system

Abstract

The system according to this embodiment aims for a robot to think for itself and perform appropriate actions according to the conditions inside the store. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a control unit. The acquisition unit acquires visual information to understand the situation inside the store. The analysis unit analyzes the visual information acquired by the acquisition unit. The generation unit generates actions based on the information analyzed by the analysis unit. The control unit controls the robot based on the actions generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the flexibility of robots used in restaurants is low and it is difficult to make appropriate responses according to the in-store situation.

[0005] The system according to the embodiment aims to enable the robot to think on its own and perform appropriate actions according to the in-store situation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a control unit. The acquisition unit acquires visual information to understand the situation inside the store. The analysis unit analyzes the visual information acquired by the acquisition unit. The generation unit generates actions based on the information analyzed by the analysis unit. The control unit controls the robot based on the actions generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows the robot to think for itself and perform appropriate actions according to the conditions inside the store. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system for improving the flexibility and communication capabilities of robots used in restaurants. This system enables robots to think, converse, and serve customers on their own. The system grasps the situation in the restaurant, finds customers, takes orders, pours water according to the orders, and serves the food. This series of actions is optimized by generative AI, allowing the robot to respond autonomously. For example, to grasp the situation in the restaurant, the system acquires visual information, and the generative AI analyzes that information. For example, the system uses a camera to acquire images of the restaurant, and the generative AI analyzes the images to identify the location of customers. This allows the system to find customers. Next, the system approaches the customer and takes their order. The generative AI understands the customer's request and takes an appropriate response. For example, if a customer says, "I would like to order," the system approaches to take the order and asks about the order details. At this time, the generative AI analyzes the customer's words and understands the order details. Furthermore, the system pours water according to the order and serves the food. The generative AI optimizes the system's actions and serves the food efficiently. For example, if the system finds an empty glass of water, it automatically pours water. Furthermore, when serving ordered dishes, the generating AI calculates the optimal route and delivers the food efficiently. This allows the system to respond autonomously according to the situation in the restaurant. This improves the operational efficiency of restaurants and helps alleviate labor shortages. For example, by having the system handle not only serving and clearing but also customer service and order taking, the burden on staff is reduced. In addition, the system's autonomous response improves the quality of service in the restaurant and increases customer satisfaction. This improves the operational efficiency of restaurants and helps alleviate labor shortages.

[0029] The system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a control unit. The acquisition unit acquires visual information to understand the situation inside the store. The acquisition unit acquires images of the store using, for example, a camera. The acquisition unit can also use multiple cameras to understand, for example, the level of crowding and customer movements inside the store. The analysis unit analyzes the visual information acquired by the acquisition unit. The analysis unit identifies the location of customers using, for example, image analysis technology. The analysis unit can also identify specific customers using, for example, facial recognition technology. The generation unit generates actions based on the information analyzed by the analysis unit. The generation unit generates actions to understand customer requests and respond appropriately. The generation unit can also understand customer orders using, for example, natural language processing technology. The control unit controls the robot based on the actions generated by the generation unit. The control unit controls the robot's movement and gestures, for example. The control unit can also control voice output to provide explanations to customers. As a result, the system according to this embodiment can autonomously respond according to the situation inside the store.

[0030] The acquisition unit acquires visual information to understand the situation inside the store. For example, the acquisition unit acquires images of the store using cameras. Specifically, it uses multiple high-resolution cameras installed on the ceiling and walls of the store to acquire wide-area visual information in real time. These cameras are equipped with wide-angle lenses and can cover every corner of the store. The cameras also have infrared capabilities to acquire clear images even in low-light environments. The acquisition unit can also use multiple cameras to understand the level of congestion and customer movement inside the store. This allows the acquisition unit to understand the congestion and customer flow in each area of ​​the store in detail. Furthermore, the acquisition unit transmits the camera footage to a central database in real time, making it accessible to the analysis unit. This allows the acquisition unit to accurately and quickly understand the situation inside the store and support the efficient operation of the entire system.

[0031] The analysis unit analyzes the visual information acquired by the acquisition unit. For example, the analysis unit uses image analysis technology to identify the location of customers. Specifically, it uses an image recognition algorithm based on deep learning to detect the posture and movement of customers from camera footage and identify the location of each customer. The analysis unit can also identify specific customers using facial recognition technology. Facial recognition technology can identify specific customers by comparing them with a pre-registered facial database and track their movements. Furthermore, the analysis unit can analyze customer behavior patterns and extract information such as which areas customers stay in for a long time and which products they show interest in. This allows the analysis unit to gain a detailed understanding of the store situation and provide basic data for taking appropriate action based on customer behavior. The analysis unit transmits these analysis results to the generation unit in real time, enabling a rapid response.

[0032] The generation unit generates actions based on the information analyzed by the analysis unit. For example, the generation unit generates actions to understand a customer's request and respond appropriately. Specifically, it uses natural language processing technology to analyze the customer's statements and understand their intent. For example, if a customer says, "Please tell me about this product," the generation unit understands the request and generates actions to provide an appropriate explanation. The generation unit can also use natural language processing technology to understand the customer's order. It analyzes the order, checks the necessary product information and inventory status, and generates instructions to take appropriate action. Furthermore, the generation unit can generate actions to provide individualized responses based on the customer's behavior patterns and location information provided by the analysis unit. This allows the generation unit to respond flexibly to customer needs and improve customer satisfaction. The generation unit transmits the generated actions to the control unit and provides instructions for executing the actual actions.

[0033] The control unit controls the robot based on the actions generated by the generation unit. For example, the control unit controls the robot's movement and gestures. Specifically, it controls the robot's joints and motors to move to a designated position or perform specific gestures. The control unit can also control voice output to provide explanations to customers. Using speech synthesis technology, it can provide product descriptions and guidance to customers in a natural voice. Furthermore, the control unit can monitor the robot's sensor information in real time and generate actions to avoid obstacles. This ensures that the robot operates safely and efficiently. Based on instructions from the generation unit, the control unit can finely control the robot's actions and respond quickly and appropriately to customer requests. This allows the control unit to optimize the robot's actions as part of a system that autonomously responds according to the store's conditions, thereby improving the quality of customer service.

[0034] The robot is equipped with a water dispenser for dispensing water according to orders. The water dispenser can, for example, automatically dispense water when a customer places an order. The water dispenser can, for example, locate the position of a glass and dispense the appropriate amount of water. The water dispenser can also, for example, use a sensor to detect when a glass is full and add water as needed. This allows the robot to dispense water according to orders. Some or all of the above processes in the water dispenser may be performed using or without a generative AI. For example, to locate the position of a glass, the water dispenser can input image data acquired by a camera into a generative AI, which can then analyze the image data to locate the glass.

[0035] The robot is equipped with a serving unit for serving food. The serving unit, for example, carries the ordered food. The serving unit can, for example, locate the food and serve it in the appropriate place. The serving unit can also, for example, use sensors to detect the temperature of the food and serve it at the appropriate temperature. This allows the robot to serve food. Some or all of the above-described processes in the serving unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, in order to locate the food, the serving unit can input image data acquired by a camera into a generative AI, and the generative AI can analyze the image data to locate the food.

[0036] The robot includes a selection unit that selects actions in response to customer requests. The selection unit, for example, selects the dishes ordered by the customer. The selection unit can, for example, select an appropriate action based on the customer's request. The selection unit can also understand the customer's request using, for example, speech recognition technology. This allows the robot to select actions in response to customer requests. Some or all of the above-described processes in the selection unit may be performed using or without a generative AI. For example, the selection unit can input voice data into a generative AI to understand the customer's request, and the generative AI can analyze the voice data to understand the request.

