, which is obtained by the user reading codes or tags installed at various locations within the station.

[0093] "Destination information" refers to information about a desired location that is specified by a user by inputting it into a terminal.

[0094] The "processing device" refers to a computer system or server that receives current location information and destination information and calculates the optimal route based on that information.

[0095] "Generation system" refers to artificial intelligence (AI) that generates easy-to-understand route guidance messages in natural language based on optimal route information calculated by a processing device.

[0096] "Route Guidance Message" means a message containing instructions regarding the optimal route from a current location to a destination, provided in a format that is easy for a user to understand.

[0097] "Display device" refers to a device, primarily a smartphone or dedicated terminal, that receives the route guidance message generated by the generation system and displays it to the user.

[0098] The present invention relates to a navigation system that enables users to reach their destinations without getting lost in a complex and vast station premises. Specific embodiments of this system will be described below.

[0099] System configuration

[0100] This system consists of codes or tags installed in stations, terminals for reading them, and a processing device (server) that generates route guidance. The terminals are smartphones or dedicated reading devices, and the processing device is often located on the cloud. Cloud computing platforms, for example, can be used as cloud services.

[0101] Program processing

[0102] 1. The user reads the code or tag

[0103] Users use their smartphones or other dedicated devices to scan codes or tags placed at various locations within the station, which allows the device to acquire information about the user's current location.

[0104] 2. The device sends the current location and destination information to the server.

[0105] The user enters destination information into their smartphone and presses the send button. The device then sends the current location and destination information to the server. This process is carried out via a dedicated application or browser on the device.

[0106] 3. The server calculates the optimal route and generates a route guidance message.

[0107] The server calculates the optimal route based on the received current location information and destination information, for example, using a map platform API. Furthermore, it generates an easy-to-understand route guidance message in natural language using a generative AI model (for example, OpenAI's GPT-4 (registered trademark)). An example of a prompt used in this case is shown below.

[0108] Current location: east_exit_01

[0109] Destination: Tourist Information Center

[0110] Please provide natural language guidance on the best way to get from your current location to your destination, using a large map of the station. For example, please include exit numbers, instructions on how to use escalators, directions to underground passages, etc. Also, please generate an easy-to-understand illustrated map.

[0111] Based on this prompt, the server generates a specific route guidance message such as, "Exit the east exit, turn left, go straight through the underground passage and you will see the tourist information center on your right."

[0112] 4. The server generates a route guidance message and sends it to the terminal.

[0113] The generated route guidance message and illustrated map are sent from the server to the user's device using cloud communication technology, such as a cloud messaging service.

[0114] 5. The device displays a route guidance message.

[0115] The user's device displays the received route guidance message, allowing the user to understand what to do next and reach their destination.

[0116] Usage example

[0117] For example, consider the case where a tourist is looking for a tourist information center at a train station in a large city they are visiting for the first time. The user scans a QR code installed near the east exit of the station with their smartphone and obtains the current location ID "east_exit_01." Next, they enter the destination information "tourist information center" and submit it. The server calculates the optimal route based on this information and uses a generative AI model to generate a message that says, "Exit the east exit, turn left, and go straight through the underground passage; you will see the tourist information center on your right," which is sent to the user's device. The user follows this message and arrives safely at their destination.

[0118] This system allows users to reach their destinations quickly and without getting lost even in vast and complex station premises.

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] Step 1:

[0121] The user scans a code or tag inside the station.

[0122] Users use their smartphones or dedicated devices to scan codes and tags placed throughout the station to obtain location information. Specifically, a QR code reader app or NFC-enabled device is used. The input is the identification information embedded in the code or tag, and the output is a location ID such as "east_exit_01" sent to the device.

[0123] Step 2:

[0124] The device sends current location information and destination information to the server.

[0125] The user enters destination information into the terminal and presses the send button. The terminal then sends the acquired current location information and the destination information entered by the user to the server via the Internet. The inputs include the current location ID and destination information (e.g., "tourist information center"), and the output is this information sent to the server.

[0126] Step 3:

[0127] The server calculates the optimal route and generates a route guidance message.

[0128] The server calculates the optimal route by referencing map data based on the received current location information and destination information. For example, a map data API is used to calculate the route, and a prompt sentence is entered into a generative AI model (e.g., GPT-4) to generate a route guidance message. The input includes the current location ID and destination information, and data processing involves route calculation and natural language generation by the generative AI model. The output is a route guidance message such as, "Exit the east exit, turn left, and go straight through the underground passage; you will see the tourist information center on your right."

[0129] Step 4:

[0130] The server sends the generated route guidance message to the terminal.

[0131] The server sends the generated route guidance message and the illustrated map to the user's terminal via the Internet. The input is the route guidance message and the accompanying illustrated map, and the output is sent to the user's terminal.

[0132] Step 5:

[0133] The device displays a route guidance message.

[0134] The terminal displays the received route guidance message and illustrated map. By referring to this, the user can intuitively understand how to get to their destination. The input is the route guidance message and illustrated map sent from the server, and the output is this information displayed on the terminal screen. In concrete terms, the message and map are displayed on the terminal screen, and the user checks them before taking action.

[0135] (Application example 1)

[0136] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0137] In large, complex commercial facilities and large stores, users often get lost before reaching their destination. This complexity significantly reduces user convenience, especially for first-time visitors or those looking for a specific product or section. Furthermore, the lack of real-time guidance makes it difficult to navigate in a facility based on the current situation. For this reason, there is a need for an effective navigation system that can help users reach their destination smoothly.

[0138] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0139] In this invention, the server includes means for acquiring a user's current location using QR codes or NFC tags installed at multiple locations within the commercial facility, means for transmitting the current location information and destination information entered by the user to the server, means for the server to calculate an optimal route based on the current location information and destination information, means for the server to generate a route guidance message using a generation AI, means for the server to transmit the route guidance message to the user's terminal, and means for the terminal to display the route guidance message, thereby enabling users to quickly and accurately reach their destination within the commercial facility without getting lost.

[0140] "Commercial facility" refers to a large building or area that houses multiple stores and related facilities offering goods or services.

[0141] "QR Code" is a type of two-dimensional barcode that is a rectangular code that can efficiently store information and be quickly read.

[0142] An "NFC tag" is an electronic tag that uses Near Field Communication technology to read and write information without contact.

[0143] "User" means a customer who visits a commercial establishment and uses goods or services.

[0144] "Current location information" refers to information about the user's current location, obtained by reading a QR code or NFC tag.

[0145] "Destination information" refers to information about a specific location or store that a user wants to reach within a commercial facility.

[0146] A "server" is a computer system that communicates with multiple terminals via a network and stores and processes data.

[0147] The "optimal route" refers to the most efficient and shortest route between the current location and the destination.

[0148] "Generative AI" refers to a system that uses artificial intelligence technology to generate messages and information based on natural language.

[0149] A "route guidance message" is a message that provides the user with easy-to-understand guidance on how to reach a destination.

[0150] "Terminal" refers to a device used by a user, such as a mobile information device or smartphone.

[0151] The present invention relates to a navigation system for assisting users in smoothly reaching their destinations in commercial facilities. Specific embodiments of this system will be described below.

[0152] System Configuration

[0153] This system consists of QR codes or NFC tags installed within the commercial facility, a user's device to read them, and a server to process the data.User devices are typically smartphones, and QR codes or NFC tags are installed at multiple points within the facility, such as at the entrance and near the escalators on each floor.

[0154] Operational Overview

[0155] 1. Reading a QR code or NFC tag

[0156] Users can obtain location information by scanning QR codes or NFC tags in commercial facilities with their smartphones. QR codes are read using the smartphone's camera and the ZXing library, while NFC tags are read using the smartphone's NFC reader and the ANDROID (registered trademark) NFC API.

[0157] 2. Sending current location and destination information

[0158] The user enters the name of the desired store or department in the application and presses the send button. The device then sends the acquired current location information and the entered destination information to the server. The server receives the current location information and destination information via API communication and processes the data.

[0159] 3. Calculating the optimal route and generating guidance messages

[0160] The server refers to the commercial facility's map data based on the received current location information and destination information and calculates the optimal route. This route calculation uses the A algorithm. After calculating the optimal route, it uses the generative AI model GPT-4 to generate an easy-to-understand route guidance message in natural language. The following is used as an example of a prompt sentence.

[0161] Prompt Sentence Examples

[0162] "Generate a guidance message for when a user scans a QR code at the entrance of a commercial facility and enters the women's clothing department as the destination. Starting point: Entrance Arrival point: Women's clothing department Optimal route based on map information: Turn right after entering the entrance, take the escalator to the second floor, and go straight. You will see the women's clothing department on your left."

[0163] 4. Sending a message to the device

[0164] The generated route guidance message, illustrated map, and guide map are sent from the server to the user's device. The message data is sent via API communication and displayed on the user's smartphone.

[0165] 5. Display of route guidance messages

[0166] The user's device displays the received route guidance message. Based on the message and map displayed on the device, the user can navigate to their destination. This display is done using React Native, a mobile application framework.

[0167] Specific examples

[0168] For example, consider the case where Person A is looking for the "women's clothing section" at a large shopping mall that she is visiting for the first time. Person A scans the QR code at the entrance, enters "women's clothing section" into the application, and submits it. Based on this information, the server generates a guidance message and map that says, "Enter the entrance, turn right, take the escalator to the second floor, and go straight; you will see the women's clothing section on your left," and sends this to Person A's smartphone. Person A can follow the guidance and arrive at the women's clothing section without getting lost.

[0169] This system allows users to reach their destinations efficiently and accurately even within large, complex commercial facilities.

[0170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0171] Step 1:

[0172] A user scans a QR code or NFC tag in a commercial facility with their smartphone. This allows the current location information (for example, location information near the entrance) to be obtained using the camera and ZXing library in the case of a QR code, or the NFC reader and Android NFC API in the case of an NFC tag. Specifically, by reading the QR code, a text-formatted current location ID is entered into the smartphone.

[0173] Step 2:

[0174] The device obtains destination information based on the user's input. The user enters the name of the desired store or department (for example, "Women's clothing department") on the application screen and presses the send button. This brings the current location information and destination information together on the device.

[0175] Step 3:

[0176] The device sends the acquired current location information and destination information to the server. The server then receives the necessary data. Specifically, the current location ID and destination name are sent to the server via API communication. The data sent is in a standardized format such as JSON.

[0177] Step 4:

[0178] The server calculates the optimal route based on the current location and destination information. The server references the map data of the commercial facility and uses algorithms such as A to calculate the shortest and most efficient route. The current location and destination information are input, and a list of routes is generated as output.

[0179] Step 5:

[0180] The server uses a generative AI model (GPT-4) to generate a route guidance message. The server inputs the calculated route information as a prompt sentence into the generative AI, which generates an easy-to-understand route guidance message in natural language. The output is a route guidance message (e.g., "Enter the entrance, turn right, take the escalator to the second floor, and go straight; you will see the women's clothing department on your left").

[0181] Step 6:

[0182] The server generates route guidance messages and sends illustrated maps to the device, and text and image data are sent to the smartphone via API communication.

[0183] Step 7:

[0184] The device displays the received route guidance message and an illustrated map. The user can check the message and follow the visual map. Specifically, the screen displaying the message and map is rendered on the device using React Native, a mobile application framework.

[0185] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0186] The present invention relates to a navigation system that enables users to reach their destination without getting lost in a complex and vast train station. In particular, the present invention relates to a system that combines an emotion engine that recognizes the user's emotions and adjusts route guidance messages.

[0187] System configuration

[0188] The system consists of QR codes or NFC tags installed at multiple locations within the station, terminals for reading them, a server that generates route guidance, and an emotion engine that recognizes the user's emotions. The terminals are smartphones or dedicated reading devices. The server is located in the cloud and processes information in real time. The emotion engine analyzes the user's facial expressions and voice data to identify their emotional state.

[0189] Operational Overview

[0190] 1. The user scans a QR code or NFC tag

[0191] The user scans QR codes or NFC tags installed at various points within the station with their device, which then acquires their current location information.

[0192] 2. The device sends the current location and destination information to the server.

[0193] The device sends the current location information and the destination information entered by the user to the server, which then receives all the necessary information.

[0194] 3. The emotion engine recognizes the user's emotions

[0195] The device uses the user's camera and microphone to collect facial and voice data, which the emotion engine analyzes to identify the user's emotional state, for example, whether the user is stressed or relaxed.

[0196] 4. The server calculates the optimal route and generates a route guidance message.

[0197] Based on the current location and destination information received by the server, the system refers to map data within the station and calculates the optimal route. Based on the calculation results, the generation AI generates a route guidance message in natural language that is easy for the user to understand. The emotion engine recognizes the user's emotional state and adjusts the message content accordingly. For example, if the user is feeling stressed, the system suggests an alternative route that will help them relax.

[0198] 5. The server generates a route guidance message and sends it to the terminal.

[0199] The generated message and accompanying illustrated maps and guide maps are sent from the server to the user's terminal.

[0200] 6. The device displays a route guidance message.

[0201] The user's device will display the received route guidance message, allowing the user to easily understand what to do next.

[0202] Specific examples

[0203] For example, suppose tourist C from overseas arrives at a station in a large city in Japan for the first time and is looking for the tourist information center on the other side of the station as his destination.

[0204] First, Mr. C uses his smartphone to scan the QR code on a guide board inside the station. Mr. C's device acquires the current location ID "east_exit_01." Next, Mr. C enters "Tourist Information Center" as his destination on the device screen and presses the send button.

[0205] The device sends current location and destination information to the server. At the same time, the device uses its camera and microphone to detect C's facial expressions and voice, and the emotion engine analyzes this to determine that C is feeling somewhat stressed.

[0206] The server receives the information "east_exit_01" and "tourist information center" and calculates the optimal route based on map data within the station. Based on the calculation results, the generation AI takes into account the information from the emotion engine and generates a route guidance message saying, "Exit the east exit, turn left, go straight through the underground passage, and you will see the tourist information center on your right." At this time, audio guidance is also provided to reduce Mr. C's stress.

[0207] The server sends the generated message and map to Mr. C's smartphone. Mr. C checks the message and audio guidance he receives and follows them to safely arrive at the tourist information center.

[0208] This system aims to further improve the user experience by not only helping users reach their destinations easily and accurately, but also by providing appropriate guidance that takes into account the user's emotional state, making it particularly useful in large, complex station premises.

[0209] The processing flow will be explained below.

[0210] Step 1:

[0211] The user scans the QR code or NFC tag.

[0212] The user holds their smartphone close to a QR code or NFC tag installed in the station and reads it. At this time, the device acquires the information from the QR code or NFC tag.

[0213] Step 2:

[0214] The terminal acquires current location information and prompts the user to input destination information.

[0215] The device analyzes and obtains the current location ID information from the QR code or NFC tag, and at the same time displays a destination input form on the device screen, prompting the user to enter the destination information.

[0216] Step 3:

[0217] The terminal acquires the user's emotion data.

[0218] The device uses the smartphone's camera and microphone to collect the user's facial and voice data, which is later sent to the emotion engine.

[0219] Step 4:

[0220] The device transmits current location information, destination information, and emotion data to the server.

[0221] After the user inputs the destination information, the device sends the current location information, the input destination information, and the collected emotion data to the server as a single request data. This data includes, for example, "currentLocationId: 'east_exit_01', destination: 'Tourist Information Center', emotionData: [facial expression data, voice data]".

[0222] Step 5:

[0223] The server receives the request data and identifies the current location and destination.

[0224] The server receives the data sent from the terminal and identifies the current location from the station map database based on the current location ID. It also identifies the destination location in the map database based on the destination information.

[0225] Step 6:

[0226] The server uses an emotion engine to analyze the user's emotional state.

[0227] The server analyzes the emotional data to identify the user's emotional state, for example, by analyzing facial expressions and voice to recognize whether the user is stressed or relaxed.

[0228] Step 7:

[0229] The server calculates the optimal route.

[0230] The server uses the current and destination locations to run a route planning algorithm within the station to calculate the optimal route, taking into account obstacles, restricted areas, and current pedestrian flow data.

[0231] Step 8:

[0232] The server generates a route guidance message using the generation AI.

[0233] The server inputs the calculated route information into the generation AI, which then generates a route guidance message in natural language that is easy for the user to understand. The emotion engine recognizes the user's emotional state and adjusts the message content accordingly. For example, if the user is feeling stressed, it suggests gentler language or an alternative route.

[0234] Step 9:

[0235] The server sends the generated guidance message and illustrated map to the terminal.

[0236] The server sends route guidance messages and related illustrated map data created by the AI ​​to the device, including emotion-based advice and words of encouragement.

[0237] Step 10:

[0238] The terminal displays an information message and an illustrated map.

[0239] The device displays the route guidance message and illustrated map it receives on the screen. Users can check them and understand what to do next. For example, specific directions such as "Turn left after exiting the east exit, go straight through the underground passage, and you will see the tourist information center on your right" are displayed. If the user is feeling stressed, a message such as "It's okay, just take it easy" is added.

[0240] Step 11:

[0241] The user follows the guidance to reach the destination.

[0242] The user follows the route in real time while viewing the guidance messages, illustrated maps, and audio guidance on the device, and safely arrives at the destination. The emotion engine monitors the user's emotional state appropriately and readjusts the route guidance messages if necessary.

[0243] This step not only allows users to reach their destination easily and accurately, but also allows them to receive appropriate guidance that takes their emotional state into account.

[0244] Example 2

[0245] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0246] Accurate and easy-to-understand route guidance is required for users to reach their destinations without getting lost in train stations and large facilities. However, conventional navigation systems have had difficulty providing appropriate route guidance according to the user's emotional state. For example, flexible guidance that can respond to users who are feeling stressed or nervous is required. The present invention aims to solve this problem and provide appropriate route guidance according to the user's emotional state.

[0247] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0248] In this invention, the server includes means for acquiring current location information, means for transmitting destination information input by the user to the server, means for the server to calculate the optimal route and generate a route guidance message using a generation AI, means for the terminal to collect facial expression and voice data and include an emotion engine that analyzes the emotional state, and means for adjusting the content of the route guidance message based on the results of the emotion engine. This enables users to avoid getting lost in train stations or large facilities and to receive optimal route guidance according to their emotional state at the time.

[0249] "Current location information" is digital data that indicates the user's location within a station or large facility.

[0250] "Destination information" is digital data that indicates the final location or destination that the user is aiming for.

[0251] A "server" is a computer system that processes data and provides information via a network.

[0252] "Generative AI" is a program that uses artificial intelligence technology to generate route guidance messages in natural language.

[0253] A "terminal" is a device used by a user to read QR codes or NFC tags, such as a smartphone or a dedicated reading device.

[0254] A "QR code" is a code that encodes information into a rectangular grid and can be read in two dimensions.

[0255] An "NFC tag" is a tag that can transmit and receive data using near-field wireless communication technology.

[0256] An "emotion engine" is a software system that analyzes a user's facial expressions and voice data to identify their emotional state.

[0257] A "route guidance message" is a message provided to a user that contains instructions and explanations for reaching a destination.

