system

Abstract

The system according to this embodiment aims to automate the inventory process for store equipment and to manage the equipment accurately and quickly. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a sensor data acquisition unit, and a generation unit. The acquisition unit acquires images of the store equipment. The analysis unit analyzes the images acquired by the acquisition unit to identify the type and quantity of equipment. The sensor data acquisition unit acquires the status and location information of the equipment. The generation unit integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, 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, the inventory work of storefront devices is performed manually, which is time-consuming and laborious and lacks accuracy.

[0005] The system according to the embodiment aims to automate the inventory work of storefront devices and perform device management accurately and quickly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a sensor data acquisition unit, and a generation unit. The acquisition unit acquires images of storefront equipment. The analysis unit analyzes the images acquired by the acquisition unit to identify the type and quantity of equipment. The sensor data acquisition unit acquires the status and location information of the equipment. The generation unit integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. [Effects of the Invention]

[0007] The system according to this embodiment can automate the inventory process for store equipment, enabling accurate and rapid equipment management. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 receiving 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 receiving 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 that automates the inventory of in-store equipment and achieves accurate and rapid equipment management by utilizing a combination of AI vision image recognition technology and IoT sensor data. This system comprises an acquisition unit that acquires images of in-store equipment, an analysis unit that analyzes the acquired images and identifies the type and quantity of equipment, a sensor data acquisition unit that acquires the status and location information of the equipment, and a generation unit that integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. For example, a camera installed in the store takes images of the equipment, and the AI ​​analyzes the images to identify the type and quantity of equipment. In addition, IoT sensors acquire the location information and operating status of the equipment in real time, and the AI ​​integrates this data to generate inventory information. This system automates the inventory work that was previously performed manually by store staff, reducing their workload. Furthermore, because the AI ​​analyzes the data, false inventory reports are corrected, and accurate inventory information is obtained. For example, even if a store staff member incorrectly reports the quantity of equipment, the AI ​​can use image recognition and sensor data to identify the correct quantity. Moreover, since this system can monitor the status of equipment in real time, it is possible to detect equipment failures and abnormalities at an early stage. For example, if an IoT sensor detects abnormal operation of equipment, the AI ​​analyzes the information to identify the cause of the anomaly and proposes appropriate action. This streamlines equipment maintenance and minimizes downtime. In this way, by combining AI vision's image recognition technology with IoT sensor data, it is possible to automate inventory management of in-store equipment and achieve accurate and rapid equipment management. Thus, a system combining AI vision's image recognition technology with IoT sensor data can automate inventory management of in-store equipment and achieve accurate and rapid equipment management.

[0029] The system according to this embodiment comprises an acquisition unit, an analysis unit, a sensor data acquisition unit, and a generation unit. The acquisition unit acquires images of the store equipment. The acquisition unit, for example, takes images of the equipment using a camera installed in the store. The acquisition unit may include AI processing. The analysis unit analyzes the images acquired by the acquisition unit to identify the type and quantity of the equipment. The analysis unit, for example, uses an image recognition algorithm to identify the type of equipment and count the quantity. The analysis unit may include AI processing. The sensor data acquisition unit acquires the status and location information of the equipment. The sensor data acquisition unit, for example, uses an IoT sensor to acquire location information and operating status of the equipment in real time. The sensor data acquisition unit may also include AI processing. The generation unit integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. The generation unit, for example, integrates image data and sensor data to automatically generate inventory information. The generation unit may include AI processing. As a result, the system according to this embodiment can automate the inventory of store equipment and realize accurate and rapid equipment management.

[0030] The acquisition unit acquires images of in-store equipment. For example, the acquisition unit uses cameras installed in the store to capture images of the equipment. Specifically, the cameras are installed to capture high-resolution images and can focus on the overall store layout or specific equipment. Various types of cameras are used, including fixed cameras and pan-tilt-zoom (PTZ) cameras. Fixed cameras cover a wide area, while PTZ cameras can zoom in on specific equipment to acquire detailed images. These cameras periodically capture images and transmit the data to the acquisition unit in real time. The acquisition unit may include AI processing. The AI ​​performs image preprocessing, such as noise reduction and image correction. For example, it can adjust the brightness of images taken in low-light environments or correct distorted images. This allows the acquisition unit to provide clear and easily analyzable images. Furthermore, the acquisition unit can integrate images from multiple cameras to generate a panoramic image of the entire store. This allows for a quick overview of the store layout and improves the efficiency of the analysis unit.