[0037] The acquisition unit acquires images of the store using cameras. The acquisition unit can, for example, use multiple cameras to grasp the overall layout of the store. The acquisition unit can, for example, adjust the position and angle of the cameras to acquire optimal visual information. The acquisition unit can, for example, adjust the resolution and frame rate of the cameras to acquire clear images. In this way, the acquisition unit can acquire images of the store using cameras. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input image data acquired by the cameras into a generation AI, and the generation AI can analyze the image data to understand the situation inside the store.

[0038] The analysis unit analyzes the acquired images to determine the customer's location. The analysis unit can identify a specific customer, for example, using facial recognition technology. The analysis unit can track the customer's movements, for example, using motion detection technology. The analysis unit can also estimate the customer's attributes (age, gender, etc.), for example, using image analysis technology. As a result, the analysis unit can determine the customer's location by analyzing the acquired images. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input image data acquired by a camera into a generative AI, which can then analyze the image data to determine the customer's location.

[0039] The generation unit understands the customer's request and generates actions to respond appropriately. The generation unit understands the customer's order using, for example, natural language processing technology. The generation unit can understand the customer's request using, for example, speech recognition technology. The generation unit can also understand the customer's actions using, for example, gesture recognition technology. As a result, the generation unit can understand the customer's request and generate actions to respond appropriately. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input voice data into a generation AI, which can analyze the voice data to understand the request and generate appropriate actions.

[0040] The control unit controls the robot based on the generated actions. For example, the control unit can control the robot's movement. For example, the control unit can control the robot's gestures. For example, the control unit can control the robot's voice output to provide explanations to customers. In this way, the control unit can control the robot based on the generated actions. Some or all of the above-described processes in the control unit may be performed using a generative AI, or not using a generative AI. For example, the control unit can input the generated action data into a generative AI, which can then analyze the action data and control the robot.

[0041] The acquisition unit automatically adjusts the optimal camera settings according to the lighting conditions and time of day inside the store. For example, during bright daytime hours, the acquisition unit sets the camera exposure low to acquire a clear image. For example, during dark nighttime hours, the acquisition unit sets the camera exposure high to acquire a bright image. For example, if the lighting inside the store changes, the acquisition unit automatically adjusts the camera's white balance to acquire an image with natural colors. In this way, clear images can be acquired by automatically adjusting the optimal camera settings according to the lighting conditions and time of day inside the store. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input image data acquired by the camera into a generation AI, which can analyze the image data and adjust the optimal camera settings.

[0042] The acquisition unit dynamically changes the frequency of visual information acquisition according to the store's congestion level. For example, when the store is crowded, the acquisition unit sets a high frequency of visual information acquisition to enable a quick response. For example, when the store is empty, the acquisition unit sets a low frequency of visual information acquisition to improve the efficiency of the robot's operation. For example, when the store's congestion level changes, the acquisition unit dynamically adjusts the frequency of visual information acquisition to provide the optimal response. This allows for a quick response by dynamically changing the frequency of visual information acquisition according to the store's congestion level. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input image data acquired by a camera into a generation AI, which can analyze the image data to determine the congestion level and adjust the frequency of visual information acquisition.

[0043] The acquisition unit simultaneously acquires audio information from within the store and analyzes it in conjunction with visual information. For example, the acquisition unit acquires audio information from within the store and analyzes the content of customer conversations. For example, the acquisition unit acquires audio information from within the store and analyzes background noise and noise levels. For example, the acquisition unit acquires audio information from within the store and analyzes specific audio events (e.g., calls for orders). By simultaneously acquiring audio information from within the store and analyzing it in conjunction with visual information, more accurate analysis becomes possible. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input audio data acquired by a microphone into a generation AI, which can then analyze the audio data and integrate it with visual information.

[0044] The data acquisition unit also acquires environmental information such as temperature and humidity in the store and analyzes it in combination with visual information. For example, the data acquisition unit acquires temperature information in the store and analyzes customer comfort levels. For example, the data acquisition unit acquires humidity information in the store and analyzes customer comfort levels. For example, the data acquisition unit acquires temperature and humidity information in the store, analyzes it in combination with visual information, and proposes optimal environmental settings. As a result, by acquiring environmental information such as temperature and humidity in the store and analyzing it in combination with visual information, more appropriate environmental settings become possible. Some or all of the above processing in the data acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data acquisition unit can input data acquired by temperature sensors and humidity sensors into a generation AI, which can then analyze the data and combine it with visual information.

[0045] The analysis unit analyzes customer movements and facial expressions in real time based on acquired visual information. For example, the analysis unit analyzes customer movements and predicts the timing of orders. For example, the analysis unit analyzes customer facial expressions and evaluates satisfaction. For example, the analysis unit analyzes customer movements and facial expressions to determine the appropriate timing for serving customers. This allows for the determination of appropriate service timing by analyzing customer movements and facial expressions in real time based on acquired visual information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input image data acquired by a camera into a generation AI, which can analyze the image data to analyze customer movements and facial expressions in real time.

[0046] The analysis unit analyzes the store layout and furniture arrangement to calculate the optimal traffic flow. For example, the analysis unit analyzes the store layout and proposes an efficient traffic flow. For example, the analysis unit analyzes the furniture arrangement and identifies obstacles to traffic flow. For example, the analysis unit analyzes the store layout and furniture arrangement to calculate the optimal traffic flow. In this way, by analyzing the store layout and furniture arrangement and calculating the optimal traffic flow, an efficient traffic flow can be proposed. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input image data acquired by a camera into a generation AI, and the generation AI can analyze the image data to calculate the optimal traffic flow.

[0047] The analysis unit integrates the analysis results with audio information to perform more accurate analysis. For example, the analysis unit integrates the analysis results with audio information to analyze the content of customer conversations. For example, the analysis unit integrates the analysis results with audio information to analyze background noise and noise levels. For example, the analysis unit integrates the analysis results with audio information to analyze specific audio events (e.g., calls for orders). By integrating the analysis results with audio information, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input audio data acquired by a microphone into a generation AI, which can then analyze the audio data and integrate it with visual information.

[0048] The analysis unit displays the analysis results in combination with environmental information such as the temperature and humidity of the store. For example, the analysis unit displays the analysis results in combination with the temperature information of the store to evaluate the customer's comfort level. For example, the analysis unit displays the analysis results in combination with the humidity information of the store to evaluate the customer's comfort level. For example, the analysis unit displays the analysis results in combination with the temperature and humidity information of the store to suggest the optimal environmental settings. By displaying the analysis results in combination with environmental information such as the temperature and humidity of the store, it becomes possible to set up a more appropriate environment. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data acquired from temperature sensors and humidity sensors into a generation AI, which can analyze the data and combine it with visual information.

[0049] The generation unit generates multiple motion patterns based on the analysis results and selects the optimal one. For example, the generation unit generates an efficient motion pattern based on the analysis results. For example, the generation unit generates a motion pattern that increases customer satisfaction based on the analysis results. For example, the generation unit selects the optimal motion pattern based on the analysis results. This enables efficient operation by generating multiple motion patterns based on the analysis results and selecting the optimal one. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input image data acquired by a camera into a generation AI, which can analyze the image data to generate multiple motion patterns and select the optimal one.

[0050] The generation unit dynamically changes the priority of actions according to the congestion level in the store. For example, if the store is crowded, the generation unit prioritizes generating important actions. For example, if the store is not crowded, the generation unit prioritizes generating efficient actions. For example, if the congestion level in the store changes, the generation unit dynamically changes the priority of actions. This allows for a quick response by dynamically changing the priority of actions according to the congestion level in the store. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input image data acquired by a camera into the generation AI, which can analyze the image data to determine the congestion level and adjust the priority of actions.