[0258] This invention is a navigation system that helps users reach their destinations without getting lost in complex and vast train stations and large-scale facilities. This system provides more accurate and personalized guidance by incorporating an emotion engine that recognizes the user's emotions and adjusts route guidance messages accordingly.

[0259] This system consists of the following elements: QR codes or NFC tags installed at multiple locations within stations and facilities; terminals for reading these; a server for generating route guidance; and an emotion engine for recognizing the user's emotions. The terminals function as smartphones or dedicated reading devices, while the server is located in the cloud and processes information in real time. The emotion engine analyzes the user's facial expressions and voice data to identify their emotional state.

[0260] The flow of operation of the system is explained below.

[0261] When a user uses their device to scan QR codes or NFC tags installed at various points within the station, their current location information is acquired by the device. Next, the user enters their destination into the device and sends it along with their current location information to the server. At this time, the device uses its camera and microphone to collect the user's facial expressions and voice data, which is analyzed by an emotion engine. The server calculates the optimal route based on the received current location and destination information. A generative AI (e.g., OpenAI's GPT-4) generates an easy-to-understand route guidance message in natural language. Furthermore, the content of the message is adjusted according to the user's emotional state as recognized by the emotion engine.

[0262] For example, imagine a tourist from overseas arrives at a station in a major Japanese city for the first time and is looking for the tourist information center on the other side of the station as his or her destination.

[0263] When a user scans a QR code on a guide board inside the station with their smartphone, the device obtains the current location ID "east_exit_01." Next, they enter "Tourist Information Center" as their destination on the device screen and press the send button. The device then sends the current location and destination information to the server. At the same time, the device uses its camera and microphone to detect the user's facial expressions and voice, which the emotion engine analyzes and determines that the user is feeling somewhat stressed. Based on this information, the server calculates the optimal route by referencing map data within the station and generates a route guidance message that reads, "Exit the east exit, turn left, and go straight through the underground passage; you will see the Tourist Information Center on your right." Audio guidance is also provided at this time to reduce the user's stress. The server then sends the generated message and map to the user's smartphone, where the user confirms the message and map and safely arrives at the Tourist Information Center.

[0264] An example of a prompt to be input to the generative AI model is as follows:

[0265] "The user's current location is the east exit and their destination is the tourist information center. They're feeling a bit stressed. Use this information to generate a message that will guide them to a more relaxing route."

[0266] As described above, this system aims to improve the user experience by providing appropriate guidance according to the user's emotional state, making it particularly useful in large, complex train stations and other large-scale facilities.

[0267] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0268] Step 1: User scans a QR code or NFC tag

[0269] The user scans a QR code or NFC tag installed at a designated point within a station or facility using a device such as a smartphone.

[0270] Input: QR code or NFC tag

[0271] Data processing: The device reads the QR code or NFC tag information and obtains the current location information.

[0272] Output: Current location information (e.g. "east_exit_01")

[0273] Step 2: The device sends the current location and destination information to the server.

[0274] The user inputs destination information into the terminal and transmits it to the server together with current location information.

[0275] Input: Current location information, destination information (e.g. "tourist information center")

[0276] Data processing: Packages the current location information and destination information and sends it to the server via the network.

[0277] Output: Current location and destination information received by the server

[0278] Step 3: The device collects facial and voice data

[0279] The user's device uses a camera and microphone to collect facial expressions and voice data.

[0280] Input: User's facial expression data, voice data

[0281] Data processing: Collect data in real time using cameras and microphones and send that data to the emotion engine.

[0282] Output: Collected facial expression data and voice data

[0283] Step 4: The emotion engine analyzes the user's emotions

[0284] The emotion engine analyzes facial expression and voice data to identify the user's emotional state.

[0285] Input: facial expression data, voice data

[0286] Data processing: Using data analysis algorithms to identify emotional states such as stress or relaxation

[0287] Output: Identified emotional state (e.g., "stressed")

[0288] Step 5: The server calculates the optimal route

[0289] The server calculates the optimal route based on the current location information and destination information, and refers to map data within the station or facility.

[0290] Input: Current location information, destination information, map data

[0291] Data processing: Using an algorithm to calculate the optimal route from your current location to your destination

[0292] Output: Calculated optimal route data

[0293] Step 6: The server generates a route guidance message using the generation AI.

[0294] The server uses a generative AI (e.g., GPT-4) to generate a natural language route guidance message based on the optimal route, adjusting the message content based on the results of the emotion engine.

[0295] Input: optimal route data, emotional state

[0296] Data processing: Generative AI uses natural language processing algorithms to generate route guidance messages

[0297] Output: Generated route guidance message (e.g. "Turn left after exiting the east exit, go straight through the underground passage and you will see the tourist information center on your right")

[0298] Step 7: The server generates a route guidance message and sends it to the device.

[0299] The server sends route guidance messages and accompanying maps and directions to the user's terminal.

[0300] Input: Route guidance message, map data

[0301] Data processing: Packages message and map data and sends them to the terminal via the network

[0302] Output: Route guidance message and map sent to device

[0303] Step 8: The device displays a route guidance message.

[0304] The user's device displays the received route guidance message and plays back the audio guide, allowing the user to reach their destination.

[0305] Input: Received route guidance message, map data

[0306] Data processing: The device displays the message on the screen and plays the audio guide.

[0307] Output: The displayed and played guidance information allows the user to navigate to their destination.

[0308] In this way, users can receive accurate and emotionally sensitive route guidance in their local environment.

[0309] (Application example 2)

[0310] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0311] Navigation systems are needed to help workers navigate large, complex facilities such as factories without getting lost. In particular, there is a need to reduce stress and confusion by providing guidance messages that take the user's emotional state into account. However, conventional navigation systems are unable to generate route guidance messages that take the user's emotional state into account, making it difficult to reduce user stress.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0313] In this invention, the server includes means for calculating an optimal route based on current location information and destination information, means for generating a route guidance message using a generation AI, means for analyzing the emotional state of the user using a camera and microphone and transmitting the results to the server, and means for the server to adjust the route guidance message taking the emotional state into consideration, thereby enabling the user to reach the destination easily and reducing stress and confusion.

[0314] A "QR code" is a type of two-dimensional barcode that visually represents information and can be read by a device with a camera.

[0315] An "NFC tag" is an information recording medium that uses near-field wireless communication technology, and is a tag that can read and write data when a compatible device comes close.

[0316] "Location information" is information that indicates that a user is in a specific location, and is data obtained through a QR code or NFC tag.

[0317] "Destination information" is information about the location the user is heading for, and is data that is specified by the user entering it into the terminal.

[0318] A "server" is a computer system that stores and processes data on a network, and is a device that performs tasks such as route calculations and generating guidance messages.

[0319] A "route guidance message" is a message containing instructions for users to reach their destination from their current location, and is created by a generation AI.

[0320] "Device" means an input / display device held by a user, such as a smartphone, tablet, smart glasses, or head-mounted display.

[0321] A "camera" is a device for acquiring visual information, and is built into a terminal and used to capture the user's facial expression.

[0322] A "microphone" is a device for acquiring voice information, and is built into a terminal and used to record the user's voice.

[0323] "Emotional state" is information that indicates the user's psychological and emotional state, and is analyzed based on data obtained through cameras and microphones.

[0324] "Generative AI" is a type of artificial intelligence that refers to programs or models that perform natural language processing based on large amounts of data and generate route guidance messages.

[0325] "Adjustment" refers to the act of changing the content and presentation method of a route guidance message depending on the user's emotional state.

[0326] This invention relates to a navigation system that enables workers to reach their destinations without getting lost in a complex and vast factory. In particular, it relates to a system that combines an emotion engine that recognizes the user's emotions and adjusts route guidance messages.

[0327] System configuration

[0328] The system consists of QR codes or NFC tags installed at multiple locations within the factory, terminals for reading them, a server that generates route guidance, and an emotion engine that recognizes the user's emotions. The terminals are smartphones, smart glasses, head-mounted displays, etc. The server is located on the cloud and processes information in real time. The emotion engine analyzes the user's facial expressions and voice data to identify their emotional state.

[0329] System Operation

[0330] Read QR codes and NFC tags and determine current location

[0331] The user scans a QR code or NFC tag installed at a designated location in the factory with the device, which then acquires the current location information.

[0332] Setting the destination and sending to the server

[0333] The device operated by the user transmits the acquired current location information and the destination information entered by the user to the server, which then receives data for calculating the optimal route based on this information.

[0334] Recognition of emotional states

[0335] The device uses a built-in camera and microphone to collect facial and voice data from the user, and the emotion engine analyzes this data to determine the user's emotional state, such as whether they are stressed or relaxed.

[0336] Generate route guidance messages

[0337] The server calculates the optimal route based on the received current location and destination information, as well as the user's emotional state. The server's AI uses natural language processing to generate a route guidance message that is easy for the user to understand. The content of the message is adjusted according to the user's emotional state.

[0338] Sending and displaying information messages

[0339] The server generates route guidance messages and sends them to the user's device along with accompanying illustrated maps and guide maps. The user can then check the messages on their device and follow the instructions to reach their destination.

[0340] Hardware and software used

[0341] QR codes and NFC tags: Used to identify your location.

[0342] Devices: Smartphones, smart glasses, head-mounted displays (e.g., Samsung Galaxy S21, Google® Glass®, Microsoft® HoloLens® 2).

[0343] Server: Cloud server (e.g., Amazon Web Services (AWS(R))).

[0344] Emotion engine: Analyzes data captured by the camera and microphone (e.g., Amazon Rekognition, Amazon Comprehend).

[0345] Generative AI: Natural language processing models (e.g., GPT-4 (OpenAI)).

[0346] Specific examples

[0347] Below are some specific examples of how this system can be used.

[0348] Worker A, heading to Parts Warehouse B in the factory, scans a QR code with his smartphone to identify his current location. Worker A's device retrieves "Section A" and then enters "Parts Warehouse B" as his destination. At the same time, the camera and microphone record Worker A's facial expressions and voice. The emotion engine determines that Worker A is feeling stressed.

[0349] The server receives information about "Section A" and "Parts Warehouse B" and calculates the optimal route. The generation AI takes into account information from the emotion engine and generates a route guidance message suggesting a relaxing route. The message generated reads, "Exit production line section A, turn right and go straight. Turn left at the third intersection and take the elevator down to the first floor. Go straight and you will see Parts Warehouse B on your right."

[0350] The server sends the generated message and map to Person A's smartphone. Person A checks the received message and follows the instructions to safely reach Parts Warehouse B.

[0351] The following text is used as an example of a prompt sentence:

[0352] plain text

[0353] Your current location is assembly line section A and your destination is parts warehouse B. Your emotional state is stressed.

[0354] This not only allows users to reach their destinations more easily, but also reduces stress and confusion.

[0355] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0356] Step 1:

[0357] Obtaining current location information

[0358] The user scans a QR code or NFC tag installed in a location within the factory with a device (smartphone, smart glasses, head-mounted display). This allows the device to acquire data indicating the current location as input. The acquired current location information is recognized as an identification ID and used for the next process.

[0359] Input: QR code or NFC tag

[0360] Output: Current location ID

[0361] Step 2:

[0362] Setting destination information

[0363] The user inputs the next destination (e.g., a parts warehouse or other work section) into the terminal. The terminal receives this destination information as input. The destination information is used in the next step of route calculation.

[0364] Input: Destination information (user input)

[0365] Output: Destination ID

[0366] Step 3:

[0367] Recognition of emotional states

[0368] The device's camera and microphone are used to collect the user's facial expressions and voice data. The emotion engine analyzes this data to identify the user's emotional state (e.g., whether they are stressed or relaxed). The analyzed emotion data is used in the next step.

[0369] Input: User's facial expression data and voice data (input from camera and microphone)

[0370] Output: Emotional state (stressed, relaxed, etc.)

[0371] Step 4:

[0372] Generate route guidance messages

[0373] The server receives current location information, destination information, and emotional state as input and calculates the optimal route. Based on the calculation results, the generation AI generates easy-to-understand route guidance messages in natural language. The route and message content are adjusted depending on the emotional state.

[0374] Input: Current location, destination, emotional state

[0375] Output: Route guidance message

[0376] Step 5:

[0377] Sending an information message

[0378] The server generates a route guidance message and sends it to the user's device along with an accompanying illustrated map or guide map. The message is then displayed in the final step.

[0379] Input: Route guidance message (generated by AI)

[0380] Output: Sent message and map

[0381] Step 6:

[0382] Displaying route guidance messages

[0383] The user's device displays the route guidance message received from the server, allowing the user to receive visual and audio instructions to reach the destination.

[0384] Input: Sent message and map (received from server)

[0385] Output: Displayed route guidance message

[0386] Specific operation example

[0387] In the case of Worker A heading to Parts Warehouse B in the factory:

[0388] Step 1: Person A scans the QR code with their smartphone and obtains their current location as "Section A."

[0389] Step 2: Person A enters "Parts Warehouse B" as the destination into the terminal.

[0390] Step 3: Using a camera and microphone, facial and voice data from Person A is collected, and the emotion engine determines that Person A is feeling stressed.

[0391] Step 4: The server receives information about "Section A" and "Parts Warehouse B" and calculates the optimal route. The generation AI generates a route guidance message to reduce stress.

[0392] Step 5: The server sends the generated message and map to Mr. A's smartphone.

[0393] Step 6: Person A checks the message and map on his smartphone and follows it to reach "Parts Warehouse B."

[0394] The following is used as an example prompt sentence:

[0395] plain text

[0396] Your current location is assembly line section A and your destination is parts warehouse B. Your emotional state is stressed.

[0397] 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.

[0398] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0399] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0400] [Second embodiment]

[0401] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0402] 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.

[0403] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0404] 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.

[0405] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0406] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0407] 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.

[0408] 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.

[0409] 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 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.

[0410] 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.

[0411] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0412] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0413] The present invention relates to a navigation system that enables users to reach their destinations without getting lost in a complex and vast station premises. Specific embodiments of this system will be described below.

[0414] System configuration

[0415] The system consists of a station where QR codes or NFC tags are installed, a terminal for reading them, and a server that generates route guidance. The terminal is a smartphone or a dedicated reading device. The server is located in the cloud and processes information in real time.

[0416] Operational Overview

[0417] 1. The user scans a QR code or NFC tag

[0418] The user scans QR codes or NFC tags installed at various points within the station with their device, which then acquires their current location information.

[0419] 2. The device sends the current location and destination information to the server.

[0420] The device sends the acquired current location information and the destination information entered by the user to the server, which then receives all the necessary information.

[0421] 3. The server calculates the optimal route and generates a route guidance message.

[0422] The server calculates the optimal route based on the received current location and destination information, referencing the station's map data. Based on the calculation results, the generation AI generates a route guidance message in natural language that is easy for the user to understand. For example, a message might say, "Go to Exit 5, take the escalator and go through the underground passage, and you will arrive at the north exit."

[0423] 4. The server generates a route guidance message and sends it to the terminal.

[0424] The generated message and accompanying illustrated maps and guide maps are sent from the server to the user's terminal.

[0425] 5. The device displays a route guidance message.

[0426] The user's device will display the received route guidance message, allowing the user to easily understand what to do next.

[0427] Specific examples

[0428] For example, suppose tourist B from overseas arrives at a station in a large city in Japan for the first time and is looking for the tourist information center on the other side of the station as his destination.

[0429] First, Person B uses his smartphone to scan the QR code on a guide board inside the station. Person B's device acquires the current location ID "east_exit_01." Next, Person B enters "Tourist Information Center" as his destination on the device screen and presses the send button.

[0430] The device sends its current location and destination information to the server. The server receives the information "east_exit_01" and "tourist information center" and calculates the optimal route based on map data within the station. The AI ​​then generates a route guidance message and illustrated map, saying, "Exit the east exit, turn left, and go straight through the underground passage. You will see the tourist information center on your right."

[0431] The server sends the generated message and map to Person B's smartphone. Person B checks the message on the screen and follows the instructions to safely arrive at the tourist information center.

[0432] This system helps users reach their destinations easily and accurately, making it particularly useful in large, complex station premises.

[0433] The processing flow will be explained below.

[0434] Step 1:

[0435] The user scans a QR code or NFC tag in the station.

[0436] The user holds their smartphone close to a QR code or NFC tag installed in the station and reads it. At this time, the device acquires the information from the QR code or NFC tag.

[0437] Step 2:

[0438] The terminal acquires current location information and prompts the user to input destination information.

[0439] The device analyzes and obtains the current location ID information from the QR code or NFC tag, and at the same time displays a destination input form on the device screen, prompting the user to enter the destination information.

[0440] Step 3:

[0441] The device sends current location information and destination information to the server.

[0442] After the user inputs the destination information, the device sends the current location information and the input destination information to the server as a single request data. This data includes, for example, "currentLocationId: 'east_exit_01', destination: 'Tourist Information Center'".

[0443] Step 4:

[0444] The server receives the request data and identifies the current location and destination.

[0445] The server receives the data sent from the terminal and identifies the current location from the station map database based on the current location ID. It also identifies the destination location in the map database based on the destination information.

[0446] Step 5:

[0447] The server calculates the optimal route.

[0448] The server uses the current and destination locations to run a route planning algorithm within the station to calculate the optimal route, taking into account obstacles, restricted areas, and current pedestrian flow data.

[0449] Step 6:

[0450] The server generates a route guidance message using the generation AI.

[0451] The server inputs the calculated route information into the generation AI, which then generates a route guidance message in natural language that is easy for users to understand. For example, a message such as, "Exit the east exit, turn left, and go straight through the underground passage; you will see the tourist information center on your right."

[0452] Step 7:

[0453] The server sends the generated guidance message and illustrated map to the terminal.

[0454] The server sends the route guidance message created by the AI ​​and related illustrated map data to the device.

[0455] Step 8:

[0456] The terminal displays an information message and an illustrated map.

[0457] The device displays the received route guidance message and illustrated map on the screen, and the user can check them and follow the instructions to head to their destination.

[0458] Step 9:

[0459] The user follows the guidance to reach the destination.

[0460] The user follows the route in real time while looking at the guidance messages and illustrated map on the device, and safely reaches the destination.

[0461] Example 1

[0462] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0463] There is a need to provide navigation that will enable users to quickly and accurately reach their destinations within vast and complex station premises without getting lost. Conventional guidance systems make it difficult for users to grasp their exact current location or the appropriate route. Furthermore, because they do not provide guidance based on information updated in real time, they are unable to respond when routes change due to construction, events, etc. Therefore, a new system is needed to solve these issues.

[0464] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0465] In this invention, the server includes means for acquiring the user's current location using codes or tags installed at multiple locations within the station, means for transmitting the current location information and destination information entered by the user to the processing device, means for the processing device to calculate an optimal route based on the current location information and the destination information, means for generating a route guidance message using the generation system, means for transmitting the generated route guidance message to the user's display device, and means for the display device to display the route guidance message. This allows users to easily and quickly check their current location and destination and receive optimal route guidance in real time.

[0466] "Code" refers to QR codes or other similar technology that are placed at various locations within the station for reading by passenger devices.

[0467] "Tag" refers to an NFC tag or other similar technology that is placed at various locations within a station to be read by a passenger's device.

