System

Abstract

An object of a system according to an embodiment is to provide a visitor with a private conversation in which ambient noise is suppressed.SOLUTION: A system includes a super-directional speaker, a microphone, an analysis unit, and a response generation unit. A super-directional speaker delivers sound in a particular direction relative to the visitor. The microphone acquires the voice of the visitor. The analysis unit analyzes the sound acquired by the microphone. The response generation unit generates a response on the basis of the voice analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that guidance for visitors in commercial facilities and public areas is easily affected by surrounding noise, making it difficult to have private conversations.

[0005] The system according to the embodiment aims to provide visitors with a private conversation with reduced ambient noise. [Means for solving the problem]

[0006] A system according to an embodiment includes an ultradirectional speaker, a microphone, an analysis unit, and a response generation unit. The ultradirectional speaker transmits sound in a specific direction toward a visitor. The microphone acquires the visitor's voice. The analysis unit analyzes the voice acquired by the microphone. The response generation unit generates a response based on the voice analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide visitors with a private interaction with reduced ambient noise. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A guidance system according to an embodiment of the present invention provides guidance to visitors in commercial facilities and public areas through natural conversation using an ultra-directional speaker and microphone. This system can suppress ambient noise and enable private conversations. This allows visitors to receive services more easily and directly than searching for information on a smartphone, saving time and effort. By providing visitors with a comfortable and efficient guidance service in conjunction with a simple user interface, it is possible to improve visitor satisfaction and facility operational efficiency.

[0029] A guidance system according to an embodiment includes a superdirectional speaker, a microphone, an analysis unit, and a response generation unit. The superdirectional speaker can transmit sound in a specific direction to a visitor. For example, the superdirectional speaker can transmit sound only in a specific direction and suppress ambient noise. The superdirectional speaker can also adjust the sound's reach using technology for controlling sound directionality. The microphone acquires the visitor's voice. For example, the microphone can acquire the visitor's voice with high accuracy and remove ambient noise using noise cancellation technology. The microphone can also acquire the visitor's voice in real time and transmit it to the analysis unit. The analysis unit analyzes the voice acquired by the microphone. For example, the analysis unit can convert the visitor's voice into text data using speech recognition technology. The analysis unit can also analyze the intent of the visitor's question using natural language processing technology. The analysis unit can also analyze the tone and speed of the visitor's voice and analyze the visitor's emotional state using an emotion estimation function. The response generation unit generates a response based on the voice analyzed by the analysis unit. For example, the response generation unit generates an optimal response to a visitor's question. The response generation unit can also adjust the tone and speed of the response depending on the tone and speed of the visitor's voice. Furthermore, the response generation unit can generate flexible responses depending on the visitor's emotional state. As a result, the guidance system according to the embodiment can provide guidance through natural conversation by transmitting sound in a specific direction to the visitor, acquiring and analyzing the visitor's voice, and generating a response. For example, if a visitor asks, "Where is the restaurant in this facility?" the system can return a response such as, "It's on the second floor, on your left." This allows visitors to receive more convenient and direct service than searching for information on a smartphone, saving time and effort.

[0030] The analysis unit can analyze the intent of the visitor's question and generate the optimal answer. The analysis unit uses, for example, natural language processing technology to more accurately analyze the intent of the visitor's question and build a system that generates the optimal answer. For example, it analyzes the content of the visitor's question and provides an appropriate answer. The analysis unit also analyzes the intent of the visitor's question and generates the optimal answer based on the results. For example, if a visitor asks, "Where is the restaurant?", it provides guidance to a specific location. The analysis unit also uses natural language processing technology to develop an algorithm that analyzes the intent of the visitor's question and generates the optimal answer. For example, it learns the content of the visitor's question and provides the optimal answer. This makes it possible to analyze the intent of the visitor's question and generate the optimal answer, thereby providing more accurate guidance.

[0031] The analysis unit can adjust the tone or speed of the response depending on the tone or speed of the visitor's voice. For example, the analysis unit analyzes microphone audio data in real time and builds a system that adjusts the tone and speed of the response depending on the tone and speed of the visitor's voice. For example, if the visitor is in a hurry, the response is made faster. The analysis unit also analyzes the tone and speed of the visitor's voice and dynamically adjusts the tone and speed of the response based on the results. For example, if the visitor speaks in a calm tone, the response is also made calmer. The analysis unit also analyzes microphone audio data in real time and develops an algorithm that adjusts the tone and speed of the response depending on the tone and speed of the visitor's voice. For example, the analysis unit learns the characteristics of the visitor's voice and generates an optimal response. As a result, the tone and speed of the response can be adjusted depending on the tone and speed of the visitor's voice, thereby achieving a more natural conversation.

[0032] Superdirectional speakers can identify the location of visitors and automatically adjust the optimal volume and direction based on that location. For example, a system can be built using the sound waves of the superdirectional speaker to identify the location of visitors in real time and automatically adjust the optimal volume and direction based on that location. For example, the speaker's volume and direction can be dynamically changed as the visitor moves. The superdirectional speaker can also acquire the visitor's location information and adjust the sound waves of the superdirectional speaker based on that location. For example, the volume can be lowered as the visitor approaches and increased as the visitor moves away. The superdirectional speaker can also use the sound waves of the superdirectional speaker to identify the location of visitors and develop an algorithm that automatically adjusts the optimal volume and direction based on that location. For example, the speaker can track the visitor's movements and constantly maintain the optimal volume and direction. This allows for more effective guidance by automatically adjusting the optimal volume and direction based on the visitor's location.

[0033] By combining a super-directional speaker with a microphone, a visitor's voice can be transmitted only in a specific direction, making it possible to have a private conversation. By combining a super-directional speaker with a microphone, for example, a system can be built that transmits a visitor's voice only in a specific direction, preventing what the visitor is saying from being heard by those around them. Also, by combining a super-directional speaker with a microphone, a system can be built that transmits a visitor's voice only in a specific direction, making it possible to have a private conversation. For example, conversations at an information counter in a commercial facility can be prevented from being heard by other visitors. Also, by combining a super-directional speaker with a microphone, an algorithm can be developed that transmits a visitor's voice only in a specific direction, preventing other visitors from hearing the conversation. For example, the direction of the visitor's voice can be tracked in real time and the sound can be transmitted in the optimal direction. This makes it possible to have a private conversation by transmitting the visitor's voice only in a specific direction.

[0034] Ultra-directional speakers can attract visitors' attention by playing music or advertisements only in specific areas. Ultra-directional speakers can build systems that play music or advertisements only in specific areas, for example, playing promotional music only in specific areas of a commercial facility. Ultra-directional speakers can also attract visitors' attention by playing music or advertisements only in specific areas, for example, playing advertisements only in specific product corners. Ultra-directional speakers can also develop algorithms that play music or advertisements only in specific areas, for example, by tracking visitor movements and concentrating sound in specific areas. This makes it possible to attract visitors' attention by playing music or advertisements only in specific areas.