[0051] The generation unit adds voice guidance to the generated actions and provides explanations to the customer. For example, the generation unit adds voice guidance to the generated actions and confirms the customer's order. For example, the generation unit adds voice guidance to the generated actions and explains the dishes to the customer. For example, the generation unit adds voice guidance to the generated actions and provides service information to the customer. In this way, explanations can be provided to the customer by adding voice guidance to the generated actions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input voice data into a generation AI, which can analyze the voice data to generate a voice guide and add it to the actions.

[0052] The generation unit adds visual signs to the actions it generates, providing visual guidance to the customer. For example, the generation unit adds visual signs to the actions it generates to confirm the customer's order. For example, the generation unit adds visual signs to the actions it generates to explain the dishes to the customer. For example, the generation unit adds visual signs to the actions it generates to provide service information to the customer. In this way, by adding visual signs to the actions it generates, it is possible to provide visual guidance to the customer. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input visual data into a generation AI, which can analyze the visual data to generate visual signs and add them to the actions.

[0053] The control unit monitors the robot's movements in real time and automatically corrects any abnormalities. For example, the control unit monitors the robot's movements in real time and automatically corrects any abnormalities. For example, the control unit monitors the robot's movements in real time and issues an alert if an abnormality occurs. For example, the control unit monitors the robot's movements in real time and manually corrects any abnormalities. This enables stable operation by monitoring the robot's movements in real time and automatically correcting any abnormalities. Some or all of the above-described processes in the control unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the control unit can input robot movement data into a generation AI, which can analyze the movement data to detect abnormalities and automatically correct them.

[0054] The control unit dynamically changes the robot's operating speed according to the conditions in the store. For example, if the store is crowded, the control unit slows down the robot's operating speed to ensure safety. For example, if the store is empty, the control unit speeds up the robot's operating speed to improve efficiency. For example, if the conditions in the store change, the control unit dynamically adjusts the robot's operating speed. This allows for efficient operation by dynamically changing the robot's operating speed according to the conditions in the store. Some or all of the above processing in the control unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the control unit can input image data acquired by a camera into a generative AI, which can analyze the image data to determine the conditions in the store and adjust the operating speed.

[0055] The control unit synchronizes the robot's movements with a voice guide to provide explanations to customers. For example, the control unit synchronizes the robot's movements with a voice guide to confirm orders with customers. For example, the control unit synchronizes the robot's movements with a voice guide to explain dishes to customers. For example, the control unit synchronizes the robot's movements with a voice guide to provide service information to customers. In this way, explanations can be provided to customers by synchronizing the robot's movements with a voice guide. Some or all of the above processing in the control unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the control unit can input voice data into a generative AI, which can analyze the voice data to generate a voice guide and synchronize it with the robot's movements.

[0056] The control unit links the robot's movements with visual signs to provide visual guidance to customers. For example, the control unit links the robot's movements with visual signs to confirm orders with customers. For example, the control unit links the robot's movements with visual signs to explain dishes to customers. For example, the control unit links the robot's movements with visual signs to provide service information to customers. In this way, by linking the robot's movements with visual signs, customers can be guided visually. Some or all of the above-described processes in the control unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the control unit can input visual data into a generative AI, which can analyze the visual data to generate visual signs and link them to the robot's movements.

[0057] The water dispensing unit selects the optimal water dispensing method according to the shape and material of the glass. For example, the water dispensing unit adjusts the angle and speed of water dispensing according to the shape of the glass. For example, the water dispensing unit adjusts the temperature and amount of water dispensing according to the material of the glass. For example, the water dispensing unit selects the optimal water dispensing method according to the shape and material of the glass. This enables efficient water dispensing by selecting the optimal water dispensing method according to the shape and material of the glass. Some or all of the above processes in the water dispensing unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the water dispensing unit can input image data acquired by a camera into a generation AI, which can analyze the image data to identify the shape and material of the glass and select the optimal water dispensing method.

[0058] The water dispensing unit measures the temperature and humidity of the glass when dispensing water and calculates the optimal amount of water to dispense. For example, the water dispensing unit measures the temperature of the glass and calculates the optimal amount of water to dispense. For example, the water dispensing unit measures the humidity of the glass and calculates the optimal amount of water to dispense. For example, the water dispensing unit measures the temperature and humidity of the glass and calculates the optimal amount of water to dispense. This allows for efficient water dispensing by measuring the temperature and humidity of the glass and calculating the optimal amount of water to dispense when dispensing water. Some or all of the above processing in the water dispensing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the water dispensing unit can input data acquired by a temperature sensor and a humidity sensor into a generation AI, which can then analyze the data and calculate the optimal amount of water to dispense.

[0059] The serving unit selects the optimal serving method according to the type and temperature of the dishes. The serving unit adjusts the order and method of serving according to the type of dishes, for example. The serving unit adjusts the timing and method of serving according to the temperature of the dishes, for example. The serving unit selects the optimal serving method according to the type and temperature of the dishes, for example. This enables efficient serving by selecting the optimal serving method according to the type and temperature of the dishes. Some or all of the above processes in the serving unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the serving unit can input image data acquired by a camera into a generation AI, which can analyze the image data to identify the type and temperature of the dishes and select the optimal serving method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analysis unit can analyze audio information within the store and understand the content of customer conversations. For example, the analysis unit can use speech recognition technology to transcribe customer conversations into text and analyze the content of the conversation. This allows the analysis unit to take appropriate action based on the content of the customer conversation. For instance, if a customer says, "I'd like another glass of water, please," the analysis unit can understand the request and take appropriate action. The analysis unit can also analyze the noise level within the store and adjust the robot's voice output if the noise level is high. This allows the analysis unit to analyze audio information within the store and take appropriate action.

[0062] The acquisition unit can acquire environmental information such as temperature and humidity within the store and provide it to the analysis unit. For example, the acquisition unit can acquire environmental information within the store using temperature sensors and humidity sensors. This allows the acquisition unit to provide the environmental information to the analysis unit, which can then take appropriate action based on that information. For instance, if the temperature inside the store is high, the analysis unit can adjust the robot's movements to ensure customer comfort. The acquisition unit can also monitor the air quality inside the store and prompt ventilation as needed. In this way, the acquisition unit acquires environmental information within the store and provides it to the analysis unit, enabling appropriate action to be taken.

[0063] The control unit can monitor the robot's movements in real time and automatically correct any abnormalities. For example, the control unit can monitor the robot's movements and issue an alert if an abnormality occurs. This allows the control unit to monitor the robot's movements in real time and respond quickly to any abnormalities. Alternatively, the control unit can also monitor the robot's movements and manually correct any abnormalities. This allows the control unit to monitor the robot's movements in real time and take appropriate action if an abnormality occurs.

[0064] The generation unit can generate multiple operation patterns in response to customer requests and select the optimal one. For example, the generation unit can generate efficient operation patterns based on customer requests. This allows the generation unit to select the optimal operation pattern in response to customer requests. Furthermore, the generation unit can generate multiple operation patterns based on customer requests and select the operation pattern that enhances customer satisfaction. This enables efficient operation by allowing the generation unit to select the optimal operation pattern in response to customer requests.

[0065] The analysis unit can analyze the store layout and furniture arrangement to calculate the optimal traffic flow. For example, the analysis unit can analyze the store layout and propose efficient traffic flow. In this way, the analysis unit can analyze the store layout and furniture arrangement to calculate the optimal traffic flow. Furthermore, the analysis unit can analyze the furniture arrangement to identify obstacles to traffic flow. Therefore, by analyzing the store layout and furniture arrangement and calculating the optimal traffic flow, the analysis unit can propose efficient traffic flow.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The acquisition unit acquires visual information to understand the situation inside the store. For example, the acquisition unit acquires images of the store using a camera. The acquisition unit can also use multiple cameras to understand, for example, the level of crowding inside the store and the movement of customers. Step 2: The analysis unit analyzes the visual information acquired by the acquisition unit. The analysis unit identifies the customer's location, for example, using image analysis technology. The analysis unit can also identify a specific customer, for example, using facial recognition technology. Step 3: The generation unit generates actions based on the information analyzed by the analysis unit. For example, the generation unit generates actions that understand a customer's request and respond appropriately. The generation unit can also understand the customer's order using, for example, natural language processing technology. Step 4: The control unit controls the robot based on the actions generated by the generation unit. For example, the control unit controls the robot's movement and gestures. The control unit can also, for example, control voice output to provide explanations to customers.