[0468] "Current location information" refers to information indicating the user's current location, which is obtained by the user reading codes or tags installed at various locations within the station.

[0469] "Destination information" refers to information about a desired location that is specified by a user by inputting it into a terminal.

[0470] The "processing device" refers to a computer system or server that receives current location information and destination information and calculates the optimal route based on that information.

[0471] "Generation system" refers to artificial intelligence (AI) that generates easy-to-understand route guidance messages in natural language based on optimal route information calculated by a processing device.

[0472] "Route Guidance Message" means a message containing instructions regarding the optimal route from a current location to a destination, provided in a format that is easy for a user to understand.

[0473] "Display device" refers to a device, primarily a smartphone or dedicated terminal, that receives the route guidance message generated by the generation system and displays it to the user.

[0474] The present invention relates to a navigation system that enables users to reach their destinations without getting lost in a complex and vast station premises. Specific embodiments of this system will be described below.

[0475] System configuration

[0476] This system consists of codes or tags installed in stations, terminals for reading them, and a processing device (server) that generates route guidance. The terminals are smartphones or dedicated reading devices, and the processing device is often located on the cloud. Cloud computing platforms, for example, can be used as cloud services.

[0477] Program processing

[0478] 1. The user reads the code or tag

[0479] Users use their smartphones or other dedicated devices to scan codes or tags placed at various locations within the station, which allows the device to acquire information about the user's current location.

[0480] 2. The device sends the current location and destination information to the server.

[0481] The user enters destination information into their smartphone and presses the send button. The device then sends the current location and destination information to the server. This process is carried out via a dedicated application or browser on the device.

[0482] 3. The server calculates the optimal route and generates a route guidance message.

[0483] The server calculates the optimal route based on the received current location information and destination information, for example, using a map platform API. Furthermore, it generates an easy-to-understand route guidance message in natural language using a generative AI model (for example, OpenAI's GPT-4). An example of a prompt used in this case is shown below.

[0484] Current location: east_exit_01

[0485] Destination: Tourist Information Center

[0486] Please provide natural language guidance on the best way to get from your current location to your destination, using a large map of the station. For example, please include exit numbers, instructions on how to use escalators, directions to underground passages, etc. Also, please generate an easy-to-understand illustrated map.

[0487] Based on this prompt, the server generates a specific route guidance message such as, "Exit the east exit, turn left, go straight through the underground passage and you will see the tourist information center on your right."

[0488] 4. The server generates a route guidance message and sends it to the terminal.

[0489] The generated route guidance message and illustrated map are sent from the server to the user's device using cloud communication technology, such as a cloud messaging service.

[0490] 5. The device displays a route guidance message.

[0491] The user's device displays the received route guidance message, allowing the user to understand what to do next and reach their destination.

[0492] Usage example

[0493] For example, consider the case where a tourist is looking for a tourist information center at a train station in a large city they are visiting for the first time. The user scans a QR code installed near the east exit of the station with their smartphone and obtains the current location ID "east_exit_01." Next, they enter the destination information "tourist information center" and submit it. The server calculates the optimal route based on this information and uses a generative AI model to generate a message that says, "Exit the east exit, turn left, and go straight through the underground passage; you will see the tourist information center on your right," which is sent to the user's device. The user follows this message and arrives safely at their destination.

[0494] This system allows users to reach their destinations quickly and without getting lost even in vast and complex station premises.

[0495] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0496] Step 1:

[0497] The user scans a code or tag inside the station.

[0498] Users use their smartphones or dedicated devices to scan codes and tags placed throughout the station to obtain location information. Specifically, a QR code reader app or NFC-enabled device is used. The input is the identification information embedded in the code or tag, and the output is a location ID such as "east_exit_01" sent to the device.

[0499] Step 2:

[0500] The device sends current location information and destination information to the server.

[0501] The user enters destination information into the terminal and presses the send button. The terminal then sends the acquired current location information and the destination information entered by the user to the server via the Internet. The inputs include the current location ID and destination information (e.g., "tourist information center"), and the output is this information sent to the server.

[0502] Step 3:

[0503] The server calculates the optimal route and generates a route guidance message.

[0504] The server calculates the optimal route by referencing map data based on the received current location information and destination information. For example, a map data API is used to calculate the route, and a prompt sentence is entered into a generative AI model (e.g., GPT-4) to generate a route guidance message. The input includes the current location ID and destination information, and data processing involves route calculation and natural language generation by the generative AI model. The output is a route guidance message such as, "Exit the east exit, turn left, and go straight through the underground passage; you will see the tourist information center on your right."

[0505] Step 4:

[0506] The server sends the generated route guidance message to the terminal.

[0507] The server sends the generated route guidance message and the illustrated map to the user's terminal via the Internet. The input is the route guidance message and the accompanying illustrated map, and the output is sent to the user's terminal.

[0508] Step 5:

[0509] The device displays a route guidance message.

[0510] The terminal displays the received route guidance message and illustrated map. By referring to this, the user can intuitively understand how to get to their destination. The input is the route guidance message and illustrated map sent from the server, and the output is this information displayed on the terminal screen. In concrete terms, the message and map are displayed on the terminal screen, and the user checks them before taking action.

[0511] (Application example 1)

[0512] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0513] In large, complex commercial facilities and large stores, users often get lost before reaching their destination. This complexity significantly reduces user convenience, especially for first-time visitors or those looking for a specific product or section. Furthermore, the lack of real-time guidance makes it difficult to navigate in a facility based on the current situation. For this reason, there is a need for an effective navigation system that can help users reach their destination smoothly.

[0514] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0515] In this invention, the server includes means for acquiring a user's current location using QR codes or NFC tags installed at multiple locations within the commercial facility, means for transmitting the current location information and destination information entered by the user to the server, means for the server to calculate an optimal route based on the current location information and destination information, means for the server to generate a route guidance message using a generation AI, means for the server to transmit the route guidance message to the user's terminal, and means for the terminal to display the route guidance message, thereby enabling users to quickly and accurately reach their destination within the commercial facility without getting lost.

[0516] "Commercial facility" refers to a large building or area that houses multiple stores and related facilities offering goods or services.

[0517] "QR Code" is a type of two-dimensional barcode that is a rectangular code that can efficiently store information and be quickly read.

[0518] An "NFC tag" is an electronic tag that uses Near Field Communication technology to read and write information without contact.

[0519] "User" means a customer who visits a commercial establishment and uses goods or services.

[0520] "Current location information" refers to information about the user's current location, obtained by reading a QR code or NFC tag.

[0521] "Destination information" refers to information about a specific location or store that a user wants to reach within a commercial facility.

[0522] A "server" is a computer system that communicates with multiple terminals via a network and stores and processes data.

[0523] The "optimal route" refers to the most efficient and shortest route between the current location and the destination.

[0524] "Generative AI" refers to a system that uses artificial intelligence technology to generate messages and information based on natural language.

[0525] A "route guidance message" is a message that provides the user with easy-to-understand guidance on how to reach a destination.

[0526] "Terminal" refers to a device used by a user, such as a mobile information device or smartphone.

[0527] The present invention relates to a navigation system for assisting users in smoothly reaching their destinations in commercial facilities. Specific embodiments of this system will be described below.

[0528] System Configuration

[0529] This system consists of QR codes or NFC tags installed within the commercial facility, a user's device to read them, and a server to process the data.User devices are typically smartphones, and QR codes or NFC tags are installed at multiple points within the facility, such as at the entrance and near the escalators on each floor.

[0530] Operational Overview

[0531] 1. Reading a QR code or NFC tag

[0532] Users can obtain location information by scanning QR codes or NFC tags in commercial facilities with their smartphones. QR codes are read using the smartphone's camera and the ZXing library, and NFC tags are read using the smartphone's NFC reader and the Android NFC API.

[0533] 2. Sending current location and destination information

[0534] The user enters the name of the desired store or department in the application and presses the send button. The device then sends the acquired current location information and the entered destination information to the server. The server receives the current location information and destination information via API communication and processes the data.

[0535] 3. Calculating the optimal route and generating guidance messages

[0536] The server refers to the commercial facility's map data based on the received current location information and destination information and calculates the optimal route. This route calculation uses the A algorithm. After calculating the optimal route, it uses the generative AI model GPT-4 to generate an easy-to-understand route guidance message in natural language. The following is used as an example of a prompt sentence.

[0537] Prompt Sentence Examples

[0538] "Generate a guidance message for when a user scans a QR code at the entrance of a commercial facility and enters the women's clothing department as the destination. Starting point: Entrance Arrival point: Women's clothing department Optimal route based on map information: Turn right after entering the entrance, take the escalator to the second floor, and go straight. You will see the women's clothing department on your left."

[0539] 4. Sending a message to the device

[0540] The generated route guidance message, illustrated map, and guide map are sent from the server to the user's device. The message data is sent via API communication and displayed on the user's smartphone.

[0541] 5. Display of route guidance messages

[0542] The user's device displays the received route guidance message. Based on the message and map displayed on the device, the user can navigate to their destination. This display is done using React Native, a mobile application framework.

[0543] Specific examples

[0544] For example, consider the case where Person A is looking for the "women's clothing section" at a large shopping mall that she is visiting for the first time. Person A scans the QR code at the entrance, enters "women's clothing section" into the application, and submits it. Based on this information, the server generates a guidance message and map that says, "Enter the entrance, turn right, take the escalator to the second floor, and go straight; you will see the women's clothing section on your left," and sends this to Person A's smartphone. Person A can follow the guidance and arrive at the women's clothing section without getting lost.

[0545] This system allows users to reach their destinations efficiently and accurately even within large, complex commercial facilities.

[0546] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0547] Step 1:

[0548] A user scans a QR code or NFC tag in a commercial facility with their smartphone. This allows the current location information (for example, location information near the entrance) to be obtained using the camera and ZXing library in the case of a QR code, or the NFC reader and Android NFC API in the case of an NFC tag. Specifically, by reading the QR code, a text-formatted current location ID is entered into the smartphone.

[0549] Step 2:

[0550] The device obtains destination information based on the user's input. The user enters the name of the desired store or department (for example, "Women's clothing department") on the application screen and presses the send button. This brings the current location information and destination information together on the device.

[0551] Step 3:

[0552] The device sends the acquired current location information and destination information to the server. The server then receives the necessary data. Specifically, the current location ID and destination name are sent to the server via API communication. The data sent is in a standardized format such as JSON.

[0553] Step 4:

[0554] The server calculates the optimal route based on the current location and destination information. The server references the map data of the commercial facility and uses algorithms such as A to calculate the shortest and most efficient route. The current location and destination information are input, and a list of routes is generated as output.

[0555] Step 5:

[0556] The server uses a generative AI model (GPT-4) to generate a route guidance message. The server inputs the calculated route information as a prompt sentence into the generative AI, which generates an easy-to-understand route guidance message in natural language. The output is a route guidance message (e.g., "Enter the entrance, turn right, take the escalator to the second floor, and go straight; you will see the women's clothing department on your left").

[0557] Step 6:

[0558] The server generates route guidance messages and sends illustrated maps to the device, and text and image data are sent to the smartphone via API communication.

[0559] Step 7:

[0560] The device displays the received route guidance message and an illustrated map. The user can check the message and follow the visual map. Specifically, the screen displaying the message and map is rendered on the device using React Native, a mobile application framework.

[0561] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0562] The present invention relates to a navigation system that enables users to reach their destination without getting lost in a complex and vast train station. In particular, the present invention relates to a system that combines an emotion engine that recognizes the user's emotions and adjusts route guidance messages.

[0563] System configuration

[0564] The system consists of QR codes or NFC tags installed at multiple locations within the station, terminals for reading them, a server that generates route guidance, and an emotion engine that recognizes the user's emotions. The terminals are smartphones or dedicated reading devices. The server is located in the cloud and processes information in real time. The emotion engine analyzes the user's facial expressions and voice data to identify their emotional state.

[0565] Operational Overview

[0566] 1. The user scans a QR code or NFC tag

[0567] The user scans QR codes or NFC tags installed at various points within the station with their device, which then acquires their current location information.

[0568] 2. The device sends the current location and destination information to the server.

[0569] The device sends the current location information and the destination information entered by the user to the server, which then receives all the necessary information.

[0570] 3. The emotion engine recognizes the user's emotions

[0571] The device uses the user's camera and microphone to collect facial and voice data, which the emotion engine analyzes to identify the user's emotional state, for example, whether the user is stressed or relaxed.

[0572] 4. The server calculates the optimal route and generates a route guidance message.

[0573] Based on the current location and destination information received by the server, the system refers to map data within the station and calculates the optimal route. Based on the calculation results, the generation AI generates a route guidance message in natural language that is easy for the user to understand. The emotion engine recognizes the user's emotional state and adjusts the message content accordingly. For example, if the user is feeling stressed, the system suggests an alternative route that will help them relax.

[0574] 5. The server generates a route guidance message and sends it to the terminal.

[0575] The generated message and accompanying illustrated maps and guide maps are sent from the server to the user's terminal.

[0576] 6. The device displays a route guidance message.

[0577] The user's device will display the received route guidance message, allowing the user to easily understand what to do next.

[0578] Specific examples

[0579] For example, suppose tourist C from overseas arrives at a station in a large city in Japan for the first time and is looking for the tourist information center on the other side of the station as his destination.

[0580] First, Mr. C uses his smartphone to scan the QR code on a guide board inside the station. Mr. C's device acquires the current location ID "east_exit_01." Next, Mr. C enters "Tourist Information Center" as his destination on the device screen and presses the send button.

[0581] The device sends current location and destination information to the server. At the same time, the device uses its camera and microphone to detect C's facial expressions and voice, and the emotion engine analyzes this to determine that C is feeling somewhat stressed.

[0582] The server receives the information "east_exit_01" and "tourist information center" and calculates the optimal route based on map data within the station. Based on the calculation results, the generation AI takes into account the information from the emotion engine and generates a route guidance message saying, "Exit the east exit, turn left, go straight through the underground passage, and you will see the tourist information center on your right." At this time, audio guidance is also provided to reduce Mr. C's stress.

[0583] The server sends the generated message and map to Mr. C's smartphone. Mr. C checks the message and audio guidance he receives and follows them to safely arrive at the tourist information center.

[0584] This system aims to further improve the user experience by not only helping users reach their destinations easily and accurately, but also by providing appropriate guidance that takes into account the user's emotional state, making it particularly useful in large, complex station premises.

[0585] The processing flow will be explained below.

[0586] Step 1:

[0587] The user scans the QR code or NFC tag.

[0588] The user holds their smartphone close to a QR code or NFC tag installed in the station and reads it. At this time, the device acquires the information from the QR code or NFC tag.

[0589] Step 2:

[0590] The terminal acquires current location information and prompts the user to input destination information.

[0591] The device analyzes and obtains the current location ID information from the QR code or NFC tag, and at the same time displays a destination input form on the device screen, prompting the user to enter the destination information.

[0592] Step 3:

[0593] The terminal acquires the user's emotion data.

[0594] The device uses the smartphone's camera and microphone to collect the user's facial and voice data, which is later sent to the emotion engine.

[0595] Step 4:

[0596] The device transmits current location information, destination information, and emotion data to the server.

[0597] After the user inputs the destination information, the device sends the current location information, the input destination information, and the collected emotion data to the server as a single request data. This data includes, for example, "currentLocationId: 'east_exit_01', destination: 'Tourist Information Center', emotionData: [facial expression data, voice data]".

[0598] Step 5:

[0599] The server receives the request data and identifies the current location and destination.

[0600] The server receives the data sent from the terminal and identifies the current location from the station map database based on the current location ID. It also identifies the destination location in the map database based on the destination information.

[0601] Step 6:

[0602] The server uses an emotion engine to analyze the user's emotional state.

[0603] The server analyzes the emotional data to identify the user's emotional state, for example, by analyzing facial expressions and voice to recognize whether the user is stressed or relaxed.

[0604] Step 7:

[0605] The server calculates the optimal route.

[0606] The server uses the current and destination locations to run a route planning algorithm within the station to calculate the optimal route, taking into account obstacles, restricted areas, and current pedestrian flow data.

[0607] Step 8:

[0608] The server generates a route guidance message using the generation AI.

[0609] The server inputs the calculated route information into the generation AI, which then generates a route guidance message in natural language that is easy for the user to understand. The emotion engine recognizes the user's emotional state and adjusts the message content accordingly. For example, if the user is feeling stressed, it suggests gentler language or an alternative route.

[0610] Step 9:

[0611] The server sends the generated guidance message and illustrated map to the terminal.

[0612] The server sends route guidance messages and related illustrated map data created by the AI ​​to the device, including emotion-based advice and words of encouragement.

[0613] Step 10:

[0614] The terminal displays an information message and an illustrated map.

[0615] The device displays the route guidance message and illustrated map it receives on the screen. Users can check them and understand what to do next. For example, specific directions such as "Turn left after exiting the east exit, go straight through the underground passage, and you will see the tourist information center on your right" are displayed. If the user is feeling stressed, a message such as "It's okay, just take it easy" is added.

[0616] Step 11:

[0617] The user follows the guidance to reach the destination.

[0618] The user follows the route in real time while viewing the guidance messages, illustrated maps, and audio guidance on the device, and safely arrives at the destination. The emotion engine monitors the user's emotional state appropriately and readjusts the route guidance messages if necessary.

[0619] This step not only allows users to reach their destination easily and accurately, but also allows them to receive appropriate guidance that takes their emotional state into account.

[0620] Example 2

[0621] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0622] Accurate and easy-to-understand route guidance is required for users to reach their destinations without getting lost in train stations and large facilities. However, conventional navigation systems have had difficulty providing appropriate route guidance according to the user's emotional state. For example, flexible guidance that can respond to users who are feeling stressed or nervous is required. The present invention aims to solve this problem and provide appropriate route guidance according to the user's emotional state.

[0623] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0624] In this invention, the server includes means for acquiring current location information, means for transmitting destination information input by the user to the server, means for the server to calculate the optimal route and generate a route guidance message using a generation AI, means for the terminal to collect facial expression and voice data and include an emotion engine that analyzes the emotional state, and means for adjusting the content of the route guidance message based on the results of the emotion engine. This enables users to avoid getting lost in train stations or large facilities and to receive optimal route guidance according to their emotional state at the time.

[0625] "Current location information" is digital data that indicates the user's location within a station or large facility.

[0626] "Destination information" is digital data that indicates the final location or destination that the user is aiming for.

[0627] A "server" is a computer system that processes data and provides information via a network.

[0628] "Generative AI" is a program that uses artificial intelligence technology to generate route guidance messages in natural language.

[0629] A "terminal" is a device used by a user to read QR codes or NFC tags, such as a smartphone or a dedicated reading device.

[0630] A "QR code" is a code that encodes information into a rectangular grid and can be read in two dimensions.

[0631] An "NFC tag" is a tag that can transmit and receive data using near-field wireless communication technology.

[0632] An "emotion engine" is a software system that analyzes a user's facial expressions and voice data to identify their emotional state.

[0633] A "route guidance message" is a message provided to a user that contains instructions and explanations for reaching a destination.