[0031] The analysis unit analyzes images acquired by the acquisition unit to identify the type and quantity of equipment. For example, the analysis unit uses image recognition algorithms to identify the type of equipment and count its quantity. Specifically, an AI-powered deep learning model analyzes the images and extracts equipment features. For instance, a convolutional neural network (CNN) is used to identify features such as the shape, color, and logo of the equipment. This allows the analysis unit to identify different types of equipment with high accuracy. Furthermore, the analysis unit uses object detection algorithms to count the quantity of equipment. For example, algorithms such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector) are used to locate equipment within the image and count individual devices. The analysis unit can also incorporate AI processing. The AI ​​can update the analysis results in real time, ensuring that the store's situation is always up-to-date. Additionally, the analysis unit can analyze equipment inventory fluctuations and trends by comparing them with historical data. This allows the analysis unit to streamline in-store equipment management and support inventory optimization.

[0032] The sensor data acquisition unit acquires the status and location information of equipment. For example, the sensor data acquisition unit uses IoT sensors to acquire the location information and operating status of equipment in real time. Specifically, it uses GPS sensors or BLE (Bluetooth Low Energy) beacons attached to the equipment to acquire the precise location information of the equipment. This allows the sensor data acquisition unit to know where the equipment is located in the store. In addition, temperature sensors and vibration sensors can be used to monitor the operating status. For example, it can monitor the temperature of refrigeration equipment and issue an alert if an abnormal temperature change is detected. The sensor data acquisition unit may also include AI processing. The AI ​​analyzes the sensor data and performs anomaly detection and predictive maintenance. For example, it can analyze the vibration data of equipment to detect abnormal vibration patterns and detect signs of failure early. This allows the sensor data acquisition unit to monitor the status of equipment in real time and enable rapid response. Furthermore, the sensor data acquisition unit can transmit the acquired data to the cloud and cooperate with other systems and departments. This allows the sensor data acquisition unit to streamline equipment status management and improve overall operational efficiency.

[0033] The generation unit generates inventory information by integrating data obtained from the analysis unit and the sensor data acquisition unit. For example, the generation unit integrates image data and sensor data to automatically generate inventory information. Specifically, it combines data on the type and quantity of equipment obtained from the analysis unit with location information and operating status data obtained from the sensor data acquisition unit. This allows the generation unit to grasp the overall picture of the equipment in the store and generate accurate inventory information. The generation unit can include AI processing. The AI ​​automates the data integration process and efficiently generates inventory information. For example, it automatically updates the inventory list based on the data on the type and quantity of equipment, and reflects the operating status and location information of the equipment based on sensor data. Furthermore, the generation unit can visualize the generated inventory information and provide it to the user. For example, a dashboard can be used to allow users to check the status of the equipment in the store at a glance. In addition, the generation unit can periodically update the inventory information to always provide the latest information. This allows the generation unit to automate the inventory of equipment in the store and achieve accurate and rapid equipment management. Furthermore, the generation unit can analyze inventory trends and patterns based on past inventory data, which can be used to improve future inventory management. This allows the generation unit to streamline the management of in-store equipment and improve overall operational efficiency.

[0034] The generation unit can automatically generate inventory information. For example, the generation unit integrates image data and sensor data to automatically generate inventory information. The generation unit can include AI processing. This reduces the workload by automatically generating inventory information.

[0035] The sensor data acquisition unit can acquire the location information and operating status of equipment in real time. For example, the sensor data acquisition unit uses IoT sensors to acquire the location information and operating status of equipment in real time. The sensor data acquisition unit may also include AI processing. By acquiring the location information and operating status of equipment in real time, accurate inventory information can be obtained.

[0036] The analysis unit can analyze acquired images to identify the type and quantity of equipment. For example, the analysis unit can use an image recognition algorithm to identify the type of equipment and count its quantity. The analysis unit can also incorporate AI processing. This allows for accurate identification of the type and quantity of equipment through image analysis.

[0037] The generation unit can perform anomaly detection and maintenance suggestions. For example, the generation unit can detect equipment anomalies, analyze the information to identify the cause of the anomaly, and propose appropriate countermeasures. The generation unit can also incorporate AI processing. This streamlines equipment management through anomaly detection and maintenance suggestions.