[0068] (Example of form 2) The system according to an embodiment of the present invention is a system for improving the flexibility and communication capabilities of robots used in restaurants. This system enables robots to think, converse, and serve customers on their own. The system grasps the situation in the restaurant, finds customers, takes orders, pours water according to the orders, and serves the food. This series of actions is optimized by generative AI, allowing the robot to respond autonomously. For example, to grasp the situation in the restaurant, the system acquires visual information, and the generative AI analyzes that information. For example, the system uses a camera to acquire images of the restaurant, and the generative AI analyzes the images to identify the location of customers. This allows the system to find customers. Next, the system approaches the customer and takes their order. The generative AI understands the customer's request and takes an appropriate response. For example, if a customer says, "I would like to order," the system approaches to take the order and asks about the order details. At this time, the generative AI analyzes the customer's words and understands the order details. Furthermore, the system pours water according to the order and serves the food. The generative AI optimizes the system's actions and serves the food efficiently. For example, if the system finds an empty glass of water, it automatically pours water. Furthermore, when serving ordered dishes, the generating AI calculates the optimal route and delivers the food efficiently. This allows the system to respond autonomously according to the situation in the restaurant. This improves the operational efficiency of restaurants and helps alleviate labor shortages. For example, by having the system handle not only serving and clearing but also customer service and order taking, the burden on staff is reduced. In addition, the system's autonomous response improves the quality of service in the restaurant and increases customer satisfaction. This improves the operational efficiency of restaurants and helps alleviate labor shortages.

[0069] The system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a control unit. The acquisition unit acquires visual information to understand the situation inside the store. The acquisition unit acquires images of the store using, for example, a camera. The acquisition unit can also use multiple cameras to understand, for example, the level of crowding and customer movements inside the store. The analysis unit analyzes the visual information acquired by the acquisition unit. The analysis unit identifies the location of customers using, for example, image analysis technology. The analysis unit can also identify specific customers using, for example, facial recognition technology. The generation unit generates actions based on the information analyzed by the analysis unit. The generation unit generates actions to understand customer requests and respond appropriately. The generation unit can also understand customer orders using, for example, natural language processing technology. The control unit controls the robot based on the actions generated by the generation unit. The control unit controls the robot's movement and gestures, for example. The control unit can also control voice output to provide explanations to customers. As a result, the system according to this embodiment can autonomously respond according to the situation inside the store.

[0070] The acquisition unit acquires visual information to understand the situation inside the store. For example, the acquisition unit acquires images of the store using cameras. Specifically, it uses multiple high-resolution cameras installed on the ceiling and walls of the store to acquire wide-area visual information in real time. These cameras are equipped with wide-angle lenses and can cover every corner of the store. The cameras also have infrared capabilities to acquire clear images even in low-light environments. The acquisition unit can also use multiple cameras to understand the level of congestion and customer movement inside the store. This allows the acquisition unit to understand the congestion and customer flow in each area of ​​the store in detail. Furthermore, the acquisition unit transmits the camera footage to a central database in real time, making it accessible to the analysis unit. This allows the acquisition unit to accurately and quickly understand the situation inside the store and support the efficient operation of the entire system.

[0071] The analysis unit analyzes the visual information acquired by the acquisition unit. For example, the analysis unit uses image analysis technology to identify the location of customers. Specifically, it uses an image recognition algorithm based on deep learning to detect the posture and movement of customers from camera footage and identify the location of each customer. The analysis unit can also identify specific customers using facial recognition technology. Facial recognition technology can identify specific customers by comparing them with a pre-registered facial database and track their movements. Furthermore, the analysis unit can analyze customer behavior patterns and extract information such as which areas customers stay in for a long time and which products they show interest in. This allows the analysis unit to gain a detailed understanding of the store situation and provide basic data for taking appropriate action based on customer behavior. The analysis unit transmits these analysis results to the generation unit in real time, enabling a rapid response.

[0072] The generation unit generates actions based on the information analyzed by the analysis unit. For example, the generation unit generates actions to understand a customer's request and respond appropriately. Specifically, it uses natural language processing technology to analyze the customer's statements and understand their intent. For example, if a customer says, "Please tell me about this product," the generation unit understands the request and generates actions to provide an appropriate explanation. The generation unit can also use natural language processing technology to understand the customer's order. It analyzes the order, checks the necessary product information and inventory status, and generates instructions to take appropriate action. Furthermore, the generation unit can generate actions to provide individualized responses based on the customer's behavior patterns and location information provided by the analysis unit. This allows the generation unit to respond flexibly to customer needs and improve customer satisfaction. The generation unit transmits the generated actions to the control unit and provides instructions for executing the actual actions.

[0073] The control unit controls the robot based on the actions generated by the generation unit. For example, the control unit controls the robot's movement and gestures. Specifically, it controls the robot's joints and motors to move to a designated position or perform specific gestures. The control unit can also control voice output to provide explanations to customers. Using speech synthesis technology, it can provide product descriptions and guidance to customers in a natural voice. Furthermore, the control unit can monitor the robot's sensor information in real time and generate actions to avoid obstacles. This ensures that the robot operates safely and efficiently. Based on instructions from the generation unit, the control unit can finely control the robot's actions and respond quickly and appropriately to customer requests. This allows the control unit to optimize the robot's actions as part of a system that autonomously responds according to the store's conditions, thereby improving the quality of customer service.

[0074] The robot is equipped with a water dispenser for dispensing water according to orders. The water dispenser can, for example, automatically dispense water when a customer places an order. The water dispenser can, for example, locate the position of a glass and dispense the appropriate amount of water. The water dispenser can also, for example, use a sensor to detect when a glass is full and add water as needed. This allows the robot to dispense water according to orders. Some or all of the above processes in the water dispenser may be performed using or without a generative AI. For example, to locate the position of a glass, the water dispenser can input image data acquired by a camera into a generative AI, which can then analyze the image data to locate the glass.

[0075] The robot is equipped with a serving unit for serving food. The serving unit, for example, carries the ordered food. The serving unit can, for example, locate the food and serve it in the appropriate place. The serving unit can also, for example, use sensors to detect the temperature of the food and serve it at the appropriate temperature. This allows the robot to serve food. Some or all of the above-described processes in the serving unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, in order to locate the food, the serving unit can input image data acquired by a camera into a generative AI, and the generative AI can analyze the image data to locate the food.

[0076] The robot includes a selection unit that selects actions in response to customer requests. The selection unit, for example, selects the dishes ordered by the customer. The selection unit can, for example, select an appropriate action based on the customer's request. The selection unit can also understand the customer's request using, for example, speech recognition technology. This allows the robot to select actions in response to customer requests. Some or all of the above-described processes in the selection unit may be performed using or without a generative AI. For example, the selection unit can input voice data into a generative AI to understand the customer's request, and the generative AI can analyze the voice data to understand the request.

[0077] The acquisition unit acquires images of the store using cameras. The acquisition unit can, for example, use multiple cameras to grasp the overall layout of the store. The acquisition unit can, for example, adjust the position and angle of the cameras to acquire optimal visual information. The acquisition unit can, for example, adjust the resolution and frame rate of the cameras to acquire clear images. In this way, the acquisition unit can acquire images of the store using cameras. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input image data acquired by the cameras into a generation AI, and the generation AI can analyze the image data to understand the situation inside the store.