[0634] This invention is a navigation system that helps users reach their destinations without getting lost in complex and vast train stations and large-scale facilities. This system provides more accurate and personalized guidance by incorporating an emotion engine that recognizes the user's emotions and adjusts route guidance messages accordingly.

[0635] This system consists of the following elements: QR codes or NFC tags installed at multiple locations within stations and facilities; terminals for reading these; a server for generating route guidance; and an emotion engine for recognizing the user's emotions. The terminals function as smartphones or dedicated reading devices, while the server is located in the cloud and processes information in real time. The emotion engine analyzes the user's facial expressions and voice data to identify their emotional state.

[0636] The flow of operation of the system is explained below.

[0637] When a user uses their device to scan QR codes or NFC tags installed at various points within the station, their current location information is acquired by the device. Next, the user enters their destination into the device and sends it along with their current location information to the server. At this time, the device uses its camera and microphone to collect the user's facial expressions and voice data, which is analyzed by an emotion engine. The server calculates the optimal route based on the received current location and destination information. A generative AI (e.g., OpenAI's GPT-4) generates an easy-to-understand route guidance message in natural language. Furthermore, the content of the message is adjusted according to the user's emotional state as recognized by the emotion engine.

[0638] For example, imagine a tourist from overseas arrives at a station in a major Japanese city for the first time and is looking for the tourist information center on the other side of the station as his or her destination.

[0639] When a user scans a QR code on a guide board inside the station with their smartphone, the device obtains the current location ID "east_exit_01." Next, they enter "Tourist Information Center" as their destination on the device screen and press the send button. The device then sends the current location and destination information to the server. At the same time, the device uses its camera and microphone to detect the user's facial expressions and voice, which the emotion engine analyzes and determines that the user is feeling somewhat stressed. Based on this information, the server calculates the optimal route by referencing map data within the station and generates a route guidance message that reads, "Exit the east exit, turn left, and go straight through the underground passage; you will see the Tourist Information Center on your right." Audio guidance is also provided at this time to reduce the user's stress. The server then sends the generated message and map to the user's smartphone, where the user confirms the message and map and safely arrives at the Tourist Information Center.

[0640] An example of a prompt to be input to the generative AI model is as follows:

[0641] "The user's current location is the east exit and their destination is the tourist information center. They're feeling a bit stressed. Use this information to generate a message that will guide them to a more relaxing route."

[0642] As described above, this system aims to improve the user experience by providing appropriate guidance according to the user's emotional state, making it particularly useful in large, complex train stations and other large-scale facilities.

[0643] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0644] Step 1: User scans a QR code or NFC tag

[0645] The user scans a QR code or NFC tag installed at a designated point within a station or facility using a device such as a smartphone.

[0646] Input: QR code or NFC tag

[0647] Data processing: The device reads the QR code or NFC tag information and obtains the current location information.

[0648] Output: Current location information (e.g. "east_exit_01")

[0649] Step 2: The device sends the current location and destination information to the server.

[0650] The user inputs destination information into the terminal and transmits it to the server together with current location information.

[0651] Input: Current location information, destination information (e.g. "tourist information center")

[0652] Data processing: Packages the current location information and destination information and sends it to the server via the network.

[0653] Output: Current location and destination information received by the server

[0654] Step 3: The device collects facial and voice data

[0655] The user's device uses a camera and microphone to collect facial expressions and voice data.

[0656] Input: User's facial expression data, voice data

[0657] Data processing: Collect data in real time using cameras and microphones and send that data to the emotion engine.

[0658] Output: Collected facial expression data and voice data

[0659] Step 4: The emotion engine analyzes the user's emotions

[0660] The emotion engine analyzes facial expression and voice data to identify the user's emotional state.

[0661] Input: facial expression data, voice data

[0662] Data processing: Using data analysis algorithms to identify emotional states such as stress or relaxation

[0663] Output: Identified emotional state (e.g., "stressed")

[0664] Step 5: The server calculates the optimal route

[0665] The server calculates the optimal route based on the current location information and destination information, and refers to map data within the station or facility.

[0666] Input: Current location information, destination information, map data

[0667] Data processing: Using an algorithm to calculate the optimal route from your current location to your destination

[0668] Output: Calculated optimal route data

[0669] Step 6: The server generates a route guidance message using the generation AI.

[0670] The server uses a generative AI (e.g., GPT-4) to generate a natural language route guidance message based on the optimal route, adjusting the message content based on the results of the emotion engine.

[0671] Input: optimal route data, emotional state

[0672] Data processing: Generative AI uses natural language processing algorithms to generate route guidance messages

[0673] Output: Generated route guidance message (e.g. "Turn left after exiting the east exit, go straight through the underground passage and you will see the tourist information center on your right")

[0674] Step 7: The server generates a route guidance message and sends it to the device.

[0675] The server sends route guidance messages and accompanying maps and directions to the user's terminal.

[0676] Input: Route guidance message, map data

[0677] Data processing: Packages message and map data and sends them to the terminal via the network

[0678] Output: Route guidance message and map sent to device

[0679] Step 8: The device displays a route guidance message.

[0680] The user's device displays the received route guidance message and plays back the audio guide, allowing the user to reach their destination.

[0681] Input: Received route guidance message, map data

[0682] Data processing: The device displays the message on the screen and plays the audio guide.

[0683] Output: The displayed and played guidance information allows the user to navigate to their destination.

[0684] In this way, users can receive accurate and emotionally sensitive route guidance in their local environment.

[0685] (Application example 2)

[0686] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0687] Navigation systems are needed to help workers navigate large, complex facilities such as factories without getting lost. In particular, there is a need to reduce stress and confusion by providing guidance messages that take the user's emotional state into account. However, conventional navigation systems are unable to generate route guidance messages that take the user's emotional state into account, making it difficult to reduce user stress.

[0688] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0689] In this invention, the server includes means for calculating an optimal route based on current location information and destination information, means for generating a route guidance message using a generation AI, means for analyzing the emotional state of the user using a camera and microphone and transmitting the results to the server, and means for the server to adjust the route guidance message taking the emotional state into consideration, thereby enabling the user to reach the destination easily and reducing stress and confusion.

[0690] A "QR code" is a type of two-dimensional barcode that visually represents information and can be read by a device with a camera.

[0691] An "NFC tag" is an information recording medium that uses near-field wireless communication technology, and is a tag that can read and write data when a compatible device comes close.

[0692] "Location information" is information that indicates that a user is in a specific location, and is data obtained through a QR code or NFC tag.

[0693] "Destination information" is information about the location the user is heading for, and is data that is specified by the user entering it into the terminal.

[0694] A "server" is a computer system that stores and processes data on a network, and is a device that performs tasks such as route calculations and generating guidance messages.

[0695] A "route guidance message" is a message containing instructions for users to reach their destination from their current location, and is created by a generation AI.

[0696] "Device" means an input / display device held by a user, such as a smartphone, tablet, smart glasses, or head-mounted display.

[0697] A "camera" is a device for acquiring visual information, and is built into a terminal and used to capture the user's facial expression.

[0698] A "microphone" is a device for acquiring voice information, and is built into a terminal and used to record the user's voice.

[0699] "Emotional state" is information that indicates the user's psychological and emotional state, and is analyzed based on data obtained through cameras and microphones.

[0700] "Generative AI" is a type of artificial intelligence that refers to programs or models that perform natural language processing based on large amounts of data and generate route guidance messages.

[0701] "Adjustment" refers to the act of changing the content and presentation method of a route guidance message depending on the user's emotional state.

[0702] This invention relates to a navigation system that enables workers to reach their destinations without getting lost in a complex and vast factory. In particular, it relates to a system that combines an emotion engine that recognizes the user's emotions and adjusts route guidance messages.

[0703] System configuration

[0704] The system consists of QR codes or NFC tags installed at multiple locations within the factory, terminals for reading them, a server that generates route guidance, and an emotion engine that recognizes the user's emotions. The terminals are smartphones, smart glasses, head-mounted displays, etc. The server is located on the cloud and processes information in real time. The emotion engine analyzes the user's facial expressions and voice data to identify their emotional state.

[0705] System Operation

[0706] Read QR codes and NFC tags and determine current location

[0707] The user scans a QR code or NFC tag installed at a designated location in the factory with the device, which then acquires the current location information.

[0708] Setting the destination and sending to the server

[0709] The device operated by the user transmits the acquired current location information and the destination information entered by the user to the server, which then receives data for calculating the optimal route based on this information.

[0710] Recognition of emotional states

[0711] The device uses a built-in camera and microphone to collect facial and voice data from the user, and the emotion engine analyzes this data to determine the user's emotional state, such as whether they are stressed or relaxed.

[0712] Generate route guidance messages

[0713] The server calculates the optimal route based on the received current location and destination information, as well as the user's emotional state. The server's AI uses natural language processing to generate a route guidance message that is easy for the user to understand. The content of the message is adjusted according to the user's emotional state.

[0714] Sending and displaying information messages

[0715] The server generates route guidance messages and sends them to the user's device along with accompanying illustrated maps and guide maps. The user can then check the messages on their device and follow the instructions to reach their destination.

[0716] Hardware and software used

[0717] QR codes and NFC tags: Used to identify your location.

[0718] Devices: Smartphones, smart glasses, head-mounted displays (e.g., Samsung Galaxy S21, Google Glass, Microsoft HoloLens 2).

[0719] Server: Cloud server (e.g., Amazon Web Services (AWS)).

[0720] Emotion engine: Analyzes data captured by the camera and microphone (e.g., Amazon Rekognition, Amazon Comprehend).

[0721] Generative AI: Natural language processing models (e.g., GPT-4 (OpenAI)).

[0722] Specific examples

[0723] Below are some specific examples of how this system can be used.

[0724] Worker A, heading to Parts Warehouse B in the factory, scans a QR code with his smartphone to identify his current location. Worker A's device retrieves "Section A" and then enters "Parts Warehouse B" as his destination. At the same time, the camera and microphone record Worker A's facial expressions and voice. The emotion engine determines that Worker A is feeling stressed.

[0725] The server receives information about "Section A" and "Parts Warehouse B" and calculates the optimal route. The generation AI takes into account information from the emotion engine and generates a route guidance message suggesting a relaxing route. The message generated reads, "Exit production line section A, turn right and go straight. Turn left at the third intersection and take the elevator down to the first floor. Go straight and you will see Parts Warehouse B on your right."

[0726] The server sends the generated message and map to Person A's smartphone. Person A checks the received message and follows the instructions to safely reach Parts Warehouse B.

[0727] The following text is used as an example of a prompt sentence:

[0728] plain text

[0729] Your current location is assembly line section A and your destination is parts warehouse B. Your emotional state is stressed.

[0730] This not only allows users to reach their destinations more easily, but also reduces stress and confusion.

[0731] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0732] Step 1:

[0733] Obtaining current location information

[0734] The user scans a QR code or NFC tag installed in a location within the factory with a device (smartphone, smart glasses, head-mounted display). This allows the device to acquire data indicating the current location as input. The acquired current location information is recognized as an identification ID and used for the next process.

[0735] Input: QR code or NFC tag

[0736] Output: Current location ID

[0737] Step 2:

[0738] Setting destination information

[0739] The user inputs the next destination (e.g., a parts warehouse or other work section) into the terminal. The terminal receives this destination information as input. The destination information is used in the next step of route calculation.

[0740] Input: Destination information (user input)

[0741] Output: Destination ID

[0742] Step 3:

[0743] Recognition of emotional states

[0744] The device's camera and microphone are used to collect the user's facial expressions and voice data. The emotion engine analyzes this data to identify the user's emotional state (e.g., whether they are stressed or relaxed). The analyzed emotion data is used in the next step.

[0745] Input: User's facial expression data and voice data (input from camera and microphone)

[0746] Output: Emotional state (stressed, relaxed, etc.)

[0747] Step 4:

[0748] Generate route guidance messages

[0749] The server receives current location information, destination information, and emotional state as input and calculates the optimal route. Based on the calculation results, the generation AI generates easy-to-understand route guidance messages in natural language. The route and message content are adjusted depending on the emotional state.

[0750] Input: Current location, destination, emotional state

[0751] Output: Route guidance message

[0752] Step 5:

[0753] Sending an information message

[0754] The server generates a route guidance message and sends it to the user's device along with an accompanying illustrated map or guide map. The message is then displayed in the final step.

[0755] Input: Route guidance message (generated by AI)

[0756] Output: Sent message and map

[0757] Step 6:

[0758] Displaying route guidance messages

[0759] The user's device displays the route guidance message received from the server, allowing the user to receive visual and audio instructions to reach the destination.

[0760] Input: Sent message and map (received from server)

[0761] Output: Displayed route guidance message

[0762] Specific operation example

[0763] In the case of Worker A heading to Parts Warehouse B in the factory:

[0764] Step 1: Person A scans the QR code with their smartphone and obtains their current location as "Section A."

[0765] Step 2: Person A enters "Parts Warehouse B" as the destination into the terminal.

[0766] Step 3: Using a camera and microphone, facial and voice data from Person A is collected, and the emotion engine determines that Person A is feeling stressed.

[0767] Step 4: The server receives information about "Section A" and "Parts Warehouse B" and calculates the optimal route. The generation AI generates a route guidance message to reduce stress.

[0768] Step 5: The server sends the generated message and map to Mr. A's smartphone.

[0769] Step 6: Person A checks the message and map on his smartphone and follows it to reach "Parts Warehouse B."

[0770] The following is used as an example prompt sentence:

[0771] plain text

[0772] Your current location is assembly line section A and your destination is parts warehouse B. Your emotional state is stressed.

[0773] 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.

[0774] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0775] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0776] [Third embodiment]

[0777] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0778] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0779] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0780] 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.

[0781] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0782] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0783] 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.

[0784] 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.

[0785] 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 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.

[0786] 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.

[0787] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0788] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0789] The present invention relates to a navigation system that enables users to reach their destinations without getting lost in a complex and vast station premises. Specific embodiments of this system will be described below.

[0790] System configuration

[0791] The system consists of a station where QR codes or NFC tags are installed, a terminal for reading them, and a server that generates route guidance. The terminal is a smartphone or a dedicated reading device. The server is located in the cloud and processes information in real time.

[0792] Operational Overview

[0793] 1. The user scans a QR code or NFC tag

[0794] The user scans QR codes or NFC tags installed at various points within the station with their device, which then acquires their current location information.

[0795] 2. The device sends the current location and destination information to the server.

[0796] The device sends the acquired current location information and the destination information entered by the user to the server, which then receives all the necessary information.

[0797] 3. The server calculates the optimal route and generates a route guidance message.

[0798] The server calculates the optimal route based on the received current location and destination information, referencing the station's map data. Based on the calculation results, the generation AI generates a route guidance message in natural language that is easy for the user to understand. For example, a message might say, "Go to Exit 5, take the escalator and go through the underground passage, and you will arrive at the north exit."

[0799] 4. The server generates a route guidance message and sends it to the terminal.

[0800] The generated message and accompanying illustrated maps and guide maps are sent from the server to the user's terminal.

[0801] 5. The device displays a route guidance message.

[0802] The user's device will display the received route guidance message, allowing the user to easily understand what to do next.

[0803] Specific examples

[0804] For example, suppose tourist B from overseas arrives at a station in a large city in Japan for the first time and is looking for the tourist information center on the other side of the station as his destination.

[0805] First, Person B uses his smartphone to scan the QR code on a guide board inside the station. Person B's device acquires the current location ID "east_exit_01." Next, Person B enters "Tourist Information Center" as his destination on the device screen and presses the send button.

[0806] The device sends its current location and destination information to the server. The server receives the information "east_exit_01" and "tourist information center" and calculates the optimal route based on map data within the station. The AI ​​then generates a route guidance message and illustrated map, saying, "Exit the east exit, turn left, and go straight through the underground passage. You will see the tourist information center on your right."

[0807] The server sends the generated message and map to Person B's smartphone. Person B checks the message on the screen and follows the instructions to safely arrive at the tourist information center.

[0808] This system helps users reach their destinations easily and accurately, making it particularly useful in large, complex station premises.

[0809] The processing flow will be explained below.

[0810] Step 1:

[0811] The user scans a QR code or NFC tag in the station.

[0812] The user holds their smartphone close to a QR code or NFC tag installed in the station and reads it. At this time, the device acquires the information from the QR code or NFC tag.

[0813] Step 2:

[0814] The terminal acquires current location information and prompts the user to input destination information.

[0815] The device analyzes and obtains the current location ID information from the QR code or NFC tag, and at the same time displays a destination input form on the device screen, prompting the user to enter the destination information.

[0816] Step 3:

[0817] The device sends current location information and destination information to the server.

[0818] After the user inputs the destination information, the device sends the current location information and the input destination information to the server as a single request data. This data includes, for example, "currentLocationId: 'east_exit_01', destination: 'Tourist Information Center'".

[0819] Step 4:

[0820] The server receives the request data and identifies the current location and destination.

[0821] The server receives the data sent from the terminal and identifies the current location from the station map database based on the current location ID. It also identifies the destination location in the map database based on the destination information.

[0822] Step 5:

[0823] The server calculates the optimal route.

[0824] The server uses the current and destination locations to run a route planning algorithm within the station to calculate the optimal route, taking into account obstacles, restricted areas, and current pedestrian flow data.

[0825] Step 6:

[0826] The server generates a route guidance message using the generation AI.

[0827] The server inputs the calculated route information into the generation AI, which then generates a route guidance message in natural language that is easy for users to understand. For example, a message such as, "Exit the east exit, turn left, and go straight through the underground passage; you will see the tourist information center on your right."

[0828] Step 7:

[0829] The server sends the generated guidance message and illustrated map to the terminal.

[0830] The server sends the route guidance message created by the AI ​​and related illustrated map data to the device.

[0831] Step 8:

[0832] The terminal displays an information message and an illustrated map.

[0833] The device displays the received route guidance message and illustrated map on the screen, and the user can check them and follow the instructions to head to their destination.

[0834] Step 9:

[0835] The user follows the guidance to reach the destination.

[0836] The user follows the route in real time while looking at the guidance messages and illustrated map on the device, and safely reaches the destination.

[0837] Example 1

[0838] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0839] There is a need to provide navigation that will enable users to quickly and accurately reach their destinations within vast and complex station premises without getting lost. Conventional guidance systems make it difficult for users to grasp their exact current location or the appropriate route. Furthermore, because they do not provide guidance based on information updated in real time, they are unable to respond when routes change due to construction, events, etc. Therefore, a new system is needed to solve these issues.

[0840] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0841] In this invention, the server includes means for acquiring the user's current location using codes or tags installed at multiple locations within the station, means for transmitting the current location information and destination information entered by the user to the processing device, means for the processing device to calculate an optimal route based on the current location information and the destination information, means for generating a route guidance message using the generation system, means for transmitting the generated route guidance message to the user's display device, and means for the display device to display the route guidance message. This allows users to easily and quickly check their current location and destination and receive optimal route guidance in real time.

[0842] "Code" refers to QR codes or other similar technology that are placed at various locations within the station for reading by passenger devices.