[0038] The acquisition unit can acquire multiple images from different angles and distances, and the analysis unit can integrate them to improve accuracy. For example, the acquisition unit can acquire images from above and from the side, and the analysis unit can integrate them to obtain three-dimensional information. The acquisition unit can also acquire images from near and far distances, and the analysis unit can integrate them to obtain detailed information. The acquisition unit can also acquire images under different lighting conditions, and the analysis unit can integrate them to obtain accurate color information. The acquisition unit can include AI processing. This improves analysis accuracy by integrating multiple images.

[0039] The acquisition unit can acquire images based on specific time periods or days of the week, taking into account the store's congestion levels. For example, the acquisition unit can acquire images during weekday afternoons, and the analysis unit can identify less crowded times. The acquisition unit can also acquire images in the evenings on weekends, and the analysis unit can identify peak congestion times. The acquisition unit can also acquire images during specific event periods, and the analysis unit can analyze the impact of congestion. The acquisition unit can incorporate AI processing. This enables efficient image acquisition by considering congestion levels.

[0040] The image acquisition unit can automatically adjust the lighting conditions of the store to acquire optimal images. For example, it can adjust the brightness of the lighting to optimize image contrast. It can also adjust the color temperature of the lighting to acquire accurate color information. Furthermore, it can adjust the angle of the lighting to minimize shadows. The image acquisition unit can incorporate AI processing, enabling it to acquire optimal images by automatically adjusting lighting conditions.

[0041] The image acquisition unit can detect changes in the store layout and adaptively change the image acquisition position. For example, the acquisition unit can detect a layout change and acquire images from the new position. The acquisition unit can also detect a layout change and acquire images from the optimal angle. The acquisition unit can also detect a layout change and prioritize the acquisition of images of necessary equipment. The acquisition unit can incorporate AI processing. This allows it to always acquire the optimal image by adapting to layout changes.

[0042] The analysis unit can remove background noise from images, allowing for more accurate identification of the type and quantity of equipment. For example, the analysis unit can filter out background noise and enhance the contours of equipment. It can also remove background noise and accurately acquire color information of equipment. Furthermore, it can remove background noise and accurately identify the shape of equipment. The analysis unit can incorporate AI processing, which allows for accurate identification of the type and quantity of equipment by removing background noise.

[0043] The analysis unit can detect changes in the color and shape of equipment, enabling early detection of abnormalities. For example, the analysis unit can detect changes in the color of equipment and identify abnormalities. It can also detect changes in the shape of equipment and identify abnormalities. Furthermore, it can simultaneously detect changes in both color and shape of equipment and identify abnormalities. The analysis unit can incorporate AI processing. This allows for early detection of abnormalities by detecting changes in color and shape.

[0044] The analysis unit can improve analysis accuracy by combining different analysis algorithms. For example, it can improve analysis accuracy by combining machine learning algorithms and deep learning algorithms. It can also improve analysis accuracy by combining image analysis algorithms and sensor data analysis algorithms. The analysis unit can also improve analysis accuracy by processing different analysis algorithms in parallel. The analysis unit can include AI processing. This allows for improved analysis accuracy by combining different analysis algorithms.

[0045] The analysis unit can identify trends and patterns by comparing current data with past analysis data. For example, the analysis unit can identify trends by comparing current data with past analysis data. The analysis unit can also identify patterns by analyzing past analysis data. Furthermore, the analysis unit can integrate past and current analysis data to simultaneously identify trends and patterns. The analysis unit can incorporate AI processing, which allows it to identify trends and patterns by comparing them with past data.

[0046] The sensor data acquisition unit can acquire more multifaceted data by combining different types of sensors. For example, the sensor data acquisition unit can acquire environmental data by combining a temperature sensor and a humidity sensor. The sensor data acquisition unit can also acquire device operation data by combining a position sensor and an acceleration sensor. The sensor data acquisition unit can also acquire ambient environmental data by combining a light sensor and a sound sensor. The sensor data acquisition unit may also include AI processing. This allows for the acquisition of more multifaceted data by combining different types of sensors.

[0047] The sensor data acquisition unit can acquire data while considering environmental conditions (temperature, humidity, etc.). For example, the sensor data acquisition unit can use a temperature sensor to acquire data within a specific temperature range. The sensor data acquisition unit can also use a humidity sensor to acquire data within a specific humidity range. The sensor data acquisition unit can also monitor environmental conditions in real time and acquire data at the optimal timing. The sensor data acquisition unit may also include AI processing. This allows for the acquisition of more accurate data by considering environmental conditions.