[0078] The analysis unit analyzes the acquired images to determine the customer's location. The analysis unit can identify a specific customer, for example, using facial recognition technology. The analysis unit can track the customer's movements, for example, using motion detection technology. The analysis unit can also estimate the customer's attributes (age, gender, etc.), for example, using image analysis technology. As a result, the analysis unit can determine the customer's location by analyzing the acquired images. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input image data acquired by a camera into a generative AI, which can then analyze the image data to determine the customer's location.

[0079] The generation unit understands the customer's request and generates actions to respond appropriately. The generation unit understands the customer's order using, for example, natural language processing technology. The generation unit can understand the customer's request using, for example, speech recognition technology. The generation unit can also understand the customer's actions using, for example, gesture recognition technology. As a result, the generation unit can understand the customer's request and generate actions to respond appropriately. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input voice data into a generation AI, which can analyze the voice data to understand the request and generate appropriate actions.

[0080] The control unit controls the robot based on the generated actions. For example, the control unit can control the robot's movement. For example, the control unit can control the robot's gestures. For example, the control unit can control the robot's voice output to provide explanations to customers. In this way, the control unit can control the robot based on the generated actions. Some or all of the above-described processes in the control unit may be performed using a generative AI, or not using a generative AI. For example, the control unit can input the generated action data into a generative AI, which can then analyze the action data and control the robot.

[0081] The acquisition unit estimates the user's emotions and adjusts the timing of visual information acquisition based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit sets the frequency of visual information acquisition to a low level to make the robot's movements smoother. For example, if the user is in a hurry, the acquisition unit sets the frequency of visual information acquisition to a high level to enable a quick response. For example, if the user is feeling anxious, the acquisition unit sets the frequency of visual information acquisition to a medium level to provide appropriate support. In this way, more appropriate information can be acquired by adjusting the timing of visual information acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using the generative AI or not. For example, the acquisition unit can input image data acquired by the camera into the generative AI, which can analyze the image data to estimate the user's emotions and adjust the timing of visual information acquisition.

[0082] The acquisition unit automatically adjusts the optimal camera settings according to the lighting conditions and time of day inside the store. For example, during bright daytime hours, the acquisition unit sets the camera exposure low to acquire a clear image. For example, during dark nighttime hours, the acquisition unit sets the camera exposure high to acquire a bright image. For example, if the lighting inside the store changes, the acquisition unit automatically adjusts the camera's white balance to acquire an image with natural colors. In this way, clear images can be acquired by automatically adjusting the optimal camera settings according to the lighting conditions and time of day inside the store. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input image data acquired by the camera into a generation AI, which can analyze the image data and adjust the optimal camera settings.

[0083] The acquisition unit dynamically changes the frequency of visual information acquisition according to the store's congestion level. For example, when the store is crowded, the acquisition unit sets a high frequency of visual information acquisition to enable a quick response. For example, when the store is empty, the acquisition unit sets a low frequency of visual information acquisition to improve the efficiency of the robot's operation. For example, when the store's congestion level changes, the acquisition unit dynamically adjusts the frequency of visual information acquisition to provide the optimal response. This allows for a quick response by dynamically changing the frequency of visual information acquisition according to the store's congestion level. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input image data acquired by a camera into a generation AI, which can analyze the image data to determine the congestion level and adjust the frequency of visual information acquisition.

[0084] The acquisition unit estimates the user's emotions and determines the priority of visual information to acquire based on the estimated emotions. For example, if the user is relaxed, the acquisition unit prioritizes acquiring the overall atmosphere of the store. If the user is in a hurry, the acquisition unit prioritizes acquiring the movements and facial expressions of other customers. If the user is feeling anxious, the acquisition unit prioritizes acquiring specific areas or people. By prioritizing visual information according to the user's emotions, more appropriate information can be acquired. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using or without a generative AI. For example, the acquisition unit can input image data acquired by a camera into a generative AI, which can analyze the image data to estimate the user's emotions and determine the priority of visual information.

[0085] The acquisition unit simultaneously acquires audio information from within the store and analyzes it in conjunction with visual information. For example, the acquisition unit acquires audio information from within the store and analyzes the content of customer conversations. For example, the acquisition unit acquires audio information from within the store and analyzes background noise and noise levels. For example, the acquisition unit acquires audio information from within the store and analyzes specific audio events (e.g., calls for orders). By simultaneously acquiring audio information from within the store and analyzing it in conjunction with visual information, more accurate analysis becomes possible. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input audio data acquired by a microphone into a generation AI, which can then analyze the audio data and integrate it with visual information.

[0086] The data acquisition unit also acquires environmental information such as temperature and humidity in the store and analyzes it in combination with visual information. For example, the data acquisition unit acquires temperature information in the store and analyzes customer comfort levels. For example, the data acquisition unit acquires humidity information in the store and analyzes customer comfort levels. For example, the data acquisition unit acquires temperature and humidity information in the store, analyzes it in combination with visual information, and proposes optimal environmental settings. As a result, by acquiring environmental information such as temperature and humidity in the store and analyzing it in combination with visual information, more appropriate environmental settings become possible. Some or all of the above processing in the data acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data acquisition unit can input data acquired by temperature sensors and humidity sensors into a generation AI, which can then analyze the data and combine it with visual information.

[0087] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit displays detailed analysis results. If the user is in a hurry, the analysis unit displays concise analysis results that get straight to the point. If the user is feeling anxious, the analysis unit displays reassuring analysis results. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input image data acquired by a camera into a generative AI, which can analyze the image data to estimate the user's emotions and adjust the display method of the analysis results.

[0088] The analysis unit analyzes customer movements and facial expressions in real time based on acquired visual information. For example, the analysis unit analyzes customer movements and predicts the timing of orders. For example, the analysis unit analyzes customer facial expressions and evaluates satisfaction. For example, the analysis unit analyzes customer movements and facial expressions to determine the appropriate timing for serving customers. This allows for the determination of appropriate service timing by analyzing customer movements and facial expressions in real time based on acquired visual information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input image data acquired by a camera into a generation AI, which can analyze the image data to analyze customer movements and facial expressions in real time.

[0089] The analysis unit analyzes the store layout and furniture arrangement to calculate the optimal traffic flow. For example, the analysis unit analyzes the store layout and proposes an efficient traffic flow. For example, the analysis unit analyzes the furniture arrangement and identifies obstacles to traffic flow. For example, the analysis unit analyzes the store layout and furniture arrangement to calculate the optimal traffic flow. In this way, by analyzing the store layout and furniture arrangement and calculating the optimal traffic flow, an efficient traffic flow can be proposed. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input image data acquired by a camera into a generation AI, and the generation AI can analyze the image data to calculate the optimal traffic flow.

[0090] The analysis unit estimates the user's emotions and determines the priority of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit prioritizes displaying detailed analysis results. For example, if the user is in a hurry, the analysis unit prioritizes displaying concise analysis results. For example, if the user is feeling anxious, the analysis unit prioritizes displaying reassuring analysis results. By prioritizing the analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input image data acquired by a camera into a generative AI, which can analyze the image data to estimate the user's emotions and determine the priority of the analysis results.

[0091] The analysis unit integrates the analysis results with audio information to perform more accurate analysis. For example, the analysis unit integrates the analysis results with audio information to analyze the content of customer conversations. For example, the analysis unit integrates the analysis results with audio information to analyze background noise and noise levels. For example, the analysis unit integrates the analysis results with audio information to analyze specific audio events (e.g., calls for orders). By integrating the analysis results with audio information, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input audio data acquired by a microphone into a generation AI, which can then analyze the audio data and integrate it with visual information.