[0843] "Tag" refers to an NFC tag or other similar technology that is placed at various locations within a station to be read by a passenger's device.

[0844] "Current location information" refers to information indicating the user's current location, which is obtained by the user reading codes or tags installed at various locations within the station.

[0845] "Destination information" refers to information about a desired location that is specified by a user by inputting it into a terminal.

[0846] The "processing device" refers to a computer system or server that receives current location information and destination information and calculates the optimal route based on that information.

[0847] "Generation system" refers to artificial intelligence (AI) that generates easy-to-understand route guidance messages in natural language based on optimal route information calculated by a processing device.

[0848] "Route Guidance Message" means a message containing instructions regarding the optimal route from a current location to a destination, provided in a format that is easy for a user to understand.

[0849] "Display device" refers to a device, primarily a smartphone or dedicated terminal, that receives the route guidance message generated by the generation system and displays it to the user.

[0850] The present invention relates to a navigation system that enables users to reach their destinations without getting lost in a complex and vast station premises. Specific embodiments of this system will be described below.

[0851] System configuration

[0852] This system consists of codes or tags installed in stations, terminals for reading them, and a processing device (server) that generates route guidance. The terminals are smartphones or dedicated reading devices, and the processing device is often located on the cloud. Cloud computing platforms, for example, can be used as cloud services.

[0853] Program processing

[0854] 1. The user reads the code or tag

[0855] Users use their smartphones or other dedicated devices to scan codes or tags placed at various locations within the station, which allows the device to acquire information about the user's current location.

[0856] 2. The device sends the current location and destination information to the server.

[0857] The user enters destination information into their smartphone and presses the send button. The device then sends the current location and destination information to the server. This process is carried out via a dedicated application or browser on the device.

[0858] 3. The server calculates the optimal route and generates a route guidance message.

[0859] The server calculates the optimal route based on the received current location information and destination information, for example, using a map platform API. Furthermore, it generates an easy-to-understand route guidance message in natural language using a generative AI model (for example, OpenAI's GPT-4). An example of a prompt used in this case is shown below.

[0860] Current location: east_exit_01

[0861] Destination: Tourist Information Center

[0862] Please provide natural language guidance on the best way to get from your current location to your destination, using a large map of the station. For example, please include exit numbers, instructions on how to use escalators, directions to underground passages, etc. Also, please generate an easy-to-understand illustrated map.

[0863] Based on this prompt, the server generates a specific route guidance message such as, "Exit the east exit, turn left, go straight through the underground passage and you will see the tourist information center on your right."

[0864] 4. The server generates a route guidance message and sends it to the terminal.

[0865] The generated route guidance message and illustrated map are sent from the server to the user's device using cloud communication technology, such as a cloud messaging service.

[0866] 5. The device displays a route guidance message.

[0867] The user's device displays the received route guidance message, allowing the user to understand what to do next and reach their destination.

[0868] Usage example

[0869] For example, consider the case where a tourist is looking for a tourist information center at a train station in a large city they are visiting for the first time. The user scans a QR code installed near the east exit of the station with their smartphone and obtains the current location ID "east_exit_01." Next, they enter the destination information "tourist information center" and submit it. The server calculates the optimal route based on this information and uses a generative AI model to generate a message that says, "Exit the east exit, turn left, and go straight through the underground passage; you will see the tourist information center on your right," which is sent to the user's device. The user follows this message and arrives safely at their destination.

[0870] This system allows users to reach their destinations quickly and without getting lost even in vast and complex station premises.

[0871] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0872] Step 1:

[0873] The user scans a code or tag inside the station.

[0874] Users use their smartphones or dedicated devices to scan codes and tags placed throughout the station to obtain location information. Specifically, a QR code reader app or NFC-enabled device is used. The input is the identification information embedded in the code or tag, and the output is a location ID such as "east_exit_01" sent to the device.

[0875] Step 2:

[0876] The device sends current location information and destination information to the server.

[0877] The user enters destination information into the terminal and presses the send button. The terminal then sends the acquired current location information and the destination information entered by the user to the server via the Internet. The inputs include the current location ID and destination information (e.g., "tourist information center"), and the output is this information sent to the server.

[0878] Step 3:

[0879] The server calculates the optimal route and generates a route guidance message.

[0880] The server calculates the optimal route by referencing map data based on the received current location information and destination information. For example, a map data API is used to calculate the route, and a prompt sentence is entered into a generative AI model (e.g., GPT-4) to generate a route guidance message. The input includes the current location ID and destination information, and data processing involves route calculation and natural language generation by the generative AI model. The output is a route guidance message such as, "Exit the east exit, turn left, and go straight through the underground passage; you will see the tourist information center on your right."

[0881] Step 4:

[0882] The server sends the generated route guidance message to the terminal.

[0883] The server sends the generated route guidance message and the illustrated map to the user's terminal via the Internet. The input is the route guidance message and the accompanying illustrated map, and the output is sent to the user's terminal.

[0884] Step 5:

[0885] The device displays a route guidance message.

[0886] The terminal displays the received route guidance message and illustrated map. By referring to this, the user can intuitively understand how to get to their destination. The input is the route guidance message and illustrated map sent from the server, and the output is this information displayed on the terminal screen. In concrete terms, the message and map are displayed on the terminal screen, and the user checks them before taking action.

[0887] (Application example 1)

[0888] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0889] In large, complex commercial facilities and large stores, users often get lost before reaching their destination. This complexity significantly reduces user convenience, especially for first-time visitors or those looking for a specific product or section. Furthermore, the lack of real-time guidance makes it difficult to navigate in a facility based on the current situation. For this reason, there is a need for an effective navigation system that can help users reach their destination smoothly.

[0890] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0891] In this invention, the server includes means for acquiring a user's current location using QR codes or NFC tags installed at multiple locations within the commercial facility, means for transmitting the current location information and destination information entered by the user to the server, means for the server to calculate an optimal route based on the current location information and destination information, means for the server to generate a route guidance message using a generation AI, means for the server to transmit the route guidance message to the user's terminal, and means for the terminal to display the route guidance message, thereby enabling users to quickly and accurately reach their destination within the commercial facility without getting lost.

[0892] "Commercial facility" refers to a large building or area that houses multiple stores and related facilities offering goods or services.

[0893] "QR Code" is a type of two-dimensional barcode that is a rectangular code that can efficiently store information and be quickly read.

[0894] An "NFC tag" is an electronic tag that uses Near Field Communication technology to read and write information without contact.

[0895] "User" means a customer who visits a commercial establishment and uses goods or services.

[0896] "Current location information" refers to information about the user's current location, obtained by reading a QR code or NFC tag.

[0897] "Destination information" refers to information about a specific location or store that a user wants to reach within a commercial facility.

[0898] A "server" is a computer system that communicates with multiple terminals via a network and stores and processes data.

[0899] The "optimal route" refers to the most efficient and shortest route between the current location and the destination.

[0900] "Generative AI" refers to a system that uses artificial intelligence technology to generate messages and information based on natural language.

[0901] A "route guidance message" is a message that provides the user with easy-to-understand guidance on how to reach a destination.

[0902] "Terminal" refers to a device used by a user, such as a mobile information device or smartphone.

[0903] The present invention relates to a navigation system for assisting users in smoothly reaching their destinations in commercial facilities. Specific embodiments of this system will be described below.

[0904] System Configuration

[0905] This system consists of QR codes or NFC tags installed within the commercial facility, a user's device to read them, and a server to process the data.User devices are typically smartphones, and QR codes or NFC tags are installed at multiple points within the facility, such as at the entrance and near the escalators on each floor.

[0906] Operational Overview

[0907] 1. Reading a QR code or NFC tag

[0908] Users can obtain location information by scanning QR codes or NFC tags in commercial facilities with their smartphones. QR codes are read using the smartphone's camera and the ZXing library, and NFC tags are read using the smartphone's NFC reader and the Android NFC API.

[0909] 2. Sending current location and destination information

[0910] The user enters the name of the desired store or department in the application and presses the send button. The device then sends the acquired current location information and the entered destination information to the server. The server receives the current location information and destination information via API communication and processes the data.

[0911] 3. Calculating the optimal route and generating guidance messages

[0912] The server refers to the commercial facility's map data based on the received current location information and destination information and calculates the optimal route. This route calculation uses the A algorithm. After calculating the optimal route, it uses the generative AI model GPT-4 to generate an easy-to-understand route guidance message in natural language. The following is used as an example of a prompt sentence.

[0913] Prompt Sentence Examples

[0914] "Generate a guidance message for when a user scans a QR code at the entrance of a commercial facility and enters the women's clothing department as the destination. Starting point: Entrance Arrival point: Women's clothing department Optimal route based on map information: Turn right after entering the entrance, take the escalator to the second floor, and go straight. You will see the women's clothing department on your left."

[0915] 4. Sending a message to the device

[0916] The generated route guidance message, illustrated map, and guide map are sent from the server to the user's device. The message data is sent via API communication and displayed on the user's smartphone.

[0917] 5. Display of route guidance messages

[0918] The user's device displays the received route guidance message. Based on the message and map displayed on the device, the user can navigate to their destination. This display is done using React Native, a mobile application framework.

[0919] Specific examples

[0920] For example, consider the case where Person A is looking for the "women's clothing section" at a large shopping mall that she is visiting for the first time. Person A scans the QR code at the entrance, enters "women's clothing section" into the application, and submits it. Based on this information, the server generates a guidance message and map that says, "Enter the entrance, turn right, take the escalator to the second floor, and go straight; you will see the women's clothing section on your left," and sends this to Person A's smartphone. Person A can follow the guidance and arrive at the women's clothing section without getting lost.

[0921] This system allows users to reach their destinations efficiently and accurately even within large, complex commercial facilities.

[0922] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0923] Step 1:

[0924] A user scans a QR code or NFC tag in a commercial facility with their smartphone. This allows the current location information (for example, location information near the entrance) to be obtained using the camera and ZXing library in the case of a QR code, or the NFC reader and Android NFC API in the case of an NFC tag. Specifically, by reading the QR code, a text-formatted current location ID is entered into the smartphone.

[0925] Step 2:

[0926] The device obtains destination information based on the user's input. The user enters the name of the desired store or department (for example, "Women's clothing department") on the application screen and presses the send button. This brings the current location information and destination information together on the device.

[0927] Step 3:

[0928] The device sends the acquired current location information and destination information to the server. The server then receives the necessary data. Specifically, the current location ID and destination name are sent to the server via API communication. The data sent is in a standardized format such as JSON.

[0929] Step 4:

[0930] The server calculates the optimal route based on the current location and destination information. The server references the map data of the commercial facility and uses algorithms such as A to calculate the shortest and most efficient route. The current location and destination information are input, and a list of routes is generated as output.

[0931] Step 5:

[0932] The server uses a generative AI model (GPT-4) to generate a route guidance message. The server inputs the calculated route information as a prompt sentence into the generative AI, which generates an easy-to-understand route guidance message in natural language. The output is a route guidance message (e.g., "Enter the entrance, turn right, take the escalator to the second floor, and go straight; you will see the women's clothing department on your left").

[0933] Step 6:

[0934] The server generates route guidance messages and sends illustrated maps to the device, and text and image data are sent to the smartphone via API communication.

[0935] Step 7:

[0936] The device displays the received route guidance message and an illustrated map. The user can check the message and follow the visual map. Specifically, the screen displaying the message and map is rendered on the device using React Native, a mobile application framework.

[0937] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0938] The present invention relates to a navigation system that enables users to reach their destination without getting lost in a complex and vast train station. In particular, the present invention relates to a system that combines an emotion engine that recognizes the user's emotions and adjusts route guidance messages.

[0939] System configuration

[0940] The system consists of QR codes or NFC tags installed at multiple locations within the station, terminals for reading them, a server that generates route guidance, and an emotion engine that recognizes the user's emotions. The terminals are smartphones or dedicated reading devices. The server is located in the cloud and processes information in real time. The emotion engine analyzes the user's facial expressions and voice data to identify their emotional state.

[0941] Operational Overview

[0942] 1. The user scans a QR code or NFC tag

[0943] The user scans QR codes or NFC tags installed at various points within the station with their device, which then acquires their current location information.

[0944] 2. The device sends the current location and destination information to the server.

[0945] The device sends the current location information and the destination information entered by the user to the server, which then receives all the necessary information.

[0946] 3. The emotion engine recognizes the user's emotions

[0947] The device uses the user's camera and microphone to collect facial and voice data, which the emotion engine analyzes to identify the user's emotional state, for example, whether the user is stressed or relaxed.

[0948] 4. The server calculates the optimal route and generates a route guidance message.

[0949] Based on the current location and destination information received by the server, the system refers to map data within the station and calculates the optimal route. Based on the calculation results, the generation AI generates a route guidance message in natural language that is easy for the user to understand. The emotion engine recognizes the user's emotional state and adjusts the message content accordingly. For example, if the user is feeling stressed, the system suggests an alternative route that will help them relax.

[0950] 5. The server generates a route guidance message and sends it to the terminal.

[0951] The generated message and accompanying illustrated maps and guide maps are sent from the server to the user's terminal.

[0952] 6. The device displays a route guidance message.

[0953] The user's device will display the received route guidance message, allowing the user to easily understand what to do next.

[0954] Specific examples

[0955] For example, suppose tourist C from overseas arrives at a station in a large city in Japan for the first time and is looking for the tourist information center on the other side of the station as his destination.

[0956] First, Mr. C uses his smartphone to scan the QR code on a guide board inside the station. Mr. C's device acquires the current location ID "east_exit_01." Next, Mr. C enters "Tourist Information Center" as his destination on the device screen and presses the send button.

[0957] The device sends current location and destination information to the server. At the same time, the device uses its camera and microphone to detect C's facial expressions and voice, and the emotion engine analyzes this to determine that C is feeling somewhat stressed.

[0958] The server receives the information "east_exit_01" and "tourist information center" and calculates the optimal route based on map data within the station. Based on the calculation results, the generation AI takes into account the information from the emotion engine and generates a route guidance message saying, "Exit the east exit, turn left, go straight through the underground passage, and you will see the tourist information center on your right." At this time, audio guidance is also provided to reduce Mr. C's stress.

[0959] The server sends the generated message and map to Mr. C's smartphone. Mr. C checks the message and audio guidance he receives and follows them to safely arrive at the tourist information center.

[0960] This system aims to further improve the user experience by not only helping users reach their destinations easily and accurately, but also by providing appropriate guidance that takes into account the user's emotional state, making it particularly useful in large, complex station premises.

[0961] The processing flow will be explained below.

[0962] Step 1:

[0963] The user scans the QR code or NFC tag.

[0964] The user holds their smartphone close to a QR code or NFC tag installed in the station and reads it. At this time, the device acquires the information from the QR code or NFC tag.

[0965] Step 2:

[0966] The terminal acquires current location information and prompts the user to input destination information.

[0967] The device analyzes and obtains the current location ID information from the QR code or NFC tag, and at the same time displays a destination input form on the device screen, prompting the user to enter the destination information.

[0968] Step 3:

[0969] The terminal acquires the user's emotion data.

[0970] The device uses the smartphone's camera and microphone to collect the user's facial and voice data, which is later sent to the emotion engine.

[0971] Step 4:

[0972] The device transmits current location information, destination information, and emotion data to the server.

[0973] After the user inputs the destination information, the device sends the current location information, the input destination information, and the collected emotion data to the server as a single request data. This data includes, for example, "currentLocationId: 'east_exit_01', destination: 'Tourist Information Center', emotionData: [facial expression data, voice data]".

[0974] Step 5:

[0975] The server receives the request data and identifies the current location and destination.

[0976] The server receives the data sent from the terminal and identifies the current location from the station map database based on the current location ID. It also identifies the destination location in the map database based on the destination information.

[0977] Step 6:

[0978] The server uses an emotion engine to analyze the user's emotional state.

[0979] The server analyzes the emotional data to identify the user's emotional state, for example, by analyzing facial expressions and voice to recognize whether the user is stressed or relaxed.

[0980] Step 7:

[0981] The server calculates the optimal route.

[0982] The server uses the current and destination locations to run a route planning algorithm within the station to calculate the optimal route, taking into account obstacles, restricted areas, and current pedestrian flow data.

[0983] Step 8:

[0984] The server generates a route guidance message using the generation AI.

[0985] The server inputs the calculated route information into the generation AI, which then generates a route guidance message in natural language that is easy for the user to understand. The emotion engine recognizes the user's emotional state and adjusts the message content accordingly. For example, if the user is feeling stressed, it suggests gentler language or an alternative route.

[0986] Step 9:

[0987] The server sends the generated guidance message and illustrated map to the terminal.

[0988] The server sends route guidance messages and related illustrated map data created by the AI ​​to the device, including emotion-based advice and words of encouragement.

[0989] Step 10:

[0990] The terminal displays an information message and an illustrated map.

[0991] The device displays the route guidance message and illustrated map it receives on the screen. Users can check them and understand what to do next. For example, specific directions such as "Turn left after exiting the east exit, go straight through the underground passage, and you will see the tourist information center on your right" are displayed. If the user is feeling stressed, a message such as "It's okay, just take it easy" is added.

[0992] Step 11:

[0993] The user follows the guidance to reach the destination.

[0994] The user follows the route in real time while viewing the guidance messages, illustrated maps, and audio guidance on the device, and safely arrives at the destination. The emotion engine monitors the user's emotional state appropriately and readjusts the route guidance messages if necessary.

[0995] This step not only allows users to reach their destination easily and accurately, but also allows them to receive appropriate guidance that takes their emotional state into account.

[0996] Example 2

[0997] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0998] Accurate and easy-to-understand route guidance is required for users to reach their destinations without getting lost in train stations and large facilities. However, conventional navigation systems have had difficulty providing appropriate route guidance according to the user's emotional state. For example, flexible guidance that can respond to users who are feeling stressed or nervous is required. The present invention aims to solve this problem and provide appropriate route guidance according to the user's emotional state.

[0999] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1000] In this invention, the server includes means for acquiring current location information, means for transmitting destination information input by the user to the server, means for the server to calculate the optimal route and generate a route guidance message using a generation AI, means for the terminal to collect facial expression and voice data and include an emotion engine that analyzes the emotional state, and means for adjusting the content of the route guidance message based on the results of the emotion engine. This enables users to avoid getting lost in train stations or large facilities and to receive optimal route guidance according to their emotional state at the time.

[1001] "Current location information" is digital data that indicates the user's location within a station or large facility.

[1002] "Destination information" is digital data that indicates the final location or destination that the user is aiming for.

[1003] A "server" is a computer system that processes data and provides information via a network.

[1004] "Generative AI" is a program that uses artificial intelligence technology to generate route guidance messages in natural language.

[1005] A "terminal" is a device used by a user to read QR codes or NFC tags, such as a smartphone or a dedicated reading device.

[1006] A "QR code" is a code that encodes information into a rectangular grid and can be read in two dimensions.

[1007] An "NFC tag" is a tag that can transmit and receive data using near-field wireless communication technology.

[1008] An "emotion engine" is a software system that analyzes a user's facial expressions and voice data to identify their emotional state.