[0035] The analysis unit can provide guidance according to the visitor's preferences and tendencies by saving the conversation history and referring to past questions and answers. The analysis unit, for example, saves the conversation history and refers to past questions and answers to build a system that provides guidance according to the visitor's preferences and tendencies. For example, guidance is provided based on places the visitor has visited in the past. The analysis unit also refers to past questions and answers and provides guidance according to the visitor's preferences and tendencies based on the results. For example, if the visitor has asked about a restaurant in the past, restaurant guidance is provided again. The analysis unit also saves the conversation history and refers to past questions and answers to develop an algorithm that provides guidance according to the visitor's preferences and tendencies. For example, the analysis unit learns the visitor's past behavior and provides optimal guidance. In this way, by saving the conversation history and referring to past questions and answers, guidance according to the visitor's preferences and tendencies can be provided.

[0036] The analysis unit can elicit the visitor's interests and provide recommended information based on them. The analysis unit, for example, constructs a system that elicits the visitor's interests through natural conversation and provides recommended information based on them. For example, the system guides the visitor to events and stores that the visitor has shown interest in. The analysis unit also elicits the visitor's interests and provides recommended information based on the results. For example, if the visitor has shown interest in stores of a particular genre, the system guides the visitor to stores of that genre. The analysis unit also develops an algorithm that elicits the visitor's interests and provides recommended information based on them. For example, the analysis unit learns the content of the visitor's conversation and provides optimal recommended information. This makes it possible to improve visitor satisfaction by eliciting the visitor's interests and providing recommended information based on them.

[0037] The analysis unit can analyze the visitor's voice during a conversation and automatically display related information in response to specific keywords. The analysis unit, for example, builds a system that analyzes the visitor's voice during a conversation and automatically displays related information in response to specific keywords. For example, if a visitor says "restaurant," restaurant information is displayed. The analysis unit also analyzes the visitor's voice and automatically displays related information in response to specific keywords. For example, if a visitor says "restroom," the location of the restroom is displayed. The analysis unit also develops an algorithm that analyzes the visitor's voice during a conversation and automatically displays related information in response to specific keywords. For example, the analysis unit learns the characteristics of the visitor's voice and displays the most appropriate information. This makes it possible to analyze the visitor's voice during a conversation and automatically display related information in response to specific keywords, thereby improving visitor convenience.

[0038] The analysis unit can analyze a visitor's question using voice recognition technology and provide an instant answer based on the results. The analysis unit, for example, uses voice recognition technology to build a system that instantly analyzes a visitor's question and provides an instant answer. For example, if a visitor asks, "Where is the restroom?", the system will instantly guide the visitor to the location of the restroom. The analysis unit also analyzes a visitor's question using voice recognition technology and provides an instant answer based on the results. For example, if a visitor asks, "Where is the restaurant?", the system will instantly guide the visitor to the location of the restaurant. The analysis unit also uses voice recognition technology to develop an algorithm that instantly analyzes a visitor's question and provides an instant answer. For example, the analysis unit learns the content of the visitor's question and generates the optimal answer. This makes it possible to analyze a visitor's question using voice recognition technology and instantly provide an answer based on the results, thereby improving visitor convenience.

[0039] The analysis unit can use the visitor's location information to provide information from the nearest guidance point. For example, the analysis unit uses the visitor's location information to build a system that provides information from the nearest guidance point. For example, the analysis unit guides the visitor to the location of the nearest restroom from their current location. The analysis unit also acquires the visitor's location information and provides information from the nearest guidance point based on that location. For example, the analysis unit guides the visitor to the location of the nearest restaurant from their current location. The analysis unit also develops an algorithm that uses the visitor's location information to provide information from the nearest guidance point. For example, the analysis unit tracks the visitor's location in real time and provides optimal guidance. This makes it possible to improve visitor convenience by using the visitor's location information to provide information from the nearest guidance point.

[0040] The analysis unit can work in conjunction with a smartwatch or wearable device to provide guidance based on the visitor's health condition or activity level. The analysis unit, for example, works in conjunction with a smartwatch or wearable device to build a system that provides guidance based on the visitor's health condition or activity level. For example, if the visitor is tired, the analysis unit guides the visitor to rest spots. The analysis unit also acquires the visitor's health condition and activity level from the smartwatch or wearable device and provides guidance based on the results. For example, if the visitor is not getting enough exercise, the analysis unit guides the visitor to activity spots. The analysis unit also works in conjunction with a smartwatch or wearable device to develop an algorithm that provides guidance based on the visitor's health condition and activity level. For example, the analysis unit learns the visitor's health data and provides optimal guidance. This makes it possible to improve visitor convenience by working in conjunction with a smartwatch or wearable device to provide guidance based on the visitor's health condition and activity level.

[0041] The analysis unit can provide visual guidance in addition to audio guidance. The analysis unit, for example, builds a system that provides visual guidance in addition to audio guidance. For example, it uses AR technology to display guidance information on the visitor's smartphone. The analysis unit also combines audio guidance and visual guidance to provide easier-to-understand guidance to visitors. For example, it displays a map on the smartphone while providing audio guidance. The analysis unit also develops an algorithm that provides visual guidance in addition to audio guidance. For example, it displays guidance information using AR technology based on the visitor's location information. This makes it possible to improve convenience for visitors by providing visual guidance in addition to audio guidance.

[0042] The analysis unit can make interfaces using touch panels or voice recognition more intuitive, allowing visitors to operate them without hesitation. The analysis unit, for example, builds a system that makes interfaces using touch panels or voice recognition more intuitive. For example, it improves the design of the interface so that visitors can operate it easily. The analysis unit also makes interfaces using touch panels or voice recognition more intuitive so that visitors can operate them without hesitation. For example, it makes it possible for visitors to understand how to operate them at a glance. The analysis unit also develops algorithms that make interfaces using touch panels or voice recognition more intuitive. For example, it learns the operation history of visitors and provides an optimal interface. This makes interfaces using touch panels or voice recognition more intuitive so that visitors can operate them without hesitation.

[0043] The analysis unit can customize the interface design according to the visitor's age or background to improve usability. The analysis unit, for example, builds a system that customizes the interface design according to the visitor's age and background. For example, it provides designs for children and designs for the elderly. The analysis unit also customizes the interface design according to the visitor's age and background to improve usability. For example, it adjusts the font size and color according to the visitor's age. The analysis unit also develops an algorithm that customizes the interface design according to the visitor's age and background. For example, it learns the visitor's age and background data and provides the optimal design. This makes it possible to customize the interface design according to the visitor's age and background to improve usability.

[0044] The analysis unit can add a gesture recognition function to the interface, allowing visitors to operate it using their hands. For example, the analysis unit adds a gesture recognition function to the interface and builds a system that allows visitors to operate it using their hands. For example, it allows visitors to operate it by simply waving their hand. The analysis unit also adds a gesture recognition function to the interface so that visitors can operate it using their hands. For example, it allows visitors to select menus with hand movements. The analysis unit also develops an algorithm that adds a gesture recognition function to the interface and allows visitors to operate it using their hands. For example, it learns the visitor's gestures and provides an optimal operation method. In this way, the convenience of operation can be improved by adding a gesture recognition function to the interface and allowing visitors to operate it using their hands.