[0092] The analysis unit displays the analysis results in combination with environmental information such as the temperature and humidity of the store. For example, the analysis unit displays the analysis results in combination with the temperature information of the store to evaluate the customer's comfort level. For example, the analysis unit displays the analysis results in combination with the humidity information of the store to evaluate the customer's comfort level. For example, the analysis unit displays the analysis results in combination with the temperature and humidity information of the store to suggest the optimal environmental settings. By displaying the analysis results in combination with environmental information such as the temperature and humidity of the store, it becomes possible to set up a more appropriate environment. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data acquired from temperature sensors and humidity sensors into a generation AI, which can analyze the data and combine it with visual information.

[0093] The generation unit estimates the user's emotions and adjusts the way it expresses the generated actions based on the estimated emotions. For example, if the user is relaxed, the generation unit generates relaxed actions. For example, if the user is in a hurry, the generation unit generates quick actions. For example, if the user is feeling anxious, the generation unit generates actions that provide a sense of security. By adjusting the way it expresses actions according to the user's emotions, it is possible to generate more appropriate actions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input image data acquired by a camera into a generation AI, which can analyze the image data to estimate the user's emotions and adjust the way it expresses the actions.

[0094] The generation unit generates multiple motion patterns based on the analysis results and selects the optimal one. For example, the generation unit generates an efficient motion pattern based on the analysis results. For example, the generation unit generates a motion pattern that increases customer satisfaction based on the analysis results. For example, the generation unit selects the optimal motion pattern based on the analysis results. This enables efficient operation by generating multiple motion patterns based on the analysis results and selecting the optimal one. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input image data acquired by a camera into a generation AI, which can analyze the image data to generate multiple motion patterns and select the optimal one.

[0095] The generation unit dynamically changes the priority of actions according to the congestion level in the store. For example, if the store is crowded, the generation unit prioritizes generating important actions. For example, if the store is not crowded, the generation unit prioritizes generating efficient actions. For example, if the congestion level in the store changes, the generation unit dynamically changes the priority of actions. This allows for a quick response by dynamically changing the priority of actions according to the congestion level in the store. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input image data acquired by a camera into the generation AI, which can analyze the image data to determine the congestion level and adjust the priority of actions.

[0096] The generation unit estimates the user's emotions and determines the priority of actions to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit prioritizes generating important actions. For example, if the user is in a hurry, the generation unit prioritizes generating quick actions. For example, if the user is feeling anxious, the generation unit prioritizes generating actions that provide a sense of security. By prioritizing actions according to the user's emotions, more appropriate actions can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input image data acquired by a camera into a generation AI, which can analyze the image data to estimate the user's emotions and determine the priority of actions.

[0097] The generation unit adds voice guidance to the generated actions and provides explanations to the customer. For example, the generation unit adds voice guidance to the generated actions and confirms the customer's order. For example, the generation unit adds voice guidance to the generated actions and explains the dishes to the customer. For example, the generation unit adds voice guidance to the generated actions and provides service information to the customer. In this way, explanations can be provided to the customer by adding voice guidance to the generated actions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input voice data into a generation AI, which can analyze the voice data to generate a voice guide and add it to the actions.

[0098] The generation unit adds visual signs to the actions it generates, providing visual guidance to the customer. For example, the generation unit adds visual signs to the actions it generates to confirm the customer's order. For example, the generation unit adds visual signs to the actions it generates to explain the dishes to the customer. For example, the generation unit adds visual signs to the actions it generates to provide service information to the customer. In this way, by adding visual signs to the actions it generates, it is possible to provide visual guidance to the customer. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input visual data into a generation AI, which can analyze the visual data to generate visual signs and add them to the actions.

[0099] The control unit estimates the user's emotions and adjusts the control method based on the estimated emotions. For example, if the user is relaxed, the control unit controls slow movements. For example, if the user is in a hurry, the control unit controls rapid movements. For example, if the user is feeling anxious, the control unit controls movements that provide a sense of security. By adjusting the control method according to the user's emotions, more appropriate control becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the control unit may be performed using a generative AI or not. For example, the control unit can input image data acquired by a camera into a generative AI, which can analyze the image data to estimate the user's emotions and adjust the control method.

[0100] The control unit monitors the robot's movements in real time and automatically corrects any abnormalities. For example, the control unit monitors the robot's movements in real time and automatically corrects any abnormalities. For example, the control unit monitors the robot's movements in real time and issues an alert if an abnormality occurs. For example, the control unit monitors the robot's movements in real time and manually corrects any abnormalities. This enables stable operation by monitoring the robot's movements in real time and automatically correcting any abnormalities. Some or all of the above-described processes in the control unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the control unit can input robot movement data into a generation AI, which can analyze the movement data to detect abnormalities and automatically correct them.

[0101] The control unit dynamically changes the robot's operating speed according to the conditions in the store. For example, if the store is crowded, the control unit slows down the robot's operating speed to ensure safety. For example, if the store is empty, the control unit speeds up the robot's operating speed to improve efficiency. For example, if the conditions in the store change, the control unit dynamically adjusts the robot's operating speed. This allows for efficient operation by dynamically changing the robot's operating speed according to the conditions in the store. Some or all of the above processing in the control unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the control unit can input image data acquired by a camera into a generative AI, which can analyze the image data to determine the conditions in the store and adjust the operating speed.

[0102] The control unit estimates the user's emotions and determines control priorities based on the estimated emotions. For example, if the user is relaxed, the control unit prioritizes important actions. For example, if the user is in a hurry, the control unit prioritizes quick actions. For example, if the user is feeling anxious, the control unit prioritizes actions that provide a sense of security. This allows for more appropriate control by determining control priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the control unit may be performed using or without a generative AI. For example, the control unit can input image data acquired by a camera into a generative AI, which can analyze the image data to estimate the user's emotions and determine control priorities.

[0103] The control unit synchronizes the robot's movements with a voice guide to provide explanations to customers. For example, the control unit synchronizes the robot's movements with a voice guide to confirm orders with customers. For example, the control unit synchronizes the robot's movements with a voice guide to explain dishes to customers. For example, the control unit synchronizes the robot's movements with a voice guide to provide service information to customers. In this way, explanations can be provided to customers by synchronizing the robot's movements with a voice guide. Some or all of the above processing in the control unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the control unit can input voice data into a generative AI, which can analyze the voice data to generate a voice guide and synchronize it with the robot's movements.

[0104] The control unit links the robot's movements with visual signs to provide visual guidance to customers. For example, the control unit links the robot's movements with visual signs to confirm orders with customers. For example, the control unit links the robot's movements with visual signs to explain dishes to customers. For example, the control unit links the robot's movements with visual signs to provide service information to customers. In this way, by linking the robot's movements with visual signs, customers can be guided visually. Some or all of the above-described processes in the control unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the control unit can input visual data into a generative AI, which can analyze the visual data to generate visual signs and link them to the robot's movements.

[0105] The water dispensing unit estimates the user's emotions and adjusts the timing of water dispensing based on the estimated emotions. For example, if the user is relaxed, the water dispensing unit will dispense water at a leisurely pace. If the user is in a hurry, the water dispensing unit will dispense water quickly. If the user is feeling anxious, the water dispensing unit will dispense water at a time that provides reassurance. By adjusting the timing of water dispensing according to the user's emotions, water can be dispensed at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the water dispensing unit may be performed using generative AI or not. For example, the water dispensing unit can input image data acquired by a camera into a generative AI, which can analyze the image data to estimate the user's emotions and adjust the timing of water dispensing.

[0106] The water dispensing unit selects the optimal water dispensing method according to the shape and material of the glass. For example, the water dispensing unit adjusts the angle and speed of water dispensing according to the shape of the glass. For example, the water dispensing unit adjusts the temperature and amount of water dispensing according to the material of the glass. For example, the water dispensing unit selects the optimal water dispensing method according to the shape and material of the glass. This enables efficient water dispensing by selecting the optimal water dispensing method according to the shape and material of the glass. Some or all of the above processes in the water dispensing unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the water dispensing unit can input image data acquired by a camera into a generation AI, which can analyze the image data to identify the shape and material of the glass and select the optimal water dispensing method.