[1009] A "route guidance message" is a message provided to a user that contains instructions and explanations for reaching a destination.

[1010] This invention is a navigation system that helps users reach their destinations without getting lost in complex and vast train stations and large-scale facilities. This system provides more accurate and personalized guidance by incorporating an emotion engine that recognizes the user's emotions and adjusts route guidance messages accordingly.

[1011] This system consists of the following elements: QR codes or NFC tags installed at multiple locations within stations and facilities; terminals for reading these; a server for generating route guidance; and an emotion engine for recognizing the user's emotions. The terminals function as smartphones or dedicated reading devices, while the server is located in the cloud and processes information in real time. The emotion engine analyzes the user's facial expressions and voice data to identify their emotional state.

[1012] The flow of operation of the system is explained below.

[1013] When a user uses their device to scan QR codes or NFC tags installed at various points within the station, their current location information is acquired by the device. Next, the user enters their destination into the device and sends it along with their current location information to the server. At this time, the device uses its camera and microphone to collect the user's facial expressions and voice data, which is analyzed by an emotion engine. The server calculates the optimal route based on the received current location and destination information. A generative AI (e.g., OpenAI's GPT-4) generates an easy-to-understand route guidance message in natural language. Furthermore, the content of the message is adjusted according to the user's emotional state as recognized by the emotion engine.

[1014] For example, imagine a tourist from overseas arrives at a station in a major Japanese city for the first time and is looking for the tourist information center on the other side of the station as his or her destination.

[1015] When a user scans a QR code on a guide board inside the station with their smartphone, the device obtains the current location ID "east_exit_01." Next, they enter "Tourist Information Center" as their destination on the device screen and press the send button. The device then sends the current location and destination information to the server. At the same time, the device uses its camera and microphone to detect the user's facial expressions and voice, which the emotion engine analyzes and determines that the user is feeling somewhat stressed. Based on this information, the server calculates the optimal route by referencing map data within the station and generates a route guidance message that reads, "Exit the east exit, turn left, and go straight through the underground passage; you will see the Tourist Information Center on your right." Audio guidance is also provided at this time to reduce the user's stress. The server then sends the generated message and map to the user's smartphone, where the user confirms the message and map and safely arrives at the Tourist Information Center.

[1016] An example of a prompt to be input to the generative AI model is as follows:

[1017] "The user's current location is the east exit and their destination is the tourist information center. They're feeling a bit stressed. Use this information to generate a message that will guide them to a more relaxing route."

[1018] As described above, this system aims to improve the user experience by providing appropriate guidance according to the user's emotional state, making it particularly useful in large, complex train stations and other large-scale facilities.

[1019] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1020] Step 1: User scans a QR code or NFC tag

[1021] The user scans a QR code or NFC tag installed at a designated point within a station or facility using a device such as a smartphone.

[1022] Input: QR code or NFC tag

[1023] Data processing: The device reads the QR code or NFC tag information and obtains the current location information.

[1024] Output: Current location information (e.g. "east_exit_01")

[1025] Step 2: The device sends the current location and destination information to the server.

[1026] The user inputs destination information into the terminal and transmits it to the server together with current location information.

[1027] Input: Current location information, destination information (e.g. "tourist information center")

[1028] Data processing: Packages the current location information and destination information and sends it to the server via the network.

[1029] Output: Current location and destination information received by the server

[1030] Step 3: The device collects facial and voice data

[1031] The user's device uses a camera and microphone to collect facial expressions and voice data.

[1032] Input: User's facial expression data, voice data

[1033] Data processing: Collect data in real time using cameras and microphones and send that data to the emotion engine.

[1034] Output: Collected facial expression data and voice data

[1035] Step 4: The emotion engine analyzes the user's emotions

[1036] The emotion engine analyzes facial expression and voice data to identify the user's emotional state.

[1037] Input: facial expression data, voice data

[1038] Data processing: Using data analysis algorithms to identify emotional states such as stress or relaxation

[1039] Output: Identified emotional state (e.g., "stressed")

[1040] Step 5: The server calculates the optimal route

[1041] The server calculates the optimal route based on the current location information and destination information, and refers to map data within the station or facility.

[1042] Input: Current location information, destination information, map data

[1043] Data processing: Using an algorithm to calculate the optimal route from your current location to your destination

[1044] Output: Calculated optimal route data

[1045] Step 6: The server generates a route guidance message using the generation AI.

[1046] The server uses a generative AI (e.g., GPT-4) to generate a natural language route guidance message based on the optimal route, adjusting the message content based on the results of the emotion engine.

[1047] Input: optimal route data, emotional state

[1048] Data processing: Generative AI uses natural language processing algorithms to generate route guidance messages

[1049] Output: Generated route guidance message (e.g. "Turn left after exiting the east exit, go straight through the underground passage and you will see the tourist information center on your right")

[1050] Step 7: The server generates a route guidance message and sends it to the device.

[1051] The server sends route guidance messages and accompanying maps and directions to the user's terminal.

[1052] Input: Route guidance message, map data

[1053] Data processing: Packages message and map data and sends them to the terminal via the network

[1054] Output: Route guidance message and map sent to device

[1055] Step 8: The device displays a route guidance message.

[1056] The user's device displays the received route guidance message and plays back the audio guide, allowing the user to reach their destination.

[1057] Input: Received route guidance message, map data

[1058] Data processing: The device displays the message on the screen and plays the audio guide.

[1059] Output: The displayed and played guidance information allows the user to navigate to their destination.

[1060] In this way, users can receive accurate and emotionally sensitive route guidance in their local environment.

[1061] (Application example 2)

[1062] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1063] Navigation systems are needed to help workers navigate large, complex facilities such as factories without getting lost. In particular, there is a need to reduce stress and confusion by providing guidance messages that take the user's emotional state into account. However, conventional navigation systems are unable to generate route guidance messages that take the user's emotional state into account, making it difficult to reduce user stress.

[1064] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1065] In this invention, the server includes means for calculating an optimal route based on current location information and destination information, means for generating a route guidance message using a generation AI, means for analyzing the emotional state of the user using a camera and microphone and transmitting the results to the server, and means for the server to adjust the route guidance message taking the emotional state into consideration, thereby enabling the user to reach the destination easily and reducing stress and confusion.

[1066] A "QR code" is a type of two-dimensional barcode that visually represents information and can be read by a device with a camera.

[1067] An "NFC tag" is an information recording medium that uses near-field wireless communication technology, and is a tag that can read and write data when a compatible device comes close.

[1068] "Location information" is information that indicates that a user is in a specific location, and is data obtained through a QR code or NFC tag.

[1069] "Destination information" is information about the location the user is heading for, and is data that is specified by the user entering it into the terminal.

[1070] A "server" is a computer system that stores and processes data on a network, and is a device that performs tasks such as route calculations and generating guidance messages.

[1071] A "route guidance message" is a message containing instructions for users to reach their destination from their current location, and is created by a generation AI.

[1072] "Device" means an input / display device held by a user, such as a smartphone, tablet, smart glasses, or head-mounted display.

[1073] A "camera" is a device for acquiring visual information, and is built into a terminal and used to capture the user's facial expression.

[1074] A "microphone" is a device for acquiring voice information, and is built into a terminal and used to record the user's voice.

[1075] "Emotional state" is information that indicates the user's psychological and emotional state, and is analyzed based on data obtained through cameras and microphones.

[1076] "Generative AI" is a type of artificial intelligence that refers to programs or models that perform natural language processing based on large amounts of data and generate route guidance messages.

[1077] "Adjustment" refers to the act of changing the content and presentation method of a route guidance message depending on the user's emotional state.

[1078] This invention relates to a navigation system that enables workers to reach their destinations without getting lost in a complex and vast factory. In particular, it relates to a system that combines an emotion engine that recognizes the user's emotions and adjusts route guidance messages.

[1079] System configuration

[1080] The system consists of QR codes or NFC tags installed at multiple locations within the factory, terminals for reading them, a server that generates route guidance, and an emotion engine that recognizes the user's emotions. The terminals are smartphones, smart glasses, head-mounted displays, etc. The server is located on the cloud and processes information in real time. The emotion engine analyzes the user's facial expressions and voice data to identify their emotional state.

[1081] System Operation

[1082] Read QR codes and NFC tags and determine current location

[1083] The user scans a QR code or NFC tag installed at a designated location in the factory with the device, which then acquires the current location information.

[1084] Setting the destination and sending to the server

[1085] The device operated by the user transmits the acquired current location information and the destination information entered by the user to the server, which then receives data for calculating the optimal route based on this information.

[1086] Recognition of emotional states

[1087] The device uses a built-in camera and microphone to collect facial and voice data from the user, and the emotion engine analyzes this data to determine the user's emotional state, such as whether they are stressed or relaxed.

[1088] Generate route guidance messages

[1089] The server calculates the optimal route based on the received current location and destination information, as well as the user's emotional state. The server's AI uses natural language processing to generate a route guidance message that is easy for the user to understand. The content of the message is adjusted according to the user's emotional state.

[1090] Sending and displaying information messages

[1091] The server generates route guidance messages and sends them to the user's device along with accompanying illustrated maps and guide maps. The user can then check the messages on their device and follow the instructions to reach their destination.

[1092] Hardware and software used

[1093] QR codes and NFC tags: Used to identify your location.

[1094] Devices: Smartphones, smart glasses, head-mounted displays (e.g., Samsung Galaxy S21, Google Glass, Microsoft HoloLens 2).

[1095] Server: Cloud server (e.g., Amazon Web Services (AWS)).

[1096] Emotion engine: Analyzes data captured by the camera and microphone (e.g., Amazon Rekognition, Amazon Comprehend).

[1097] Generative AI: Natural language processing models (e.g., GPT-4 (OpenAI)).

[1098] Specific examples

[1099] Below are some specific examples of how this system can be used.

[1100] Worker A, heading to Parts Warehouse B in the factory, scans a QR code with his smartphone to identify his current location. Worker A's device retrieves "Section A" and then enters "Parts Warehouse B" as his destination. At the same time, the camera and microphone record Worker A's facial expressions and voice. The emotion engine determines that Worker A is feeling stressed.

[1101] The server receives information about "Section A" and "Parts Warehouse B" and calculates the optimal route. The generation AI takes into account information from the emotion engine and generates a route guidance message suggesting a relaxing route. The message generated reads, "Exit production line section A, turn right and go straight. Turn left at the third intersection and take the elevator down to the first floor. Go straight and you will see Parts Warehouse B on your right."

[1102] The server sends the generated message and map to Person A's smartphone. Person A checks the received message and follows the instructions to safely reach Parts Warehouse B.

[1103] The following text is used as an example of a prompt sentence:

[1104] plain text

[1105] Your current location is assembly line section A and your destination is parts warehouse B. Your emotional state is stressed.

[1106] This not only allows users to reach their destinations more easily, but also reduces stress and confusion.

[1107] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1108] Step 1:

[1109] Obtaining current location information

[1110] The user scans a QR code or NFC tag installed in a location within the factory with a device (smartphone, smart glasses, head-mounted display). This allows the device to acquire data indicating the current location as input. The acquired current location information is recognized as an identification ID and used for the next process.

[1111] Input: QR code or NFC tag

[1112] Output: Current location ID

[1113] Step 2:

[1114] Setting destination information

[1115] The user inputs the next destination (e.g., a parts warehouse or other work section) into the terminal. The terminal receives this destination information as input. The destination information is used in the next step of route calculation.

[1116] Input: Destination information (user input)

[1117] Output: Destination ID

[1118] Step 3:

[1119] Recognition of emotional states

[1120] The device's camera and microphone are used to collect the user's facial expressions and voice data. The emotion engine analyzes this data to identify the user's emotional state (e.g., whether they are stressed or relaxed). The analyzed emotion data is used in the next step.

[1121] Input: User's facial expression data and voice data (input from camera and microphone)

[1122] Output: Emotional state (stressed, relaxed, etc.)

[1123] Step 4:

[1124] Generate route guidance messages

[1125] The server receives current location information, destination information, and emotional state as input and calculates the optimal route. Based on the calculation results, the generation AI generates easy-to-understand route guidance messages in natural language. The route and message content are adjusted depending on the emotional state.

[1126] Input: Current location, destination, emotional state

[1127] Output: Route guidance message

[1128] Step 5:

[1129] Sending an information message

[1130] The server generates a route guidance message and sends it to the user's device along with an accompanying illustrated map or guide map. The message is then displayed in the final step.

[1131] Input: Route guidance message (generated by AI)

[1132] Output: Sent message and map

[1133] Step 6:

[1134] Displaying route guidance messages

[1135] The user's device displays the route guidance message received from the server, allowing the user to receive visual and audio instructions to reach the destination.

[1136] Input: Sent message and map (received from server)

[1137] Output: Displayed route guidance message

[1138] Specific operation example

[1139] In the case of Worker A heading to Parts Warehouse B in the factory:

[1140] Step 1: Person A scans the QR code with their smartphone and obtains their current location as "Section A."

[1141] Step 2: Person A enters "Parts Warehouse B" as the destination into the terminal.

[1142] Step 3: Using a camera and microphone, facial and voice data from Person A is collected, and the emotion engine determines that Person A is feeling stressed.

[1143] Step 4: The server receives information about "Section A" and "Parts Warehouse B" and calculates the optimal route. The generation AI generates a route guidance message to reduce stress.

[1144] Step 5: The server sends the generated message and map to Mr. A's smartphone.

[1145] Step 6: Person A checks the message and map on his smartphone and follows it to reach "Parts Warehouse B."

[1146] The following is used as an example prompt sentence:

[1147] plain text

[1148] Your current location is assembly line section A and your destination is parts warehouse B. Your emotional state is stressed.

[1149] 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.

[1150] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[1151] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1152] [Fourth embodiment]

[1153] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1156] 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.

[1157] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1158] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1159] 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.

[1160] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1161] 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.

[1162] 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 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.

[1163] 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.

[1164] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1165] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1166] The present invention relates to a navigation system that enables users to reach their destinations without getting lost in a complex and vast station premises. Specific embodiments of this system will be described below.

[1167] System configuration

[1168] The system consists of a station where QR codes or NFC tags are installed, a terminal for reading them, and a server that generates route guidance. The terminal is a smartphone or a dedicated reading device. The server is located in the cloud and processes information in real time.

[1169] Operational Overview

[1170] 1. The user scans a QR code or NFC tag

[1171] The user scans QR codes or NFC tags installed at various points within the station with their device, which then acquires their current location information.

[1172] 2. The device sends the current location and destination information to the server.

[1173] The device sends the acquired current location information and the destination information entered by the user to the server, which then receives all the necessary information.

[1174] 3. The server calculates the optimal route and generates a route guidance message.

[1175] The server calculates the optimal route based on the received current location and destination information, referencing the station's map data. Based on the calculation results, the generation AI generates a route guidance message in natural language that is easy for the user to understand. For example, a message might say, "Go to Exit 5, take the escalator and go through the underground passage, and you will arrive at the north exit."

[1176] 4. The server generates a route guidance message and sends it to the terminal.

[1177] The generated message and accompanying illustrated maps and guide maps are sent from the server to the user's terminal.

[1178] 5. The device displays a route guidance message.

[1179] The user's device will display the received route guidance message, allowing the user to easily understand what to do next.

[1180] Specific examples

[1181] For example, suppose tourist B from overseas arrives at a station in a large city in Japan for the first time and is looking for the tourist information center on the other side of the station as his destination.

[1182] First, Person B uses his smartphone to scan the QR code on a guide board inside the station. Person B's device acquires the current location ID "east_exit_01." Next, Person B enters "Tourist Information Center" as his destination on the device screen and presses the send button.

[1183] The device sends its current location and destination information to the server. The server receives the information "east_exit_01" and "tourist information center" and calculates the optimal route based on map data within the station. The AI ​​then generates a route guidance message and illustrated map, saying, "Exit the east exit, turn left, and go straight through the underground passage. You will see the tourist information center on your right."

[1184] The server sends the generated message and map to Person B's smartphone. Person B checks the message on the screen and follows the instructions to safely arrive at the tourist information center.

[1185] This system helps users reach their destinations easily and accurately, making it particularly useful in large, complex station premises.

[1186] The processing flow will be explained below.

[1187] Step 1:

[1188] The user scans a QR code or NFC tag in the station.

[1189] The user holds their smartphone close to a QR code or NFC tag installed in the station and reads it. At this time, the device acquires the information from the QR code or NFC tag.

[1190] Step 2:

[1191] The terminal acquires current location information and prompts the user to input destination information.

[1192] The device analyzes and obtains the current location ID information from the QR code or NFC tag, and at the same time displays a destination input form on the device screen, prompting the user to enter the destination information.

[1193] Step 3:

[1194] The device sends current location information and destination information to the server.

[1195] After the user inputs the destination information, the device sends the current location information and the input destination information to the server as a single request data. This data includes, for example, "currentLocationId: 'east_exit_01', destination: 'Tourist Information Center'".

[1196] Step 4:

[1197] The server receives the request data and identifies the current location and destination.

[1198] The server receives the data sent from the terminal and identifies the current location from the station map database based on the current location ID. It also identifies the destination location in the map database based on the destination information.

[1199] Step 5:

[1200] The server calculates the optimal route.

[1201] The server uses the current and destination locations to run a route planning algorithm within the station to calculate the optimal route, taking into account obstacles, restricted areas, and current pedestrian flow data.

[1202] Step 6:

[1203] The server generates a route guidance message using the generation AI.

[1204] The server inputs the calculated route information into the generation AI, which then generates a route guidance message in natural language that is easy for users to understand. For example, a message such as, "Exit the east exit, turn left, and go straight through the underground passage; you will see the tourist information center on your right."

[1205] Step 7:

[1206] The server sends the generated guidance message and illustrated map to the terminal.

[1207] The server sends the route guidance message created by the AI ​​and related illustrated map data to the device.

[1208] Step 8:

[1209] The terminal displays an information message and an illustrated map.

[1210] The device displays the received route guidance message and illustrated map on the screen, and the user can check them and follow the instructions to head to their destination.

[1211] Step 9:

[1212] The user follows the guidance to reach the destination.

[1213] The user follows the route in real time while looking at the guidance messages and illustrated map on the device, and safely reaches the destination.

[1214] Example 1

[1215] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1216] There is a need to provide navigation that will enable users to quickly and accurately reach their destinations within vast and complex station premises without getting lost. Conventional guidance systems make it difficult for users to grasp their exact current location or the appropriate route. Furthermore, because they do not provide guidance based on information updated in real time, they are unable to respond when routes change due to construction, events, etc. Therefore, a new system is needed to solve these issues.

[1217] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1218] In this invention, the server includes means for acquiring the user's current location using codes or tags installed at multiple locations within the station, means for transmitting the current location information and destination information entered by the user to the processing device, means for the processing device to calculate an optimal route based on the current location information and the destination information, means for generating a route guidance message using the generation system, means for transmitting the generated route guidance message to the user's display device, and means for the display device to display the route guidance message. This allows users to easily and quickly check their current location and destination and receive optimal route guidance in real time.

[1219] "Code" refers to QR codes or other similar technology that are placed at various locations within the station for reading by passenger devices.