[0048] The sensor data acquisition unit can acquire data from different locations within the store using movable sensors. For example, the sensor data acquisition unit can acquire temperature data from different locations within the store using movable sensors. The sensor data acquisition unit can also acquire humidity data from different locations within the store using movable sensors. The sensor data acquisition unit can also acquire location data from different locations within the store using movable sensors. The sensor data acquisition unit may also include AI processing. This allows data to be acquired from different locations within the store by using movable sensors.

[0049] The sensor data acquisition unit can collaborate with other IoT devices to complement each other's data. For example, the sensor data acquisition unit can collaborate with other IoT devices to complement temperature data. The sensor data acquisition unit can also collaborate with other IoT devices to complement humidity data. The sensor data acquisition unit can also collaborate with other IoT devices to complement location data. The sensor data acquisition unit may also include AI processing. This enables data complementation by collaborating with other IoT devices.

[0050] The generation unit can propose specific countermeasures when an anomaly is detected. For example, the generation unit can detect an equipment anomaly and propose repair procedures. The generation unit can also detect an equipment anomaly and propose a list of replacement parts. The generation unit can detect an equipment anomaly and propose how to contact a specialist technician. The generation unit can incorporate AI processing. This enables a rapid response by proposing specific countermeasures when an anomaly is detected.

[0051] The generation unit can identify anomalies and trends by comparing current data with past inventory data. For example, the generation unit can compare current data with past inventory data to identify anomalies. The generation unit can also analyze past inventory data to identify trends. Furthermore, the generation unit can integrate past and current inventory data to simultaneously identify anomalies and trends. The generation unit can incorporate AI processing, which allows it to identify anomalies and trends by comparing them with past data.

[0052] The generation unit can integrate different data sources (e.g., sales data) to generate more comprehensive inventory information. For example, it can integrate sales data and inventory data to analyze inventory trends. It can also integrate customer data and inventory data to perform demand forecasting. Furthermore, it can integrate logistics data and inventory data to optimize the supply chain. The generation unit can incorporate AI processing, enabling it to generate comprehensive inventory information by integrating different data sources.

[0053] The generation unit can update inventory information in real time, always providing the latest information. For example, the generation unit can acquire sensor data in real time and update inventory information. The generation unit can also analyze image data in real time and update inventory information. The generation unit can perform anomaly detection in real time and update inventory information. The generation unit can incorporate AI processing. This allows for real-time inventory updates, ensuring that the latest information is always provided.

[0054] The generation unit can visually display inventory information, making it intuitively understandable to users. For example, it can display inventory information in graphs and charts, enabling intuitive user comprehension. The generation unit can also color-code inventory information to visually highlight anomalies and trends. Furthermore, it can display inventory information on an interactive dashboard, allowing users to access detailed information. The generation unit can incorporate AI processing, enabling intuitive user comprehension through the visual display of inventory information.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The acquisition unit can acquire multiple images from different angles and distances, and the analysis unit can integrate them to improve accuracy. For example, the acquisition unit can acquire images from above and from the side, and the analysis unit can integrate them to obtain three-dimensional information. The acquisition unit can also acquire images from near and far distances, and the analysis unit can integrate them to obtain detailed information. The acquisition unit can also acquire images under different lighting conditions, and the analysis unit can integrate them to obtain accurate color information. The acquisition unit can include AI processing. This improves analysis accuracy by integrating multiple images.

[0057] The acquisition unit can acquire images based on specific time periods or days of the week, taking into account the store's congestion levels. For example, the acquisition unit can acquire images during weekday afternoons, and the analysis unit can identify less crowded times. The acquisition unit can also acquire images in the evenings on weekends, and the analysis unit can identify peak congestion times. The acquisition unit can also acquire images during specific event periods, and the analysis unit can analyze the impact of congestion. The acquisition unit can incorporate AI processing. This enables efficient image acquisition by considering congestion levels.

[0058] The image acquisition unit can automatically adjust the lighting conditions of the store to acquire optimal images. For example, it can adjust the brightness of the lighting to optimize image contrast. It can also adjust the color temperature of the lighting to acquire accurate color information. Furthermore, it can adjust the angle of the lighting to minimize shadows. The image acquisition unit can incorporate AI processing, enabling it to acquire optimal images by automatically adjusting lighting conditions.