[0107] The water dispensing unit estimates the user's emotions and determines the priority of water dispensing based on the estimated emotions. For example, if the user is relaxed, the water dispensing unit prioritizes important water dispensing. If the user is in a hurry, the water dispensing unit prioritizes quick water dispensing. If the user is feeling anxious, the water dispensing unit prioritizes reassuring water dispensing. By determining the priority of water dispensing according to the user's emotions, more appropriate water dispensing becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the water dispensing unit may be performed using generative AI or not. For example, the water dispensing unit can input image data acquired by a camera into a generative AI, which can analyze the image data to estimate the user's emotions and determine the priority of water dispensing.

[0108] The water dispensing unit measures the temperature and humidity of the glass when dispensing water and calculates the optimal amount of water to dispense. For example, the water dispensing unit measures the temperature of the glass and calculates the optimal amount of water to dispense. For example, the water dispensing unit measures the humidity of the glass and calculates the optimal amount of water to dispense. For example, the water dispensing unit measures the temperature and humidity of the glass and calculates the optimal amount of water to dispense. This allows for efficient water dispensing by measuring the temperature and humidity of the glass and calculating the optimal amount of water to dispense when dispensing water. Some or all of the above processing in the water dispensing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the water dispensing unit can input data acquired by a temperature sensor and a humidity sensor into a generation AI, which can then analyze the data and calculate the optimal amount of water to dispense.

[0109] The serving unit estimates the user's emotions and adjusts the timing of serving based on the estimated emotions. For example, if the user is relaxed, the serving unit will serve at a leisurely pace. If the user is in a hurry, the serving unit will serve quickly. If the user is feeling anxious, the serving unit will serve at a time that provides reassurance. By adjusting the timing of serving according to the user's emotions, the serving unit can serve at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the serving unit may be performed using generative AI or not. For example, the serving unit can input image data acquired by a camera into a generative AI, which can analyze the image data to estimate the user's emotions and adjust the timing of serving.

[0110] The serving unit selects the optimal serving method according to the type and temperature of the dishes. The serving unit adjusts the order and method of serving according to the type of dishes, for example. The serving unit adjusts the timing and method of serving according to the temperature of the dishes, for example. The serving unit selects the optimal serving method according to the type and temperature of the dishes, for example. This enables efficient serving by selecting the optimal serving method according to the type and temperature of the dishes. Some or all of the above processes in the serving unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the serving unit can input image data acquired by a camera into a generation AI, which can analyze the image data to identify the type and temperature of the dishes and select the optimal serving method.

[0111] The serving unit estimates the user's emotions and determines the priority of serving based on the estimated emotions. For example, if the user is relaxed, the serving unit will prioritize important servings. If the user is in a hurry, the serving unit will prioritize quick servings. If the user is feeling anxious, the serving unit will prioritize servings that provide reassurance. By determining the priority of serving according to the user's emotions, more appropriate serving becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the serving unit may be performed using generative AI or not. For example, the serving unit can input image data acquired by a camera into a generative AI, which can analyze the image data to estimate the user's emotions and determine the priority of serving.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The analysis unit can analyze audio information within the store and understand the content of customer conversations. For example, the analysis unit can use speech recognition technology to transcribe customer conversations into text and analyze the content of the conversation. This allows the analysis unit to take appropriate action based on the content of the customer conversation. For instance, if a customer says, "I'd like another glass of water, please," the analysis unit can understand the request and take appropriate action. The analysis unit can also analyze the noise level within the store and adjust the robot's voice output if the noise level is high. This allows the analysis unit to analyze audio information within the store and take appropriate action.

[0114] The acquisition unit can acquire environmental information such as temperature and humidity within the store and provide it to the analysis unit. For example, the acquisition unit can acquire environmental information within the store using temperature sensors and humidity sensors. This allows the acquisition unit to provide the environmental information to the analysis unit, which can then take appropriate action based on that information. For instance, if the temperature inside the store is high, the analysis unit can adjust the robot's movements to ensure customer comfort. The acquisition unit can also monitor the air quality inside the store and prompt ventilation as needed. In this way, the acquisition unit acquires environmental information within the store and provides it to the analysis unit, enabling appropriate action to be taken.

[0115] The control unit can monitor the robot's movements in real time and automatically correct any abnormalities. For example, the control unit can monitor the robot's movements and issue an alert if an abnormality occurs. This allows the control unit to monitor the robot's movements in real time and respond quickly to any abnormalities. Alternatively, the control unit can also monitor the robot's movements and manually correct any abnormalities. This allows the control unit to monitor the robot's movements in real time and take appropriate action if an abnormality occurs.

[0116] The generation unit can generate multiple operation patterns in response to customer requests and select the optimal one. For example, the generation unit can generate efficient operation patterns based on customer requests. This allows the generation unit to select the optimal operation pattern in response to customer requests. Furthermore, the generation unit can generate multiple operation patterns based on customer requests and select the operation pattern that enhances customer satisfaction. This enables efficient operation by allowing the generation unit to select the optimal operation pattern in response to customer requests.

[0117] The analysis unit can analyze the store layout and furniture arrangement to calculate the optimal traffic flow. For example, the analysis unit can analyze the store layout and propose efficient traffic flow. In this way, the analysis unit can analyze the store layout and furniture arrangement to calculate the optimal traffic flow. Furthermore, the analysis unit can analyze the furniture arrangement to identify obstacles to traffic flow. Therefore, by analyzing the store layout and furniture arrangement and calculating the optimal traffic flow, the analysis unit can propose efficient traffic flow.

[0118] The acquisition unit can estimate the user's emotions and adjust the timing of visual information acquisition based on the estimated emotions. For example, if the user is relaxed, the acquisition unit can set a lower frequency of visual information acquisition, allowing the robot to operate more smoothly. This allows the acquisition unit to adjust the timing of visual information acquisition according to the user's emotions. Conversely, if the user is in a hurry, the acquisition unit can set a higher frequency of visual information acquisition, enabling a quicker response. By adjusting the timing of visual information acquisition according to the user's emotions, the acquisition unit can acquire more appropriate information.

[0119] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. This allows the analysis unit to adjust how the analysis results are displayed according to the user's emotions. Furthermore, if the user is in a hurry, the analysis unit can display concise analysis results that focus on the key points. By adjusting how the analysis results are displayed according to the user's emotions, the analysis unit can provide more appropriate information.

[0120] The generation unit can estimate the user's emotions and adjust the way it expresses the generated actions based on those emotions. For example, if the user is relaxed, the generation unit can generate relaxed actions. This allows the generation unit to adjust the way it expresses actions according to the user's emotions. Conversely, if the user is in a hurry, the generation unit can generate quick actions. This allows the generation unit to generate more appropriate actions by adjusting the way it expresses actions according to the user's emotions.

[0121] The control unit can estimate the user's emotions and adjust the control method based on those emotions. For example, if the user is relaxed, the control unit can control the device with slow, deliberate movements. This allows the control unit to adjust the control method according to the user's emotions. Conversely, if the user is in a hurry, the control unit can control the device with rapid movements. By adjusting the control method according to the user's emotions, the control unit can provide more appropriate control.