[1220] "Tag" refers to an NFC tag or other similar technology that is placed at various locations within a station to be read by a passenger's device.

[1221] "Current location information" refers to information indicating the user's current location, which is obtained by the user reading codes or tags installed at various locations within the station.

[1222] "Destination information" refers to information about a desired location that is specified by a user by inputting it into a terminal.

[1223] The "processing device" refers to a computer system or server that receives current location information and destination information and calculates the optimal route based on that information.

[1224] "Generation system" refers to artificial intelligence (AI) that generates easy-to-understand route guidance messages in natural language based on optimal route information calculated by a processing device.

[1225] "Route Guidance Message" means a message containing instructions regarding the optimal route from a current location to a destination, provided in a format that is easy for a user to understand.

[1226] "Display device" refers to a device, primarily a smartphone or dedicated terminal, that receives the route guidance message generated by the generation system and displays it to the user.

[1227] The present invention relates to a navigation system that enables users to reach their destinations without getting lost in a complex and vast station premises. Specific embodiments of this system will be described below.

[1228] System configuration

[1229] This system consists of codes or tags installed in stations, terminals for reading them, and a processing device (server) that generates route guidance. The terminals are smartphones or dedicated reading devices, and the processing device is often located on the cloud. Cloud computing platforms, for example, can be used as cloud services.

[1230] Program processing

[1231] 1. The user reads the code or tag

[1232] Users use their smartphones or other dedicated devices to scan codes or tags placed at various locations within the station, which allows the device to acquire information about the user's current location.

[1233] 2. The device sends the current location and destination information to the server.

[1234] The user enters destination information into their smartphone and presses the send button. The device then sends the current location and destination information to the server. This process is carried out via a dedicated application or browser on the device.

[1235] 3. The server calculates the optimal route and generates a route guidance message.

[1236] The server calculates the optimal route based on the received current location information and destination information, for example, using a map platform API. Furthermore, it generates an easy-to-understand route guidance message in natural language using a generative AI model (for example, OpenAI's GPT-4). An example of a prompt used in this case is shown below.

[1237] Current location: east_exit_01

[1238] Destination: Tourist Information Center

[1239] Please provide natural language guidance on the best way to get from your current location to your destination, using a large map of the station. For example, please include exit numbers, instructions on how to use escalators, directions to underground passages, etc. Also, please generate an easy-to-understand illustrated map.

[1240] Based on this prompt, the server generates a specific route guidance message such as, "Exit the east exit, turn left, go straight through the underground passage and you will see the tourist information center on your right."

[1241] 4. The server generates a route guidance message and sends it to the terminal.

[1242] The generated route guidance message and illustrated map are sent from the server to the user's device using cloud communication technology, such as a cloud messaging service.

[1243] 5. The device displays a route guidance message.

[1244] The user's device displays the received route guidance message, allowing the user to understand what to do next and reach their destination.

[1245] Usage example

[1246] For example, consider the case where a tourist is looking for a tourist information center at a train station in a large city they are visiting for the first time. The user scans a QR code installed near the east exit of the station with their smartphone and obtains the current location ID "east_exit_01." Next, they enter the destination information "tourist information center" and submit it. The server calculates the optimal route based on this information and uses a generative AI model to generate a message that says, "Exit the east exit, turn left, and go straight through the underground passage; you will see the tourist information center on your right," which is sent to the user's device. The user follows this message and arrives safely at their destination.

[1247] This system allows users to reach their destinations quickly and without getting lost even in vast and complex station premises.

[1248] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1249] Step 1:

[1250] The user scans a code or tag inside the station.

[1251] Users use their smartphones or dedicated devices to scan codes and tags placed throughout the station to obtain location information. Specifically, a QR code reader app or NFC-enabled device is used. The input is the identification information embedded in the code or tag, and the output is a location ID such as "east_exit_01" sent to the device.

[1252] Step 2:

[1253] The device sends current location information and destination information to the server.

[1254] The user enters destination information into the terminal and presses the send button. The terminal then sends the acquired current location information and the destination information entered by the user to the server via the Internet. The inputs include the current location ID and destination information (e.g., "tourist information center"), and the output is this information sent to the server.

[1255] Step 3:

[1256] The server calculates the optimal route and generates a route guidance message.

[1257] The server calculates the optimal route by referencing map data based on the received current location information and destination information. For example, a map data API is used to calculate the route, and a prompt sentence is entered into a generative AI model (e.g., GPT-4) to generate a route guidance message. The input includes the current location ID and destination information, and data processing involves route calculation and natural language generation by the generative AI model. The output is a route guidance message such as, "Exit the east exit, turn left, and go straight through the underground passage; you will see the tourist information center on your right."

[1258] Step 4:

[1259] The server sends the generated route guidance message to the terminal.

[1260] The server sends the generated route guidance message and the illustrated map to the user's terminal via the Internet. The input is the route guidance message and the accompanying illustrated map, and the output is sent to the user's terminal.

[1261] Step 5:

[1262] The device displays a route guidance message.

[1263] The terminal displays the received route guidance message and illustrated map. By referring to this, the user can intuitively understand how to get to their destination. The input is the route guidance message and illustrated map sent from the server, and the output is this information displayed on the terminal screen. In concrete terms, the message and map are displayed on the terminal screen, and the user checks them before taking action.

[1264] (Application example 1)

[1265] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1266] In large, complex commercial facilities and large stores, users often get lost before reaching their destination. This complexity significantly reduces user convenience, especially for first-time visitors or those looking for a specific product or section. Furthermore, the lack of real-time guidance makes it difficult to navigate in a facility based on the current situation. For this reason, there is a need for an effective navigation system that can help users reach their destination smoothly.

[1267] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1268] In this invention, the server includes means for acquiring a user's current location using QR codes or NFC tags installed at multiple locations within the commercial facility, means for transmitting the current location information and destination information entered by the user to the server, means for the server to calculate an optimal route based on the current location information and destination information, means for the server to generate a route guidance message using a generation AI, means for the server to transmit the route guidance message to the user's terminal, and means for the terminal to display the route guidance message, thereby enabling users to quickly and accurately reach their destination within the commercial facility without getting lost.

[1269] "Commercial facility" refers to a large building or area that houses multiple stores and related facilities offering goods or services.

[1270] "QR Code" is a type of two-dimensional barcode that is a rectangular code that can efficiently store information and be quickly read.

[1271] An "NFC tag" is an electronic tag that uses Near Field Communication technology to read and write information without contact.

[1272] "User" means a customer who visits a commercial establishment and uses goods or services.

[1273] "Current location information" refers to information about the user's current location, obtained by reading a QR code or NFC tag.

[1274] "Destination information" refers to information about a specific location or store that a user wants to reach within a commercial facility.

[1275] A "server" is a computer system that communicates with multiple terminals via a network and stores and processes data.

[1276] The "optimal route" refers to the most efficient and shortest route between the current location and the destination.

[1277] "Generative AI" refers to a system that uses artificial intelligence technology to generate messages and information based on natural language.

[1278] A "route guidance message" is a message that provides the user with easy-to-understand guidance on how to reach a destination.

[1279] "Terminal" refers to a device used by a user, such as a mobile information device or smartphone.

[1280] The present invention relates to a navigation system for assisting users in smoothly reaching their destinations in commercial facilities. Specific embodiments of this system will be described below.

[1281] System Configuration

[1282] This system consists of QR codes or NFC tags installed within the commercial facility, a user's device to read them, and a server to process the data.User devices are typically smartphones, and QR codes or NFC tags are installed at multiple points within the facility, such as at the entrance and near the escalators on each floor.

[1283] Operational Overview

[1284] 1. Reading a QR code or NFC tag

[1285] Users can obtain location information by scanning QR codes or NFC tags in commercial facilities with their smartphones. QR codes are read using the smartphone's camera and the ZXing library, and NFC tags are read using the smartphone's NFC reader and the Android NFC API.

[1286] 2. Sending current location and destination information

[1287] The user enters the name of the desired store or department in the application and presses the send button. The device then sends the acquired current location information and the entered destination information to the server. The server receives the current location information and destination information via API communication and processes the data.

[1288] 3. Calculating the optimal route and generating guidance messages

[1289] The server refers to the commercial facility's map data based on the received current location information and destination information and calculates the optimal route. This route calculation uses the A algorithm. After calculating the optimal route, it uses the generative AI model GPT-4 to generate an easy-to-understand route guidance message in natural language. The following is used as an example of a prompt sentence.

[1290] Prompt Sentence Examples

[1291] "Generate a guidance message for when a user scans a QR code at the entrance of a commercial facility and enters the women's clothing department as the destination. Starting point: Entrance Arrival point: Women's clothing department Optimal route based on map information: Turn right after entering the entrance, take the escalator to the second floor, and go straight. You will see the women's clothing department on your left."

[1292] 4. Sending a message to the device

[1293] The generated route guidance message, illustrated map, and guide map are sent from the server to the user's device. The message data is sent via API communication and displayed on the user's smartphone.

[1294] 5. Display of route guidance messages

[1295] The user's device displays the received route guidance message. Based on the message and map displayed on the device, the user can navigate to their destination. This display is done using React Native, a mobile application framework.

[1296] Specific examples

[1297] For example, consider the case where Person A is looking for the "women's clothing section" at a large shopping mall that she is visiting for the first time. Person A scans the QR code at the entrance, enters "women's clothing section" into the application, and submits it. Based on this information, the server generates a guidance message and map that says, "Enter the entrance, turn right, take the escalator to the second floor, and go straight; you will see the women's clothing section on your left," and sends this to Person A's smartphone. Person A can follow the guidance and arrive at the women's clothing section without getting lost.

[1298] This system allows users to reach their destinations efficiently and accurately even within large, complex commercial facilities.

[1299] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1300] Step 1:

[1301] A user scans a QR code or NFC tag in a commercial facility with their smartphone. This allows the current location information (for example, location information near the entrance) to be obtained using the camera and ZXing library in the case of a QR code, or the NFC reader and Android NFC API in the case of an NFC tag. Specifically, by reading the QR code, a text-formatted current location ID is entered into the smartphone.

[1302] Step 2:

[1303] The device obtains destination information based on the user's input. The user enters the name of the desired store or department (for example, "Women's clothing department") on the application screen and presses the send button. This brings the current location information and destination information together on the device.

[1304] Step 3:

[1305] The device sends the acquired current location information and destination information to the server. The server then receives the necessary data. Specifically, the current location ID and destination name are sent to the server via API communication. The data sent is in a standardized format such as JSON.

[1306] Step 4:

[1307] The server calculates the optimal route based on the current location and destination information. The server references the map data of the commercial facility and uses algorithms such as A to calculate the shortest and most efficient route. The current location and destination information are input, and a list of routes is generated as output.

[1308] Step 5:

[1309] The server uses a generative AI model (GPT-4) to generate a route guidance message. The server inputs the calculated route information as a prompt sentence into the generative AI, which generates an easy-to-understand route guidance message in natural language. The output is a route guidance message (e.g., "Enter the entrance, turn right, take the escalator to the second floor, and go straight; you will see the women's clothing department on your left").

[1310] Step 6:

[1311] The server generates route guidance messages and sends illustrated maps to the device, and text and image data are sent to the smartphone via API communication.

[1312] Step 7:

[1313] The device displays the received route guidance message and an illustrated map. The user can check the message and follow the visual map. Specifically, the screen displaying the message and map is rendered on the device using React Native, a mobile application framework.

[1314] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1315] The present invention relates to a navigation system that enables users to reach their destination without getting lost in a complex and vast train station. In particular, the present invention relates to a system that combines an emotion engine that recognizes the user's emotions and adjusts route guidance messages.

[1316] System configuration

[1317] The system consists of QR codes or NFC tags installed at multiple locations within the station, terminals for reading them, a server that generates route guidance, and an emotion engine that recognizes the user's emotions. The terminals are smartphones or dedicated reading devices. The server is located in the cloud and processes information in real time. The emotion engine analyzes the user's facial expressions and voice data to identify their emotional state.

[1318] Operational Overview

[1319] 1. The user scans a QR code or NFC tag

[1320] The user scans QR codes or NFC tags installed at various points within the station with their device, which then acquires their current location information.

[1321] 2. The device sends the current location and destination information to the server.

[1322] The device sends the current location information and the destination information entered by the user to the server, which then receives all the necessary information.

[1323] 3. The emotion engine recognizes the user's emotions

[1324] The device uses the user's camera and microphone to collect facial and voice data, which the emotion engine analyzes to identify the user's emotional state, for example, whether the user is stressed or relaxed.

[1325] 4. The server calculates the optimal route and generates a route guidance message.

[1326] Based on the current location and destination information received by the server, the system refers to map data within the station and calculates the optimal route. Based on the calculation results, the generation AI generates a route guidance message in natural language that is easy for the user to understand. The emotion engine recognizes the user's emotional state and adjusts the message content accordingly. For example, if the user is feeling stressed, the system suggests an alternative route that will help them relax.

[1327] 5. The server generates a route guidance message and sends it to the terminal.

[1328] The generated message and accompanying illustrated maps and guide maps are sent from the server to the user's terminal.

[1329] 6. The device displays a route guidance message.

[1330] The user's device will display the received route guidance message, allowing the user to easily understand what to do next.

[1331] Specific examples

[1332] For example, suppose tourist C from overseas arrives at a station in a large city in Japan for the first time and is looking for the tourist information center on the other side of the station as his destination.

[1333] First, Mr. C uses his smartphone to scan the QR code on a guide board inside the station. Mr. C's device acquires the current location ID "east_exit_01." Next, Mr. C enters "Tourist Information Center" as his destination on the device screen and presses the send button.

[1334] The device sends current location and destination information to the server. At the same time, the device uses its camera and microphone to detect C's facial expressions and voice, and the emotion engine analyzes this to determine that C is feeling somewhat stressed.

[1335] The server receives the information "east_exit_01" and "tourist information center" and calculates the optimal route based on map data within the station. Based on the calculation results, the generation AI takes into account the information from the emotion engine and generates a route guidance message saying, "Exit the east exit, turn left, go straight through the underground passage, and you will see the tourist information center on your right." At this time, audio guidance is also provided to reduce Mr. C's stress.

[1336] The server sends the generated message and map to Mr. C's smartphone. Mr. C checks the message and audio guidance he receives and follows them to safely arrive at the tourist information center.

[1337] This system aims to further improve the user experience by not only helping users reach their destinations easily and accurately, but also by providing appropriate guidance that takes into account the user's emotional state, making it particularly useful in large, complex station premises.

[1338] The processing flow will be explained below.

[1339] Step 1:

[1340] The user scans the QR code or NFC tag.

[1341] The user holds their smartphone close to a QR code or NFC tag installed in the station and reads it. At this time, the device acquires the information from the QR code or NFC tag.

[1342] Step 2:

[1343] The terminal acquires current location information and prompts the user to input destination information.

[1344] The device analyzes and obtains the current location ID information from the QR code or NFC tag, and at the same time displays a destination input form on the device screen, prompting the user to enter the destination information.

[1345] Step 3:

[1346] The terminal acquires the user's emotion data.

[1347] The device uses the smartphone's camera and microphone to collect the user's facial and voice data, which is later sent to the emotion engine.

[1348] Step 4:

[1349] The device transmits current location information, destination information, and emotion data to the server.

[1350] After the user inputs the destination information, the device sends the current location information, the input destination information, and the collected emotion data to the server as a single request data. This data includes, for example, "currentLocationId: 'east_exit_01', destination: 'Tourist Information Center', emotionData: [facial expression data, voice data]".

[1351] Step 5:

[1352] The server receives the request data and identifies the current location and destination.

[1353] The server receives the data sent from the terminal and identifies the current location from the station map database based on the current location ID. It also identifies the destination location in the map database based on the destination information.

[1354] Step 6:

[1355] The server uses an emotion engine to analyze the user's emotional state.

[1356] The server analyzes the emotional data to identify the user's emotional state, for example, by analyzing facial expressions and voice to recognize whether the user is stressed or relaxed.

[1357] Step 7:

[1358] The server calculates the optimal route.

[1359] The server uses the current and destination locations to run a route planning algorithm within the station to calculate the optimal route, taking into account obstacles, restricted areas, and current pedestrian flow data.

[1360] Step 8:

[1361] The server generates a route guidance message using the generation AI.

[1362] The server inputs the calculated route information into the generation AI, which then generates a route guidance message in natural language that is easy for the user to understand. The emotion engine recognizes the user's emotional state and adjusts the message content accordingly. For example, if the user is feeling stressed, it suggests gentler language or an alternative route.

[1363] Step 9:

[1364] The server sends the generated guidance message and illustrated map to the terminal.

[1365] The server sends route guidance messages and related illustrated map data created by the AI ​​to the device, including emotion-based advice and words of encouragement.

[1366] Step 10:

[1367] The terminal displays an information message and an illustrated map.

[1368] The device displays the route guidance message and illustrated map it receives on the screen. Users can check them and understand what to do next. For example, specific directions such as "Turn left after exiting the east exit, go straight through the underground passage, and you will see the tourist information center on your right" are displayed. If the user is feeling stressed, a message such as "It's okay, just take it easy" is added.

[1369] Step 11:

[1370] The user follows the guidance to reach the destination.

[1371] The user follows the route in real time while viewing the guidance messages, illustrated maps, and audio guidance on the device, and safely arrives at the destination. The emotion engine monitors the user's emotional state appropriately and readjusts the route guidance messages if necessary.

[1372] This step not only allows users to reach their destination easily and accurately, but also allows them to receive appropriate guidance that takes their emotional state into account.

[1373] Example 2

[1374] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1375] Accurate and easy-to-understand route guidance is required for users to reach their destinations without getting lost in train stations and large facilities. However, conventional navigation systems have had difficulty providing appropriate route guidance according to the user's emotional state. For example, flexible guidance that can respond to users who are feeling stressed or nervous is required. The present invention aims to solve this problem and provide appropriate route guidance according to the user's emotional state.

[1376] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1377] In this invention, the server includes means for acquiring current location information, means for transmitting destination information input by the user to the server, means for the server to calculate the optimal route and generate a route guidance message using a generation AI, means for the terminal to collect facial expression and voice data and include an emotion engine that analyzes the emotional state, and means for adjusting the content of the route guidance message based on the results of the emotion engine. This enables users to avoid getting lost in train stations or large facilities and to receive optimal route guidance according to their emotional state at the time.

[1378] "Current location information" is digital data that indicates the user's location within a station or large facility.

[1379] "Destination information" is digital data that indicates the final location or destination that the user is aiming for.

[1380] A "server" is a computer system that processes data and provides information via a network.

[1381] "Generative AI" is a program that uses artificial intelligence technology to generate route guidance messages in natural language.

[1382] A "terminal" is a device used by a user to read QR codes or NFC tags, such as a smartphone or a dedicated reading device.

[1383] A "QR code" is a code that encodes information into a rectangular grid and can be read in two dimensions.

[1384] An "NFC tag" is a tag that can transmit and receive data using near-field wireless communication technology.

[1385] An "emotion engine" is a software system that analyzes a user's facial expressions and voice data to identify their emotional state.