[0059] The image acquisition unit can detect changes in the store layout and adaptively change the image acquisition position. For example, the acquisition unit can detect a layout change and acquire images from the new position. The acquisition unit can also detect a layout change and acquire images from the optimal angle. The acquisition unit can also detect a layout change and prioritize the acquisition of images of necessary equipment. The acquisition unit can incorporate AI processing. This allows it to always acquire the optimal image by adapting to layout changes.

[0060] The analysis unit can remove background noise from images, allowing for more accurate identification of the type and quantity of equipment. For example, the analysis unit can filter out background noise and enhance the contours of equipment. It can also remove background noise and accurately acquire color information of equipment. Furthermore, it can remove background noise and accurately identify the shape of equipment. The analysis unit can incorporate AI processing, which allows for accurate identification of the type and quantity of equipment by removing background noise.

[0061] The analysis unit can detect changes in the color and shape of equipment, enabling early detection of abnormalities. For example, the analysis unit can detect changes in the color of equipment and identify abnormalities. It can also detect changes in the shape of equipment and identify abnormalities. Furthermore, it can simultaneously detect changes in both color and shape of equipment and identify abnormalities. The analysis unit can incorporate AI processing. This allows for early detection of abnormalities by detecting changes in color and shape.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The acquisition unit acquires images of the in-store equipment. The acquisition unit, for example, takes images of the equipment using a camera installed in the store. The acquisition unit may include AI processing. Step 2: The analysis unit analyzes the images acquired by the acquisition unit to identify the type and quantity of equipment. The analysis unit may, for example, use an image recognition algorithm to identify the type of equipment and count the quantity. The analysis unit may include AI processing. Step 3: The sensor data acquisition unit acquires the status and location information of the equipment. The sensor data acquisition unit acquires the location information and operating status of the equipment in real time, for example, using IoT sensors. The sensor data acquisition unit may also include AI processing. Step 4: The generation unit integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. For example, the generation unit integrates image data and sensor data to automatically generate inventory information. The generation unit may include AI processing.

[0064] (Example of form 2) The system according to an embodiment of the present invention is a system that automates the inventory of in-store equipment and achieves accurate and rapid equipment management by utilizing a combination of AI vision image recognition technology and IoT sensor data. This system comprises an acquisition unit that acquires images of in-store equipment, an analysis unit that analyzes the acquired images and identifies the type and quantity of equipment, a sensor data acquisition unit that acquires the status and location information of the equipment, and a generation unit that integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. For example, a camera installed in the store takes images of the equipment, and the AI ​​analyzes the images to identify the type and quantity of equipment. In addition, IoT sensors acquire the location information and operating status of the equipment in real time, and the AI ​​integrates this data to generate inventory information. This system automates the inventory work that was previously performed manually by store staff, reducing their workload. Furthermore, because the AI ​​analyzes the data, false inventory reports are corrected, and accurate inventory information is obtained. For example, even if a store staff member incorrectly reports the quantity of equipment, the AI ​​can use image recognition and sensor data to identify the correct quantity. Moreover, since this system can monitor the status of equipment in real time, it is possible to detect equipment failures and abnormalities at an early stage. For example, if an IoT sensor detects abnormal operation of equipment, the AI ​​analyzes the information to identify the cause of the anomaly and proposes appropriate action. This streamlines equipment maintenance and minimizes downtime. In this way, by combining AI vision's image recognition technology with IoT sensor data, it is possible to automate inventory management of in-store equipment and achieve accurate and rapid equipment management. Thus, a system combining AI vision's image recognition technology with IoT sensor data can automate inventory management of in-store equipment and achieve accurate and rapid equipment management.

[0065] The system according to this embodiment comprises an acquisition unit, an analysis unit, a sensor data acquisition unit, and a generation unit. The acquisition unit acquires images of the store equipment. The acquisition unit, for example, takes images of the equipment using a camera installed in the store. The acquisition unit may include AI processing. The analysis unit analyzes the images acquired by the acquisition unit to identify the type and quantity of the equipment. The analysis unit, for example, uses an image recognition algorithm to identify the type of equipment and count the quantity. The analysis unit may include AI processing. The sensor data acquisition unit acquires the status and location information of the equipment. The sensor data acquisition unit, for example, uses an IoT sensor to acquire location information and operating status of the equipment in real time. The sensor data acquisition unit may also include AI processing. The generation unit integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. The generation unit, for example, integrates image data and sensor data to automatically generate inventory information. The generation unit may include AI processing. As a result, the system according to this embodiment can automate the inventory of store equipment and realize accurate and rapid equipment management.