[0045] The analysis unit can incorporate facial recognition technology into the interface and provide customized guidance based on the visitor's personal information. The analysis unit, for example, incorporates facial recognition technology into the interface and builds a system that provides customized guidance based on the visitor's personal information. For example, it recognizes the visitor's face and provides guidance based on the visitor's past visiting history. The analysis unit also incorporates facial recognition technology into the interface to provide customized guidance based on the visitor's personal information. For example, it provides guidance to stores and events that suit the visitor's preferences. The analysis unit also incorporates facial recognition technology into the interface and develops an algorithm that provides customized guidance based on the visitor's personal information. For example, it learns the visitor's facial data and provides optimal guidance. In this way, by incorporating facial recognition technology into the interface and providing customized guidance based on the visitor's personal information, visitor satisfaction can be improved.

[0046] The analysis unit can collect visitor feedback in real time and reflect it in system improvements. For example, the analysis unit builds a system that collects visitor feedback in real time and improves the system based on the results. For example, it immediately reflects visitor opinions. The analysis unit also collects visitor feedback in real time and analyzes the data to identify areas for improvement in the system. For example, it improves areas of dissatisfaction with visitors. The analysis unit also collects visitor feedback in real time and develops an algorithm that improves the system based on the results. For example, it learns visitor feedback data and proposes optimal improvement measures. In this way, visitor satisfaction can be improved by collecting visitor feedback in real time and reflecting it in system improvements.

[0047] The analysis unit analyzes visitor behavior data to identify congestion levels or popular spots, thereby improving operational efficiency. The analysis unit, for example, analyzes visitor behavior data to build a system that identifies congestion levels and popular spots. For example, it analyzes visitor movement patterns to identify congested areas. The analysis unit also analyzes visitor behavior data and identifies congestion levels and popular spots based on the results. For example, it identifies areas where many visitors gather, improving operational efficiency. The analysis unit also analyzes visitor behavior data to develop an algorithm that identifies congestion levels and popular spots. For example, it learns visitor behavior data and proposes optimal operational methods. In this way, operational efficiency can be improved by analyzing visitor behavior data and identifying congestion levels and popular spots.

[0048] The analysis unit can cooperate with other services within the facility to provide benefits or discounts. For example, the analysis unit builds a system that cooperates with other services within the facility to provide benefits and discounts in order to improve visitor satisfaction. For example, the analysis unit cooperates with restaurants and shops to provide discount coupons. The analysis unit also cooperates with other services within the facility to provide benefits and discounts to visitors. For example, the analysis unit provides discounts for purchases at specific stores. The analysis unit also cooperates with other services within the facility to develop an algorithm that provides benefits and discounts in order to improve visitor satisfaction. For example, the analysis unit provides optimal benefits and discounts based on visitor behavior data. This makes it possible to cooperate with other services within the facility to provide benefits and discounts and improve visitor satisfaction.

[0049] The analysis unit can optimize the layout and arrangement within the facility based on the visitor behavior data, thereby improving operational efficiency. The analysis unit, for example, builds a system that optimizes the layout and layout within the facility based on the visitor behavior data. For example, it analyzes the movement patterns of visitors and proposes the optimal layout. The analysis unit also analyzes the visitor behavior data and optimizes the layout and layout within the facility based on the results. For example, it changes the layout to avoid congested areas. The analysis unit also develops an algorithm that optimizes the layout and layout within the facility based on the visitor behavior data. For example, it learns the visitor behavior data and proposes the optimal layout and layout. In this way, operational efficiency can be improved by optimizing the layout and layout within the facility based on the visitor behavior data.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The guidance system can use the visitor's location information to provide information from the nearest guidance point. For example, the system can guide the visitor to the location of the nearest restroom from their current location. The analysis unit also acquires the visitor's location information and provides information from the nearest guidance point based on that location. For example, the system can guide the visitor to the location of the nearest restaurant from their current location. The analysis unit also uses the visitor's location information to develop an algorithm that provides information from the nearest guidance point. For example, the analysis unit can track the visitor's location in real time and provide optimal guidance. This can improve visitor convenience by using the visitor's location information to provide information from the nearest guidance point.

[0052] The analysis unit can elicit the visitor's interests and provide recommended information based on them. For example, a system can be constructed that elicits the visitor's interests through natural conversation and provides recommended information based on them. For example, the system can guide the visitor to events or stores that the visitor has shown interest in. The analysis unit can also elicit the visitor's interests and provide recommended information based on the results. For example, if the visitor has shown interest in stores of a particular genre, the system can guide the visitor to stores of that genre. The analysis unit can also elicit the visitor's interests through natural conversation and develop an algorithm that provides recommended information based on them. For example, the analysis unit can learn the content of the visitor's conversation and provide optimal recommended information. This can improve visitor satisfaction by eliciting the visitor's interests and providing recommended information based on them.

[0053] The analysis unit can analyze the visitor's voice during a conversation and automatically display related information in response to specific keywords. For example, a system can be constructed that analyzes the visitor's voice during a conversation and automatically displays related information in response to specific keywords. For example, if a visitor says "restaurant," restaurant information is displayed. The analysis unit can also analyze the visitor's voice and automatically display related information in response to specific keywords. For example, if a visitor says "restroom," the location of the restroom is displayed. The analysis unit can also develop an algorithm that analyzes the visitor's voice during a conversation and automatically displays related information in response to specific keywords. For example, the system can learn the characteristics of the visitor's voice and display the most appropriate information. This can improve visitor convenience by analyzing the visitor's voice during a conversation and automatically displaying related information in response to specific keywords.

[0054] The analysis unit can work in conjunction with a smartwatch or wearable device to provide guidance based on the visitor's health condition or activity level. For example, a system can be constructed that works in conjunction with a smartwatch or wearable device to provide guidance based on the visitor's health condition or activity level. For example, if a visitor is tired, the system can guide them to rest spots. The analysis unit can also obtain the visitor's health condition and activity level from the smartwatch or wearable device and provide guidance based on the results. For example, if a visitor is not getting enough exercise, the system can guide them to activity spots. The analysis unit can also work in conjunction with a smartwatch or wearable device to develop an algorithm that provides guidance based on the visitor's health condition and activity level. For example, the analysis unit can learn the visitor's health data and provide optimal guidance. This can improve visitor convenience by working in conjunction with a smartwatch or wearable device to provide guidance based on the visitor's health condition and activity level.