[1386] A "route guidance message" is a message provided to a user that contains instructions and explanations for reaching a destination.

[1387] This invention is a navigation system that helps users reach their destinations without getting lost in complex and vast train stations and large-scale facilities. This system provides more accurate and personalized guidance by incorporating an emotion engine that recognizes the user's emotions and adjusts route guidance messages accordingly.

[1388] This system consists of the following elements: QR codes or NFC tags installed at multiple locations within stations and facilities; terminals for reading these; a server for generating route guidance; and an emotion engine for recognizing the user's emotions. The terminals function as smartphones or dedicated reading devices, while the server is located in the cloud and processes information in real time. The emotion engine analyzes the user's facial expressions and voice data to identify their emotional state.

[1389] The flow of operation of the system is explained below.

[1390] When a user uses their device to scan QR codes or NFC tags installed at various points within the station, their current location information is acquired by the device. Next, the user enters their destination into the device and sends it along with their current location information to the server. At this time, the device uses its camera and microphone to collect the user's facial expressions and voice data, which is analyzed by an emotion engine. The server calculates the optimal route based on the received current location and destination information. A generative AI (e.g., OpenAI's GPT-4) generates an easy-to-understand route guidance message in natural language. Furthermore, the content of the message is adjusted according to the user's emotional state as recognized by the emotion engine.

[1391] For example, imagine a tourist from overseas arrives at a station in a major Japanese city for the first time and is looking for the tourist information center on the other side of the station as his or her destination.

[1392] When a user scans a QR code on a guide board inside the station with their smartphone, the device obtains the current location ID "east_exit_01." Next, they enter "Tourist Information Center" as their destination on the device screen and press the send button. The device then sends the current location and destination information to the server. At the same time, the device uses its camera and microphone to detect the user's facial expressions and voice, which the emotion engine analyzes and determines that the user is feeling somewhat stressed. Based on this information, the server calculates the optimal route by referencing map data within the station and generates a route guidance message that reads, "Exit the east exit, turn left, and go straight through the underground passage; you will see the Tourist Information Center on your right." Audio guidance is also provided at this time to reduce the user's stress. The server then sends the generated message and map to the user's smartphone, where the user confirms the message and map and safely arrives at the Tourist Information Center.

[1393] An example of a prompt to be input to the generative AI model is as follows:

[1394] "The user's current location is the east exit and their destination is the tourist information center. They're feeling a bit stressed. Use this information to generate a message that will guide them to a more relaxing route."

[1395] As described above, this system aims to improve the user experience by providing appropriate guidance according to the user's emotional state, making it particularly useful in large, complex train stations and other large-scale facilities.

[1396] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1397] Step 1: User scans a QR code or NFC tag

[1398] The user scans a QR code or NFC tag installed at a designated point within a station or facility using a device such as a smartphone.

[1399] Input: QR code or NFC tag

[1400] Data processing: The device reads the QR code or NFC tag information and obtains the current location information.

[1401] Output: Current location information (e.g. "east_exit_01")

[1402] Step 2: The device sends the current location and destination information to the server.

[1403] The user inputs destination information into the terminal and transmits it to the server together with current location information.

[1404] Input: Current location information, destination information (e.g. "tourist information center")

[1405] Data processing: Packages the current location information and destination information and sends it to the server via the network.

[1406] Output: Current location and destination information received by the server

[1407] Step 3: The device collects facial and voice data

[1408] The user's device uses a camera and microphone to collect facial expressions and voice data.

[1409] Input: User's facial expression data, voice data

[1410] Data processing: Collect data in real time using cameras and microphones and send that data to the emotion engine.

[1411] Output: Collected facial expression data and voice data

[1412] Step 4: The emotion engine analyzes the user's emotions

[1413] The emotion engine analyzes facial expression and voice data to identify the user's emotional state.

[1414] Input: facial expression data, voice data

[1415] Data processing: Using data analysis algorithms to identify emotional states such as stress or relaxation

[1416] Output: Identified emotional state (e.g., "stressed")

[1417] Step 5: The server calculates the optimal route

[1418] The server calculates the optimal route based on the current location information and destination information, and refers to map data within the station or facility.

[1419] Input: Current location information, destination information, map data

[1420] Data processing: Using an algorithm to calculate the optimal route from your current location to your destination

[1421] Output: Calculated optimal route data

[1422] Step 6: The server generates a route guidance message using the generation AI.

[1423] The server uses a generative AI (e.g., GPT-4) to generate a natural language route guidance message based on the optimal route, adjusting the message content based on the results of the emotion engine.

[1424] Input: optimal route data, emotional state

[1425] Data processing: Generative AI uses natural language processing algorithms to generate route guidance messages

[1426] Output: Generated route guidance message (e.g. "Turn left after exiting the east exit, go straight through the underground passage and you will see the tourist information center on your right")

[1427] Step 7: The server generates a route guidance message and sends it to the device.

[1428] The server sends route guidance messages and accompanying maps and directions to the user's terminal.

[1429] Input: Route guidance message, map data

[1430] Data processing: Packages message and map data and sends them to the terminal via the network

[1431] Output: Route guidance message and map sent to device

[1432] Step 8: The device displays a route guidance message.

[1433] The user's device displays the received route guidance message and plays back the audio guide, allowing the user to reach their destination.

[1434] Input: Received route guidance message, map data

[1435] Data processing: The device displays the message on the screen and plays the audio guide.

[1436] Output: The displayed and played guidance information allows the user to navigate to their destination.

[1437] In this way, users can receive accurate and emotionally sensitive route guidance in their local environment.

[1438] (Application example 2)

[1439] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1440] Navigation systems are needed to help workers navigate large, complex facilities such as factories without getting lost. In particular, there is a need to reduce stress and confusion by providing guidance messages that take the user's emotional state into account. However, conventional navigation systems are unable to generate route guidance messages that take the user's emotional state into account, making it difficult to reduce user stress.

[1441] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1442] In this invention, the server includes means for calculating an optimal route based on current location information and destination information, means for generating a route guidance message using a generation AI, means for analyzing the emotional state of the user using a camera and microphone and transmitting the results to the server, and means for the server to adjust the route guidance message taking the emotional state into consideration, thereby enabling the user to reach the destination easily and reducing stress and confusion.

[1443] A "QR code" is a type of two-dimensional barcode that visually represents information and can be read by a device with a camera.

[1444] An "NFC tag" is an information recording medium that uses near-field wireless communication technology, and is a tag that can read and write data when a compatible device comes close.

[1445] "Location information" is information that indicates that a user is in a specific location, and is data obtained through a QR code or NFC tag.

[1446] "Destination information" is information about the location the user is heading for, and is data that is specified by the user entering it into the terminal.

[1447] A "server" is a computer system that stores and processes data on a network, and is a device that performs tasks such as route calculations and generating guidance messages.

[1448] A "route guidance message" is a message containing instructions for users to reach their destination from their current location, and is created by a generation AI.

[1449] "Device" means an input / display device held by a user, such as a smartphone, tablet, smart glasses, or head-mounted display.

[1450] A "camera" is a device for acquiring visual information, and is built into a terminal and used to capture the user's facial expression.

[1451] A "microphone" is a device for acquiring voice information, and is built into a terminal and used to record the user's voice.

[1452] "Emotional state" is information that indicates the user's psychological and emotional state, and is analyzed based on data obtained through cameras and microphones.

[1453] "Generative AI" is a type of artificial intelligence that refers to programs or models that perform natural language processing based on large amounts of data and generate route guidance messages.

[1454] "Adjustment" refers to the act of changing the content and presentation method of a route guidance message depending on the user's emotional state.

[1455] This invention relates to a navigation system that enables workers to reach their destinations without getting lost in a complex and vast factory. In particular, it relates to a system that combines an emotion engine that recognizes the user's emotions and adjusts route guidance messages.

[1456] System configuration

[1457] The system consists of QR codes or NFC tags installed at multiple locations within the factory, terminals for reading them, a server that generates route guidance, and an emotion engine that recognizes the user's emotions. The terminals are smartphones, smart glasses, head-mounted displays, etc. The server is located on the cloud and processes information in real time. The emotion engine analyzes the user's facial expressions and voice data to identify their emotional state.

[1458] System Operation

[1459] Read QR codes and NFC tags and determine current location

[1460] The user scans a QR code or NFC tag installed at a designated location in the factory with the device, which then acquires the current location information.

[1461] Setting the destination and sending to the server

[1462] The device operated by the user transmits the acquired current location information and the destination information entered by the user to the server, which then receives data for calculating the optimal route based on this information.

[1463] Recognition of emotional states

[1464] The device uses a built-in camera and microphone to collect facial and voice data from the user, and the emotion engine analyzes this data to determine the user's emotional state, such as whether they are stressed or relaxed.

[1465] Generate route guidance messages

[1466] The server calculates the optimal route based on the received current location and destination information, as well as the user's emotional state. The server's AI uses natural language processing to generate a route guidance message that is easy for the user to understand. The content of the message is adjusted according to the user's emotional state.

[1467] Sending and displaying information messages

[1468] The server generates route guidance messages and sends them to the user's device along with accompanying illustrated maps and guide maps. The user can then check the messages on their device and follow the instructions to reach their destination.

[1469] Hardware and software used

[1470] QR codes and NFC tags: Used to identify your location.

[1471] Devices: Smartphones, smart glasses, head-mounted displays (e.g., Samsung Galaxy S21, Google Glass, Microsoft HoloLens 2).

[1472] Server: Cloud server (e.g., Amazon Web Services (AWS)).

[1473] Emotion engine: Analyzes data captured by the camera and microphone (e.g., Amazon Rekognition, Amazon Comprehend).

[1474] Generative AI: Natural language processing models (e.g., GPT-4 (OpenAI)).

[1475] Specific examples

[1476] Below are some specific examples of how this system can be used.

[1477] Worker A, heading to Parts Warehouse B in the factory, scans a QR code with his smartphone to identify his current location. Worker A's device retrieves "Section A" and then enters "Parts Warehouse B" as his destination. At the same time, the camera and microphone record Worker A's facial expressions and voice. The emotion engine determines that Worker A is feeling stressed.

[1478] The server receives information about "Section A" and "Parts Warehouse B" and calculates the optimal route. The generation AI takes into account information from the emotion engine and generates a route guidance message suggesting a relaxing route. The message generated reads, "Exit production line section A, turn right and go straight. Turn left at the third intersection and take the elevator down to the first floor. Go straight and you will see Parts Warehouse B on your right."

[1479] The server sends the generated message and map to Person A's smartphone. Person A checks the received message and follows the instructions to safely reach Parts Warehouse B.

[1480] The following text is used as an example of a prompt sentence:

[1481] plain text

[1482] Your current location is assembly line section A and your destination is parts warehouse B. Your emotional state is stressed.

[1483] This not only allows users to reach their destinations more easily, but also reduces stress and confusion.

[1484] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1485] Step 1:

[1486] Obtaining current location information

[1487] The user scans a QR code or NFC tag installed in a location within the factory with a device (smartphone, smart glasses, head-mounted display). This allows the device to acquire data indicating the current location as input. The acquired current location information is recognized as an identification ID and used for the next process.

[1488] Input: QR code or NFC tag

[1489] Output: Current location ID

[1490] Step 2:

[1491] Setting destination information

[1492] The user inputs the next destination (e.g., a parts warehouse or other work section) into the terminal. The terminal receives this destination information as input. The destination information is used in the next step of route calculation.

[1493] Input: Destination information (user input)

[1494] Output: Destination ID

[1495] Step 3:

[1496] Recognition of emotional states

[1497] The device's camera and microphone are used to collect the user's facial expressions and voice data. The emotion engine analyzes this data to identify the user's emotional state (e.g., whether they are stressed or relaxed). The analyzed emotion data is used in the next step.

[1498] Input: User's facial expression data and voice data (input from camera and microphone)

[1499] Output: Emotional state (stressed, relaxed, etc.)

[1500] Step 4:

[1501] Generate route guidance messages

[1502] The server receives current location information, destination information, and emotional state as input and calculates the optimal route. Based on the calculation results, the generation AI generates easy-to-understand route guidance messages in natural language. The route and message content are adjusted depending on the emotional state.

[1503] Input: Current location, destination, emotional state

[1504] Output: Route guidance message

[1505] Step 5:

[1506] Sending an information message

[1507] The server generates a route guidance message and sends it to the user's device along with an accompanying illustrated map or guide map. The message is then displayed in the final step.

[1508] Input: Route guidance message (generated by AI)

[1509] Output: Sent message and map

[1510] Step 6:

[1511] Displaying route guidance messages

[1512] The user's device displays the route guidance message received from the server, allowing the user to receive visual and audio instructions to reach the destination.

[1513] Input: Sent message and map (received from server)

[1514] Output: Displayed route guidance message

[1515] Specific operation example

[1516] In the case of Worker A heading to Parts Warehouse B in the factory:

[1517] Step 1: Person A scans the QR code with their smartphone and obtains their current location as "Section A."

[1518] Step 2: Person A enters "Parts Warehouse B" as the destination into the terminal.

[1519] Step 3: Using a camera and microphone, facial and voice data from Person A is collected, and the emotion engine determines that Person A is feeling stressed.

[1520] Step 4: The server receives information about "Section A" and "Parts Warehouse B" and calculates the optimal route. The generation AI generates a route guidance message to reduce stress.

[1521] Step 5: The server sends the generated message and map to Mr. A's smartphone.

[1522] Step 6: Person A checks the message and map on his smartphone and follows it to reach "Parts Warehouse B."

[1523] The following is used as an example prompt sentence:

[1524] plain text

[1525] Your current location is assembly line section A and your destination is parts warehouse B. Your emotional state is stressed.

[1526] 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.

[1527] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[1528] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1529] 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.

[1530] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1531] 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.

[1532] 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).

[1533] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1534] 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."

[1535] 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.

[1536] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1537] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1538] 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.

[1539] 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.

[1540] 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.

[1541] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1542] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1543] 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.

[1544] 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.

[1545] 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.

[1546] 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.

[1547] The following is further disclosed regarding the above embodiment.

[1548] (Claim 1)

[1549] A means for acquiring the user's current location using QR codes or NFC tags installed at multiple locations within the station;

[1550] means for transmitting the current location information and destination information input by the user to a server;

[1551] a means for the server to calculate an optimal route based on the current location information and destination information;

[1552] A means for the server to generate a route guidance message using a generation AI;

[1553] means for the server to transmit the route guidance message to a user's terminal;

[1554] means for displaying the route guidance message in the terminal;

[1555] A system including:

[1556] (Claim 2)

[1557] The system according to claim 1, characterized in that the generation AI generates route guidance messages taking into account the latest station information.

[1558] (Claim 3)

[1559] 2. The system according to claim 1, wherein the terminal provides real-time route guidance from the user's current location to the destination based on the route guidance message.

[1560] "Example 1"

[1561] (Claim 1)

[1562] a means for acquiring a user's current location using codes or tags installed at multiple locations within the station;

[1563] means for transmitting the current location information and destination information input by the user to a processing device;

[1564] a means for the processing device to calculate an optimal route based on the current location information and destination information;

[1565] means for generating a route guidance message using a generation system by the processing device;

[1566] means for said processing device to transmit said route guidance message to a user's display device;

[1567] means for displaying the route guidance message on the display device;

[1568] A system including:

[1569] (Claim 2)

[1570] The system according to claim 1, characterized in that the generation system generates route guidance messages taking into account the latest information on the premises.

[1571] (Claim 3)

[1572] 2. The system according to claim 1, wherein the display device provides real-time route guidance from the user's current location to the destination based on the route guidance message.

[1573] "Application Example 1"

[1574] (Claim 1)

[1575] A means for acquiring the user's current location using QR codes or NFC tags installed at multiple locations within the commercial facility;

[1576] means for transmitting the current location information and destination information input by the user to a server;

[1577] a means for the server to calculate an optimal route based on the current location information and destination information;

[1578] A means for the server to generate a route guidance message using a generation AI;

[1579] means for the server to transmit the route guidance message to a user's terminal;

[1580] a means for displaying the route guidance message in the terminal;

[1581] A system including:

[1582] (Claim 2)

[1583] The system according to claim 1, characterized in that the generation AI generates route guidance messages taking into account the latest information within commercial facilities.

[1584] (Claim 3)

[1585] 2. The system according to claim 1, wherein the terminal provides real-time route guidance from the user's current location to the destination based on the route guidance message.

[1586] "Example 2: Combining Emotion Engines"

[1587] (Claim 1)

[1588] a means for acquiring a user's current location using codes or tags installed at multiple locations within the station;

[1589] means for transmitting the current location information and destination information input by the user to a server;

[1590] a means for the server to calculate an optimal route based on the current location information and destination information;

[1591] A means for the server to generate a route guidance message using a generation AI;

[1592] means for the server to transmit the route guidance message to a user's terminal;

[1593] a means for displaying the route guidance message in the terminal;

[1594] A means for collecting facial expression and voice data of the user in the terminal;

[1595] means including an emotion engine for analyzing the emotional state of the user based on the facial expression and voice data;

[1596] means for adjusting the content of the route guidance message based on the results of the emotion engine;

[1597] A system including:

[1598] (Claim 2)

[1599] The system according to claim 1, characterized in that the generation AI generates route guidance messages taking into account the latest information within the facility.

[1600] (Claim 3)

[1601] 2. The system according to claim 1, wherein the terminal provides real-time route guidance from the user's current location to the destination based on the route guidance message.

[1602] "Application example 2 when combining emotion engines"

[1603] (Claim 1)

[1604] A means for acquiring the user's current location using QR codes or NFC tags installed at multiple locations within the station;

[1605] means for transmitting the current location information and destination information input by the user to a server;

[1606] a means for the server to calculate an optimal route based on the current location information and destination information;

[1607] A means for the server to generate a route guidance message using a generation AI;

[1608] means for the server to transmit the route guidance message to a user's terminal;

[1609] a means for displaying the route guidance message in the terminal;

[1610] means for the terminal to analyze the user's emotional state using a camera and a microphone and transmit the results to a server;

[1611] means for the server to adjust the route guidance message taking into account the emotional state;

[1612] A system including:

[1613] (Claim 2)

[1614] The system according to claim 1, characterized in that the generation AI generates route guidance messages taking into account the latest station information and the user's emotional state.

[1615] (Claim 3)

[1616] The system according to claim 1, characterized in that the terminal provides real-time route guidance from the user's current location to the destination based on the route guidance message, and further adjusts according to the user's emotional state. [Explanation of symbols]

[1617] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring the user's current location using QR codes or NFC tags installed at multiple locations within the station; means for transmitting the current location information and destination information input by the user to a server; a means for the server to calculate an optimal route based on the current location information and destination information; A means for the server to generate a route guidance message using a generation AI; means for the server to transmit the route guidance message to a user's terminal; means for displaying the route guidance message in the terminal; A system including:

2. The system according to claim 1, characterized in that the generation AI generates a route guidance message taking into account the latest information within the station.

3. 2. The system according to claim 1, wherein the terminal provides real-time route guidance from the user's current location to the destination based on the route guidance message.

Images