[0122] The water dispensing unit can estimate the user's emotions and adjust the timing of water dispensing based on those emotions. For example, if the user is relaxed, the unit can dispense water at a leisurely pace. This allows the unit to adjust the timing of water dispensing according to the user's emotions. Conversely, if the user is in a hurry, the unit can dispense water quickly. By adjusting the timing of water dispensing according to the user's emotions, the unit can dispense water at a more appropriate time.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The acquisition unit acquires visual information to understand the situation inside the store. For example, the acquisition unit acquires images of the store using a camera. The acquisition unit can also use multiple cameras to understand, for example, the level of crowding inside the store and the movement of customers. Step 2: The analysis unit analyzes the visual information acquired by the acquisition unit. The analysis unit identifies the customer's location, for example, using image analysis technology. The analysis unit can also identify a specific customer, for example, using facial recognition technology. Step 3: The generation unit generates actions based on the information analyzed by the analysis unit. For example, the generation unit generates actions that understand a customer's request and respond appropriately. The generation unit can also understand the customer's order using, for example, natural language processing technology. Step 4: The control unit controls the robot based on the actions generated by the generation unit. For example, the control unit controls the robot's movement and gestures. The control unit can also, for example, control voice output to provide explanations to customers.

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0128] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, control unit, water dispensing unit, serving unit, and selection unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires images of the store using the camera 42 of the smart device 14 and analyzes them by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired image data to identify the customer's location. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates actions based on the analyzed information. The control unit is implemented, for example, by the control unit 46A of the smart device 14 and controls the robot based on the generated actions. The water dispensing unit is implemented, for example, by the control unit 46A of the smart device 14 and identifies the position of the glass and pours the appropriate amount of water. The serving unit is implemented, for example, by the control unit 46A of the smart device 14 and identifies the position of the food and serves it in the appropriate place. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which understands the customer's request and selects the appropriate operation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, control unit, water dispensing unit, serving unit, and selection unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires images of the store using the camera 42 of the smart glasses 214 and analyzes them by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired image data to identify the customer's location. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates actions based on the analyzed information. The control unit is implemented, for example, by the control unit 46A of the smart glasses 214 and controls the robot based on the generated actions. The water dispensing unit is implemented, for example, by the control unit 46A of the smart glasses 214 and identifies the position of the glass and pours the appropriate amount of water. The serving unit is implemented, for example, by the control unit 46A of the smart glasses 214 and identifies the position of the food and serves it in the appropriate place. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which understands the customer's request and selects the appropriate operation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, control unit, water dispensing unit, serving unit, and selection unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires images of the store using the camera 42 of the headset terminal 314 and analyzes them by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the acquired image data to identify the customer's location. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates actions based on the analyzed information. The control unit is implemented by, for example, the control unit 46A of the headset terminal 314 and controls the robot based on the generated actions. The water dispensing unit is implemented by, for example, the control unit 46A of the headset terminal 314 and identifies the position of the glass and pours the appropriate amount of water. The serving unit is implemented by, for example, the control unit 46A of the headset terminal 314 and identifies the position of the food and serves it to the appropriate location. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which understands the customer's request and selects the appropriate operation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, control unit, water dispensing unit, serving unit, and selection unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires images of the store using the camera 42 of the robot 414 and analyzes them by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired image data to identify the customer's location. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates actions based on the analyzed information. The control unit is implemented, for example, by the control unit 46A of the robot 414 and controls the robot based on the generated actions. The water dispensing unit is implemented, for example, by the control unit 46A of the robot 414 and identifies the position of the glass and pours the appropriate amount of water. The serving unit is implemented, for example, by the control unit 46A of the robot 414 and identifies the position of the food and serves it in the appropriate place. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which understands the customer's request and selects the appropriate operation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) An acquisition unit that acquires visual information to understand the situation inside the store, An analysis unit analyzes the visual information acquired by the acquisition unit, A generation unit that generates an operation based on the information analyzed by the analysis unit, The system includes a control unit that controls the robot based on the movements generated by the generation unit. A system characterized by the following features. (Note 2) The robot is equipped with a water dispenser for dispensing water according to orders. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a serving unit for robots to deliver meals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The robot has a selection unit that selects actions according to the customer's request. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, Use a camera to capture images of the store interior. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The acquired images are analyzed to determine the customer's location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is It understands customer requests and generates actions to respond appropriately. The system described in Appendix 1, characterized by the features described herein. (Note 8) The control unit, Control the robot based on the generated movements. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates the user's emotions and adjusts the timing of visual information acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The camera settings are automatically adjusted to the optimal level based on the store's lighting conditions and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, The frequency of acquiring visual information is dynamically changed according to the store's congestion level. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, It estimates the user's emotions and determines the priority of visual information to retrieve based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, The system simultaneously acquires audio information from within the store and analyzes it in conjunction with visual information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, Environmental information such as temperature and humidity inside the store is also acquired and analyzed in combination with visual information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Based on the acquired visual information, the system analyzes customer movements and facial expressions in real time. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We analyze the store layout and furniture placement to calculate the optimal traffic flow. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, The analysis results are integrated with audio information to perform a more accurate analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, The analysis results are displayed in combination with environmental information such as the temperature and humidity inside the store. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the way actions are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is Based on the analysis results, multiple operation patterns are generated, and the optimal one is selected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is The priority of actions is dynamically changed according to the store's congestion level. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the user's emotions and determines the priority of actions to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is Add audio guidance to the generated actions and provide explanations to customers. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is Add visual signs to the actions being generated to visually guide customers. The system described in Appendix 1, characterized by the features described herein. (Note 27) The control unit, It estimates the user's emotions and adjusts the control method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The control unit, The robot's movements are monitored in real time, and any abnormalities are automatically corrected. The system described in Appendix 1, characterized by the features described herein. (Note 29) The control unit, The robot's operating speed is dynamically changed according to the conditions inside the store. The system described in Appendix 1, characterized by the features described herein. (Note 30) The control unit, It estimates the user's emotions and determines the priority of control based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The control unit, The robot's movements are synchronized with a voice guide to provide explanations to customers. The system described in Appendix 1, characterized by the features described herein. (Note 32) The control unit, The robot's movements are linked to visual signs to provide customers with visual guidance. The system described in Appendix 1, characterized by the features described herein. (Note 33) The water injection section is, It estimates the user's emotions and adjusts the timing of water dispensing based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The water injection section is, Choose the optimal pouring method depending on the shape and material of the glass. The system described in Appendix 2, characterized by the features described herein. (Note 35) The water injection section is, It estimates the user's emotions and determines the priority of water dispensing based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The water injection section is, The temperature and humidity of the glass are measured during the pouring process, and the optimal amount of water is calculated. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned serving section is, The system estimates the user's emotions and adjusts the timing of meal delivery based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned serving section is, Choose the optimal serving method according to the type and temperature of the dish. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned serving section is, The system estimates the user's emotions and determines the priority of serving food based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires visual information to understand the situation inside the store, An analysis unit analyzes the visual information acquired by the acquisition unit, A generation unit that generates an operation based on the information analyzed by the analysis unit, The system includes a control unit that controls the robot based on the movements generated by the generation unit. A system characterized by the following features.

2. The robot is equipped with a water dispenser for dispensing water according to orders. The system according to feature 1.

3. It is equipped with a serving unit for robots to deliver meals. The system according to feature 1.

4. The robot has a selection unit that selects actions according to the customer's request. The system according to feature 1.

5. The acquisition unit is, Use a camera to capture images of the store interior. The system according to feature 1.

6. The aforementioned analysis unit, The acquired images are analyzed to determine the customer's location. The system according to feature 1.

7. The generating unit is It understands customer requests and generates actions to respond appropriately. The system according to feature 1.

8. The control unit, Control the robot based on the generated movements. The system according to feature 1.

9. The acquisition unit is, It estimates the user's emotions and adjusts the timing of visual information acquisition based on the estimated user emotions. The system according to feature 1.

10. The acquisition unit is, The camera settings are automatically adjusted to the optimal level based on the store's lighting conditions and time of day. The system according to feature 1.

Images