[0055] The analysis unit can provide visual guidance in addition to audio guidance. For example, a system is built that provides visual guidance in addition to audio guidance. For example, AR technology is used to display guidance information on the visitor's smartphone. The analysis unit also combines audio guidance and visual guidance to provide easier-to-understand guidance to visitors. For example, a map is displayed on the smartphone while providing audio guidance. The analysis unit also develops an algorithm that provides visual guidance in addition to audio guidance. For example, AR technology is used to display guidance information based on the visitor's location information. This makes it possible to improve convenience for visitors by providing visual guidance in addition to audio guidance.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The super-directional speaker transmits sound in a specific direction to visitors. For example, the super-directional speaker can transmit sound only in a specific direction and suppress surrounding noise. In addition, the sound's reach can be adjusted using technology to control the sound's directionality. Step 2: The microphone captures the visitor's voice. For example, the microphone can capture the visitor's voice with high accuracy and eliminate ambient noise using noise cancellation technology. The microphone can also capture the visitor's voice in real time and send it to the analysis unit. Step 3: The analysis unit analyzes the audio captured by the microphone. For example, the analysis unit converts the visitor's voice into text data using voice recognition technology. The analysis unit can also analyze the intent of the visitor's question using natural language processing technology. Furthermore, the analysis unit can analyze the tone and speed of the visitor's voice and analyze the visitor's emotional state using an emotion estimation function. Step 4: The response generation unit generates a response based on the voice analyzed by the analysis unit. For example, the response generation unit generates an optimal answer to the visitor's question. The response generation unit can also adjust the tone and speed of the response according to the tone and speed of the visitor's voice. Furthermore, the response generation unit can generate flexible responses according to the visitor's emotional state.

[0058] (Example 2) A guidance system according to an embodiment of the present invention provides guidance to visitors in commercial facilities and public areas through natural conversation using an ultra-directional speaker and microphone. This system can suppress ambient noise and enable private conversations. This allows visitors to receive services more easily and directly than searching for information on a smartphone, saving time and effort. By providing visitors with a comfortable and efficient guidance service in conjunction with a simple user interface, it is possible to improve visitor satisfaction and facility operational efficiency.

[0059] A guidance system according to an embodiment includes a superdirectional speaker, a microphone, an analysis unit, and a response generation unit. The superdirectional speaker can transmit sound in a specific direction to a visitor. For example, the superdirectional speaker can transmit sound only in a specific direction and suppress ambient noise. The superdirectional speaker can also adjust the sound's reach using technology for controlling sound directionality. The microphone acquires the visitor's voice. For example, the microphone can acquire the visitor's voice with high accuracy and remove ambient noise using noise cancellation technology. The microphone can also acquire the visitor's voice in real time and transmit it to the analysis unit. The analysis unit analyzes the voice acquired by the microphone. For example, the analysis unit can convert the visitor's voice into text data using speech recognition technology. The analysis unit can also analyze the intent of the visitor's question using natural language processing technology. The analysis unit can also analyze the tone and speed of the visitor's voice and analyze the visitor's emotional state using an emotion estimation function. The response generation unit generates a response based on the voice analyzed by the analysis unit. For example, the response generation unit generates an optimal response to a visitor's question. The response generation unit can also adjust the tone and speed of the response depending on the tone and speed of the visitor's voice. Furthermore, the response generation unit can generate flexible responses depending on the visitor's emotional state. As a result, the guidance system according to the embodiment can provide guidance through natural conversation by transmitting sound in a specific direction to the visitor, acquiring and analyzing the visitor's voice, and generating a response. For example, if a visitor asks, "Where is the restaurant in this facility?" the system can return a response such as, "It's on the second floor, on your left." This allows visitors to receive more convenient and direct service than searching for information on a smartphone, saving time and effort.

[0060] The analysis unit can analyze the intent of the visitor's question and generate the optimal answer. The analysis unit uses, for example, natural language processing technology to more accurately analyze the intent of the visitor's question and build a system that generates the optimal answer. For example, it analyzes the content of the visitor's question and provides an appropriate answer. The analysis unit also analyzes the intent of the visitor's question and generates the optimal answer based on the results. For example, if a visitor asks, "Where is the restaurant?", it provides guidance to a specific location. The analysis unit also uses natural language processing technology to develop an algorithm that analyzes the intent of the visitor's question and generates the optimal answer. For example, it learns the content of the visitor's question and provides the optimal answer. This makes it possible to analyze the intent of the visitor's question and generate the optimal answer, thereby providing more accurate guidance.

[0061] The analysis unit can adjust the tone or speed of the response depending on the tone or speed of the visitor's voice. For example, the analysis unit analyzes microphone audio data in real time and builds a system that adjusts the tone and speed of the response depending on the tone and speed of the visitor's voice. For example, if the visitor is in a hurry, the response is made faster. The analysis unit also analyzes the tone and speed of the visitor's voice and dynamically adjusts the tone and speed of the response based on the results. For example, if the visitor speaks in a calm tone, the response is also made calmer. The analysis unit also analyzes microphone audio data in real time and develops an algorithm that adjusts the tone and speed of the response depending on the tone and speed of the visitor's voice. For example, the analysis unit learns the characteristics of the visitor's voice and generates an optimal response. As a result, the tone and speed of the response can be adjusted depending on the tone and speed of the visitor's voice, thereby achieving a more natural conversation.

[0062] The analysis unit can use the emotion estimation function to analyze the emotional state of the visitor and provide audio guidance according to the emotion. The analysis unit, for example, uses the emotion estimation function to analyze the emotional state of the visitor in real time and build a system that provides audio guidance according to that emotion. For example, if the visitor is nervous, the analysis unit provides guidance that helps the visitor relax. The analysis unit also analyzes the emotional state of the visitor and provides audio guidance according to the emotion based on the analysis result. For example, if the visitor is happy, the analysis unit provides guidance in a bright tone. The analysis unit also uses the emotion estimation function to analyze the emotional state of the visitor and develop an algorithm that provides audio guidance according to that emotion. For example, the analysis unit learns the visitor's emotional data and generates optimal guidance. In this way, the analysis unit can analyze the visitor's emotional state and provide audio guidance according to the emotion, thereby improving visitor satisfaction.

[0063] Superdirectional speakers can identify the location of visitors and automatically adjust the optimal volume and direction based on that location. For example, a system can be built using the sound waves of the superdirectional speaker to identify the location of visitors in real time and automatically adjust the optimal volume and direction based on that location. For example, the speaker's volume and direction can be dynamically changed as the visitor moves. The superdirectional speaker can also acquire the visitor's location information and adjust the sound waves of the superdirectional speaker based on that location. For example, the volume can be lowered as the visitor approaches and increased as the visitor moves away. The superdirectional speaker can also use the sound waves of the superdirectional speaker to identify the location of visitors and develop an algorithm that automatically adjusts the optimal volume and direction based on that location. For example, the speaker can track the visitor's movements and constantly maintain the optimal volume and direction. This allows for more effective guidance by automatically adjusting the optimal volume and direction based on the visitor's location.

[0064] By combining a super-directional speaker with a microphone, a visitor's voice can be transmitted only in a specific direction, making it possible to have a private conversation. By combining a super-directional speaker with a microphone, for example, a system can be built that transmits a visitor's voice only in a specific direction, preventing what the visitor is saying from being heard by those around them. Also, by combining a super-directional speaker with a microphone, a system can be built that transmits a visitor's voice only in a specific direction, making it possible to have a private conversation. For example, conversations at an information counter in a commercial facility can be prevented from being heard by other visitors. Also, by combining a super-directional speaker with a microphone, an algorithm can be developed that transmits a visitor's voice only in a specific direction, preventing other visitors from hearing the conversation. For example, the direction of the visitor's voice can be tracked in real time and the sound can be transmitted in the optimal direction. This makes it possible to have a private conversation by transmitting the visitor's voice only in a specific direction.

[0065] Ultra-directional speakers can attract visitors' attention by playing music or advertisements only in specific areas. Ultra-directional speakers can build systems that play music or advertisements only in specific areas, for example, playing promotional music only in specific areas of a commercial facility. Ultra-directional speakers can also attract visitors' attention by playing music or advertisements only in specific areas, for example, playing advertisements only in specific product corners. Ultra-directional speakers can also develop algorithms that play music or advertisements only in specific areas, for example, by tracking visitor movements and concentrating sound in specific areas. This makes it possible to attract visitors' attention by playing music or advertisements only in specific areas.

[0066] The analysis unit can provide music or environmental sounds according to the visitor's emotions. The analysis unit, for example, uses an emotion estimation function to build a system that provides music and environmental sounds according to the visitor's emotions. For example, if a visitor is feeling stressed, relaxing music is played. The analysis unit also analyzes the visitor's emotional state and provides music and environmental sounds according to the emotion based on the analysis result. For example, if the visitor is excited, energetic music is played. The analysis unit also uses the emotion estimation function to develop an algorithm that provides music and environmental sounds according to the visitor's emotions. For example, it learns the visitor's emotional data and generates optimal music and environmental sounds. In this way, music and environmental sounds according to the visitor's emotions can be provided to promote relaxation and excitement in the visitor.

[0067] The analysis unit can provide guidance according to the visitor's preferences and tendencies by saving the conversation history and referring to past questions and answers. The analysis unit, for example, saves the conversation history and refers to past questions and answers to build a system that provides guidance according to the visitor's preferences and tendencies. For example, guidance is provided based on places the visitor has visited in the past. The analysis unit also refers to past questions and answers and provides guidance according to the visitor's preferences and tendencies based on the results. For example, if the visitor has asked about a restaurant in the past, restaurant guidance is provided again. The analysis unit also saves the conversation history and refers to past questions and answers to develop an algorithm that provides guidance according to the visitor's preferences and tendencies. For example, the analysis unit learns the visitor's past behavior and provides optimal guidance. In this way, by saving the conversation history and referring to past questions and answers, guidance according to the visitor's preferences and tendencies can be provided.

[0068] The analysis unit generates flexible responses according to the visitor's emotions, enabling a more friendly conversation. The analysis unit, for example, uses an emotion estimation function to build a system that generates flexible responses according to the visitor's emotions. For example, if the visitor is feeling anxious, a reassuring response is provided. The analysis unit also analyzes the visitor's emotional state and generates flexible responses according to the emotions based on the results. For example, if the visitor is happy, a response is provided in a bright tone. The analysis unit also uses the emotion estimation function to develop an algorithm that generates flexible responses according to the visitor's emotions. For example, it learns the visitor's emotional data and generates optimal responses. This allows a more friendly conversation to be achieved by generating flexible responses according to the visitor's emotions.

[0069] The analysis unit can elicit the visitor's interests and provide recommended information based on them. The analysis unit, for example, constructs a system that elicits the visitor's interests through natural conversation and provides recommended information based on them. For example, the system guides the visitor to events and stores that the visitor has shown interest in. The analysis unit also elicits the visitor's interests and provides recommended information based on the results. For example, if the visitor has shown interest in stores of a particular genre, the system guides the visitor to stores of that genre. The analysis unit also develops an algorithm that elicits the visitor's interests and provides recommended information based on them. For example, the analysis unit learns the content of the visitor's conversation and provides optimal recommended information. This makes it possible to improve visitor satisfaction by eliciting the visitor's interests and providing recommended information based on them.

[0070] The analysis unit can analyze the visitor's voice during a conversation and automatically display related information in response to specific keywords. The analysis unit, for example, builds a system that analyzes the visitor's voice during a conversation and automatically displays related information in response to specific keywords. For example, if a visitor says "restaurant," restaurant information is displayed. The analysis unit also analyzes the visitor's voice and automatically displays related information in response to specific keywords. For example, if a visitor says "restroom," the location of the restroom is displayed. The analysis unit also develops an algorithm that analyzes the visitor's voice during a conversation and automatically displays related information in response to specific keywords. For example, the analysis unit learns the characteristics of the visitor's voice and displays the most appropriate information. This makes it possible to analyze the visitor's voice during a conversation and automatically display related information in response to specific keywords, thereby improving visitor convenience.

[0071] The analysis unit can provide event or promotion information according to the visitor's emotions. The analysis unit, for example, uses an emotion estimation function to build a system that provides event and promotion information according to the visitor's emotions. For example, if the visitor is excited, energetic events are introduced. The analysis unit also analyzes the visitor's emotional state and provides event and promotion information according to the emotions based on the results. For example, if the visitor is relaxed, relaxation events are introduced. The analysis unit also uses the emotion estimation function to develop an algorithm that provides event and promotion information according to the visitor's emotions. For example, it learns visitor emotional data and provides optimal event and promotion information. This makes it possible to attract the visitor's interest by providing event and promotion information according to the visitor's emotions.

[0072] The analysis unit can analyze a visitor's question using voice recognition technology and provide an instant answer based on the results. The analysis unit, for example, uses voice recognition technology to build a system that instantly analyzes a visitor's question and provides an instant answer. For example, if a visitor asks, "Where is the restroom?", the system will instantly guide the visitor to the location of the restroom. The analysis unit also analyzes a visitor's question using voice recognition technology and provides an instant answer based on the results. For example, if a visitor asks, "Where is the restaurant?", the system will instantly guide the visitor to the location of the restaurant. The analysis unit also uses voice recognition technology to develop an algorithm that instantly analyzes a visitor's question and provides an instant answer. For example, the analysis unit learns the content of the visitor's question and generates the optimal answer. This makes it possible to analyze a visitor's question using voice recognition technology and instantly provide an answer based on the results, thereby improving visitor convenience.

[0073] The analysis unit can use the visitor's location information to provide information from the nearest guidance point. For example, the analysis unit uses the visitor's location information to build a system that provides information from the nearest guidance point. For example, the analysis unit guides the visitor to the location of the nearest restroom from their current location. The analysis unit also acquires the visitor's location information and provides information from the nearest guidance point based on that location. For example, the analysis unit guides the visitor to the location of the nearest restaurant from their current location. The analysis unit also develops an algorithm that uses the visitor's location information to provide information from the nearest guidance point. For example, the analysis unit tracks the visitor's location in real time and provides optimal guidance. This makes it possible to improve visitor convenience by using the visitor's location information to provide information from the nearest guidance point.

[0074] The analysis unit can select the optimal guidance method according to the visitor's emotions, thereby reducing stress. The analysis unit, for example, uses an emotion estimation function to build a system that selects the optimal guidance method according to the visitor's emotions. For example, if the visitor is feeling stressed, a guidance method that helps them relax is provided. The analysis unit also analyzes the visitor's emotional state and selects the optimal guidance method according to the emotions based on the results. For example, if the visitor is nervous, a guidance method that reassures the visitor is provided. The analysis unit also uses the emotion estimation function to develop an algorithm that selects the optimal guidance method according to the visitor's emotions. For example, it learns the visitor's emotional data and provides the optimal guidance method. In this way, the optimal guidance method according to the visitor's emotions can be selected, thereby reducing the visitor's stress.

[0075] The analysis unit can work in conjunction with a smartwatch or wearable device to provide guidance based on the visitor's health condition or activity level. The analysis unit, for example, works in conjunction with a smartwatch or wearable device to build a system that provides guidance based on the visitor's health condition or activity level. For example, if the visitor is tired, the analysis unit guides the visitor to rest spots. The analysis unit also acquires the visitor's health condition and activity level from the smartwatch or wearable device and provides guidance based on the results. For example, if the visitor is not getting enough exercise, the analysis unit guides the visitor to activity spots. The analysis unit also works in conjunction with a smartwatch or wearable device to develop an algorithm that provides guidance based on the visitor's health condition and activity level. For example, the analysis unit learns the visitor's health data and provides optimal guidance. This makes it possible to improve visitor convenience by working in conjunction with a smartwatch or wearable device to provide guidance based on the visitor's health condition and activity level.

[0076] The analysis unit can provide visual guidance in addition to audio guidance. The analysis unit, for example, builds a system that provides visual guidance in addition to audio guidance. For example, it uses AR technology to display guidance information on the visitor's smartphone. The analysis unit also combines audio guidance and visual guidance to provide easier-to-understand guidance to visitors. For example, it displays a map on the smartphone while providing audio guidance. The analysis unit also develops an algorithm that provides visual guidance in addition to audio guidance. For example, it displays guidance information using AR technology based on the visitor's location information. This makes it possible to improve convenience for visitors by providing visual guidance in addition to audio guidance.

[0077] The analysis unit can recommend relaxation spots or rest areas according to the visitor's emotions. The analysis unit, for example, uses an emotion estimation function to build a system that recommends relaxation spots or rest areas according to the visitor's emotions. For example, if the visitor is feeling stressed, the analysis unit recommends relaxation spots. The analysis unit also analyzes the visitor's emotional state and, based on the results, recommends relaxation spots or rest areas according to the emotions. For example, if the visitor is tired, the analysis unit recommends rest areas. The analysis unit also uses the emotion estimation function to develop an algorithm that recommends relaxation spots or rest areas according to the visitor's emotions. For example, the analysis unit learns the visitor's emotional data and provides the optimal relaxation spots or rest areas. This makes it possible to reduce the visitor's stress by recommending relaxation spots or rest areas according to the visitor's emotions.

[0078] The analysis unit can make interfaces using touch panels or voice recognition more intuitive, allowing visitors to operate them without hesitation. The analysis unit, for example, builds a system that makes interfaces using touch panels or voice recognition more intuitive. For example, it improves the design of the interface so that visitors can operate it easily. The analysis unit also makes interfaces using touch panels or voice recognition more intuitive so that visitors can operate them without hesitation. For example, it makes it possible for visitors to understand how to operate them at a glance. The analysis unit also develops algorithms that make interfaces using touch panels or voice recognition more intuitive. For example, it learns the operation history of visitors and provides an optimal interface. This makes interfaces using touch panels or voice recognition more intuitive so that visitors can operate them without hesitation.

[0079] The analysis unit can customize the interface design according to the visitor's age or background to improve usability. The analysis unit, for example, builds a system that customizes the interface design according to the visitor's age and background. For example, it provides designs for children and designs for the elderly. The analysis unit also customizes the interface design according to the visitor's age and background to improve usability. For example, it adjusts the font size and color according to the visitor's age. The analysis unit also develops an algorithm that customizes the interface design according to the visitor's age and background. For example, it learns the visitor's age and background data and provides the optimal design. This makes it possible to customize the interface design according to the visitor's age and background to improve usability.

[0080] The analysis unit can dynamically change the color or design of the interface according to the visitor's emotions. The analysis unit, for example, uses an emotion estimation function to build a system that dynamically changes the color or design of the interface according to the visitor's emotions. For example, if the visitor is relaxed, the color is changed to a calmer hue. The analysis unit also analyzes the visitor's emotional state and dynamically changes the color or design of the interface according to the emotion based on the analysis result. For example, if the visitor is excited, the color is changed to a brighter hue. The analysis unit also uses the emotion estimation function to develop an algorithm that dynamically changes the color or design of the interface according to the visitor's emotions. For example, the analysis unit learns the visitor's emotional data and provides the optimal color or design. This makes it possible to improve visitor satisfaction by dynamically changing the color or design of the interface according to the visitor's emotions.

[0081] The analysis unit can add a gesture recognition function to the interface, allowing visitors to operate it using their hands. For example, the analysis unit adds a gesture recognition function to the interface and builds a system that allows visitors to operate it using their hands. For example, it allows visitors to operate it by simply waving their hand. The analysis unit also adds a gesture recognition function to the interface so that visitors can operate it using their hands. For example, it allows visitors to select menus with hand movements. The analysis unit also develops an algorithm that adds a gesture recognition function to the interface and allows visitors to operate it using their hands. For example, it learns the visitor's gestures and provides an optimal operation method. In this way, the convenience of operation can be improved by adding a gesture recognition function to the interface and allowing visitors to operate it using their hands.

[0082] The analysis unit can incorporate facial recognition technology into the interface and provide customized guidance based on the visitor's personal information. The analysis unit, for example, incorporates facial recognition technology into the interface and builds a system that provides customized guidance based on the visitor's personal information. For example, it recognizes the visitor's face and provides guidance based on the visitor's past visiting history. The analysis unit also incorporates facial recognition technology into the interface to provide customized guidance based on the visitor's personal information. For example, it provides guidance to stores and events that suit the visitor's preferences. The analysis unit also incorporates facial recognition technology into the interface and develops an algorithm that provides customized guidance based on the visitor's personal information. For example, it learns the visitor's facial data and provides optimal guidance. In this way, by incorporating facial recognition technology into the interface and providing customized guidance based on the visitor's personal information, visitor satisfaction can be improved.

[0083] The analysis unit can adjust the response speed or feedback of the interface according to the visitor's emotions. The analysis unit, for example, uses an emotion estimation function to build a system that adjusts the response speed or feedback of the interface according to the visitor's emotions. For example, if the visitor is in a hurry, the response speed is increased. The analysis unit also analyzes the visitor's emotional state and adjusts the response speed or feedback of the interface according to the emotion based on the analysis result. For example, if the visitor is relaxed, the response speed is decreased. The analysis unit also uses the emotion estimation function to develop an algorithm that adjusts the response speed or feedback of the interface according to the visitor's emotions. For example, the analysis unit learns the visitor's emotional data and provides optimal response speed or feedback. In this way, the response speed or feedback of the interface can be adjusted according to the visitor's emotions, thereby improving visitor satisfaction.

[0084] The analysis unit can collect visitor feedback in real time and reflect it in system improvements. For example, the analysis unit builds a system that collects visitor feedback in real time and improves the system based on the results. For example, it immediately reflects visitor opinions. The analysis unit also collects visitor feedback in real time and analyzes the data to identify areas for improvement in the system. For example, it improves areas of dissatisfaction with visitors. The analysis unit also collects visitor feedback in real time and develops an algorithm that improves the system based on the results. For example, it learns visitor feedback data and proposes optimal improvement measures. In this way, visitor satisfaction can be improved by collecting visitor feedback in real time and reflecting it in system improvements.

[0085] The analysis unit analyzes visitor behavior data to identify congestion levels or popular spots, thereby improving operational efficiency. The analysis unit, for example, analyzes visitor behavior data to build a system that identifies congestion levels and popular spots. For example, it analyzes visitor movement patterns to identify congested areas. The analysis unit also analyzes visitor behavior data and identifies congestion levels and popular spots based on the results. For example, it identifies areas where many visitors gather, improving operational efficiency. The analysis unit also analyzes visitor behavior data to develop an algorithm that identifies congestion levels and popular spots. For example, it learns visitor behavior data and proposes optimal operational methods. In this way, operational efficiency can be improved by analyzing visitor behavior data and identifying congestion levels and popular spots.

[0086] The analysis unit can provide services according to the visitor's emotions and improve satisfaction. The analysis unit, for example, uses an emotion estimation function to build a system that provides services according to the visitor's emotions. For example, if the visitor is relaxed, a relaxation service is provided. The analysis unit also analyzes the visitor's emotional state and provides a service according to the emotion based on the results. For example, if the visitor is excited, an energetic service is provided. The analysis unit also uses the emotion estimation function to develop an algorithm that provides a service according to the visitor's emotions. For example, it learns visitor emotion data and provides the optimal service. This makes it possible to provide a service according to the visitor's emotions and improve visitor satisfaction.

[0087] The analysis unit can cooperate with other services within the facility to provide benefits or discounts. For example, the analysis unit builds a system that cooperates with other services within the facility to provide benefits and discounts in order to improve visitor satisfaction. For example, the analysis unit cooperates with restaurants and shops to provide discount coupons. The analysis unit also cooperates with other services within the facility to provide benefits and discounts to visitors. For example, the analysis unit provides discounts for purchases at specific stores. The analysis unit also cooperates with other services within the facility to develop an algorithm that provides benefits and discounts in order to improve visitor satisfaction. For example, the analysis unit provides optimal benefits and discounts based on visitor behavior data. This makes it possible to cooperate with other services within the facility to provide benefits and discounts and improve visitor satisfaction.

[0088] The analysis unit can optimize the layout and arrangement within the facility based on the visitor behavior data, thereby improving operational efficiency. The analysis unit, for example, builds a system that optimizes the layout and layout within the facility based on the visitor behavior data. For example, it analyzes the movement patterns of visitors and proposes the optimal layout. The analysis unit also analyzes the visitor behavior data and optimizes the layout and layout within the facility based on the results. For example, it changes the layout to avoid congested areas. The analysis unit also develops an algorithm that optimizes the layout and layout within the facility based on the visitor behavior data. For example, it learns the visitor behavior data and proposes the optimal layout and layout. In this way, operational efficiency can be improved by optimizing the layout and layout within the facility based on the visitor behavior data.

[0089] The analysis unit can suggest events or activities that correspond to the visitor's emotions, thereby attracting their interest. The analysis unit, for example, uses an emotion estimation function to build a system that suggests events and activities that correspond to the visitor's emotions. For example, if the visitor is excited, an energetic event is suggested. The analysis unit also analyzes the visitor's emotional state and, based on the results, suggests events and activities that correspond to the emotions. For example, if the visitor is relaxed, a relaxation activity is suggested. The analysis unit also uses the emotion estimation function to develop an algorithm that suggests events and activities that correspond to the visitor's emotions. For example, it learns visitor emotional data and suggests optimal events and activities. This makes it possible to attract the visitor's interest by suggesting events and activities that correspond to the visitor's emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The guidance system can use the visitor's location information to provide information from the nearest guidance point. For example, the system can guide the visitor to the location of the nearest restroom from their current location. The analysis unit also acquires the visitor's location information and provides information from the nearest guidance point based on that location. For example, the system can guide the visitor to the location of the nearest restaurant from their current location. The analysis unit also uses the visitor's location information to develop an algorithm that provides information from the nearest guidance point. For example, the analysis unit can track the visitor's location in real time and provide optimal guidance. This can improve visitor convenience by using the visitor's location information to provide information from the nearest guidance point.

[0092] The analysis unit can elicit the visitor's interests and provide recommended information based on them. For example, a system can be constructed that elicits the visitor's interests through natural conversation and provides recommended information based on them. For example, the system can guide the visitor to events or stores that the visitor has shown interest in. The analysis unit can also elicit the visitor's interests and provide recommended information based on the results. For example, if the visitor has shown interest in stores of a particular genre, the system can guide the visitor to stores of that genre. The analysis unit can also elicit the visitor's interests through natural conversation and develop an algorithm that provides recommended information based on them. For example, the analysis unit can learn the content of the visitor's conversation and provide optimal recommended information. This can improve visitor satisfaction by eliciting the visitor's interests and providing recommended information based on them.

[0093] The analysis unit can analyze the visitor's voice during a conversation and automatically display related information in response to specific keywords. For example, a system can be constructed that analyzes the visitor's voice during a conversation and automatically displays related information in response to specific keywords. For example, if a visitor says "restaurant," restaurant information is displayed. The analysis unit can also analyze the visitor's voice and automatically display related information in response to specific keywords. For example, if a visitor says "restroom," the location of the restroom is displayed. The analysis unit can also develop an algorithm that analyzes the visitor's voice during a conversation and automatically displays related information in response to specific keywords. For example, the system can learn the characteristics of the visitor's voice and display the most appropriate information. This can improve visitor convenience by analyzing the visitor's voice during a conversation and automatically displaying related information in response to specific keywords.

[0094] The analysis unit can work in conjunction with a smartwatch or wearable device to provide guidance based on the visitor's health condition or activity level. For example, a system can be constructed that works in conjunction with a smartwatch or wearable device to provide guidance based on the visitor's health condition or activity level. For example, if a visitor is tired, the system can guide them to rest spots. The analysis unit can also obtain the visitor's health condition and activity level from the smartwatch or wearable device and provide guidance based on the results. For example, if a visitor is not getting enough exercise, the system can guide them to activity spots. The analysis unit can also work in conjunction with a smartwatch or wearable device to develop an algorithm that provides guidance based on the visitor's health condition and activity level. For example, the analysis unit can learn the visitor's health data and provide optimal guidance. This can improve visitor convenience by working in conjunction with a smartwatch or wearable device to provide guidance based on the visitor's health condition and activity level.

[0095] The analysis unit can provide visual guidance in addition to audio guidance. For example, a system is built that provides visual guidance in addition to audio guidance. For example, AR technology is used to display guidance information on the visitor's smartphone. The analysis unit also combines audio guidance and visual guidance to provide easier-to-understand guidance to visitors. For example, a map is displayed on the smartphone while providing audio guidance. The analysis unit also develops an algorithm that provides visual guidance in addition to audio guidance. For example, AR technology is used to display guidance information based on the visitor's location information. This makes it possible to improve convenience for visitors by providing visual guidance in addition to audio guidance.

[0096] The analysis unit can provide music or environmental sounds according to the visitor's emotions. For example, using the emotion estimation function, a system is constructed that provides music or environmental sounds according to the visitor's emotions. For example, if the visitor is feeling stressed, relaxing music is played. The analysis unit also analyzes the visitor's emotional state and provides music or environmental sounds according to the emotion based on the analysis result. For example, if the visitor is excited, energetic music is played. The analysis unit also uses the emotion estimation function to develop an algorithm that provides music or environmental sounds according to the visitor's emotions. For example, it learns the visitor's emotional data and generates optimal music or environmental sounds. In this way, music or environmental sounds according to the visitor's emotions can be provided, promoting relaxation or excitement in the visitor.

[0097] The analysis unit generates flexible responses according to the visitor's emotions, enabling a more friendly conversation. For example, a system is constructed that uses the emotion estimation function to generate flexible responses according to the visitor's emotions. For example, if the visitor is feeling anxious, a reassuring response is provided. The analysis unit also analyzes the visitor's emotional state and generates flexible responses according to the emotions based on the results. For example, if the visitor is happy, a response is provided in a bright tone. The analysis unit also uses the emotion estimation function to develop an algorithm that generates flexible responses according to the visitor's emotions. For example, the system learns the visitor's emotional data and generates optimal responses. This allows for a more friendly conversation by generating flexible responses according to the visitor's emotions.

[0098] The analysis unit can provide event or promotion information according to the visitor's emotions. For example, a system can be constructed using the emotion estimation function to provide event and promotion information according to the visitor's emotions. For example, if the visitor is excited, energetic events can be introduced. The analysis unit can also analyze the visitor's emotional state and, based on the results, provide event and promotion information according to the emotion. For example, if the visitor is relaxed, relaxation events can be introduced. The analysis unit can also use the emotion estimation function to develop an algorithm that provides event and promotion information according to the visitor's emotions. For example, the analysis unit can learn visitor emotional data and provide optimal event and promotion information. This can attract the visitor's interest by providing event and promotion information according to the visitor's emotions.

[0099] The analysis unit can recommend relaxation spots or rest areas according to the visitor's emotions. For example, using the emotion estimation function, a system can be built that recommends relaxation spots or rest areas according to the visitor's emotions. For example, if the visitor is feeling stressed, the system can recommend relaxation spots. The analysis unit can also analyze the visitor's emotional state and, based on the results, recommend relaxation spots or rest areas according to the visitor's emotions. For example, if the visitor is tired, the system can recommend rest areas. The analysis unit can also use the emotion estimation function to develop an algorithm that recommends relaxation spots or rest areas according to the visitor's emotions. For example, the system can learn the visitor's emotional data and provide the optimal relaxation spots or rest areas. This can reduce the visitor's stress by recommending relaxation spots or rest areas according to the visitor's emotions.

[0100] The analysis unit can dynamically change the color or design of the interface according to the visitor's emotions. For example, using the emotion estimation function, a system is constructed that dynamically changes the color or design of the interface according to the visitor's emotions. For example, if the visitor is relaxed, the color is changed to a calmer hue. The analysis unit also analyzes the visitor's emotional state and dynamically changes the color or design of the interface according to the emotion based on the results. For example, if the visitor is excited, the color is changed to a brighter hue. The analysis unit also uses the emotion estimation function to develop an algorithm that dynamically changes the color or design of the interface according to the visitor's emotions. For example, the algorithm learns the visitor's emotional data and provides the optimal color or design. This makes it possible to improve visitor satisfaction by dynamically changing the color or design of the interface according to the visitor's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The super-directional speaker transmits sound in a specific direction to visitors. For example, the super-directional speaker can transmit sound only in a specific direction and suppress surrounding noise. In addition, the sound's reach can be adjusted using technology to control the sound's directionality. Step 2: The microphone captures the visitor's voice. For example, the microphone can capture the visitor's voice with high accuracy and eliminate ambient noise using noise cancellation technology. The microphone can also capture the visitor's voice in real time and send it to the analysis unit. Step 3: The analysis unit analyzes the audio captured by the microphone. For example, the analysis unit converts the visitor's voice into text data using voice recognition technology. The analysis unit can also analyze the intent of the visitor's question using natural language processing technology. Furthermore, the analysis unit can analyze the tone and speed of the visitor's voice and analyze the visitor's emotional state using an emotion estimation function. Step 4: The response generation unit generates a response based on the voice analyzed by the analysis unit. For example, the response generation unit generates an optimal answer to the visitor's question. The response generation unit can also adjust the tone and speed of the response according to the tone and speed of the visitor's voice. Furthermore, the response generation unit can generate flexible responses according to the visitor's emotional state.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] 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.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] 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.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] 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.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "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.

[0157] 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."

[0158] 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.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] 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.

[0166] 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.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with a super-directional speaker and microphone, The ultra-directional speaker comprises: It transmits sound in a specific direction to the visitor, The microphone is Capture visitor audio, an analysis unit that analyzes the sound captured by the microphone; a response generation unit that generates a response based on the voice analyzed by the analysis unit; A system characterized by:

2. The ultra-directional speaker comprises: Identifying the location of the visitor and automatically adjusting the optimal volume and direction according to that location 2. The system of claim 1.

3. The analysis unit Analyzing the intent of the visitor's question and generating the most appropriate answer 2. The system of claim 1.

4. The analysis unit Analyzing the visitor's question using voice recognition technology and providing an immediate answer based on the results 2. The system of claim 1.

5. The analysis unit Analyzing the emotional state of the visitor and providing voice guidance according to the emotion.

2. The system of claim 1.

Images