[0066] The acquisition unit acquires images of in-store equipment. For example, the acquisition unit uses cameras installed in the store to capture images of the equipment. Specifically, the cameras are installed to capture high-resolution images and can focus on the overall store layout or specific equipment. Various types of cameras are used, including fixed cameras and pan-tilt-zoom (PTZ) cameras. Fixed cameras cover a wide area, while PTZ cameras can zoom in on specific equipment to acquire detailed images. These cameras periodically capture images and transmit the data to the acquisition unit in real time. The acquisition unit may include AI processing. The AI ​​performs image preprocessing, such as noise reduction and image correction. For example, it can adjust the brightness of images taken in low-light environments or correct distorted images. This allows the acquisition unit to provide clear and easily analyzable images. Furthermore, the acquisition unit can integrate images from multiple cameras to generate a panoramic image of the entire store. This allows for a quick overview of the store layout and improves the efficiency of the analysis unit.

[0067] The analysis unit analyzes images acquired by the acquisition unit to identify the type and quantity of equipment. For example, the analysis unit uses image recognition algorithms to identify the type of equipment and count its quantity. Specifically, an AI-powered deep learning model analyzes the images and extracts equipment features. For instance, a convolutional neural network (CNN) is used to identify features such as the shape, color, and logo of the equipment. This allows the analysis unit to identify different types of equipment with high accuracy. Furthermore, the analysis unit uses object detection algorithms to count the quantity of equipment. For example, algorithms such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector) are used to locate equipment within the image and count individual devices. The analysis unit can also incorporate AI processing. The AI ​​can update the analysis results in real time, ensuring that the store's situation is always up-to-date. Additionally, the analysis unit can analyze equipment inventory fluctuations and trends by comparing them with historical data. This allows the analysis unit to streamline in-store equipment management and support inventory optimization.

[0068] The sensor data acquisition unit acquires the status and location information of equipment. For example, the sensor data acquisition unit uses IoT sensors to acquire the location information and operating status of equipment in real time. Specifically, it uses GPS sensors or BLE (Bluetooth Low Energy) beacons attached to the equipment to acquire the precise location information of the equipment. This allows the sensor data acquisition unit to know where the equipment is located in the store. In addition, temperature sensors and vibration sensors can be used to monitor the operating status. For example, it can monitor the temperature of refrigeration equipment and issue an alert if an abnormal temperature change is detected. The sensor data acquisition unit may also include AI processing. The AI ​​analyzes the sensor data and performs anomaly detection and predictive maintenance. For example, it can analyze the vibration data of equipment to detect abnormal vibration patterns and detect signs of failure early. This allows the sensor data acquisition unit to monitor the status of equipment in real time and enable rapid response. Furthermore, the sensor data acquisition unit can transmit the acquired data to the cloud and cooperate with other systems and departments. This allows the sensor data acquisition unit to streamline equipment status management and improve overall operational efficiency.

[0069] The generation unit generates inventory information by integrating data obtained from the analysis unit and the sensor data acquisition unit. For example, the generation unit integrates image data and sensor data to automatically generate inventory information. Specifically, it combines data on the type and quantity of equipment obtained from the analysis unit with location information and operating status data obtained from the sensor data acquisition unit. This allows the generation unit to grasp the overall picture of the equipment in the store and generate accurate inventory information. The generation unit can include AI processing. The AI ​​automates the data integration process and efficiently generates inventory information. For example, it automatically updates the inventory list based on the data on the type and quantity of equipment, and reflects the operating status and location information of the equipment based on sensor data. Furthermore, the generation unit can visualize the generated inventory information and provide it to the user. For example, a dashboard can be used to allow users to check the status of the equipment in the store at a glance. In addition, the generation unit can periodically update the inventory information to always provide the latest information. This allows the generation unit to automate the inventory of equipment in the store and achieve accurate and rapid equipment management. Furthermore, the generation unit can analyze inventory trends and patterns based on past inventory data, which can be used to improve future inventory management. This allows the generation unit to streamline the management of in-store equipment and improve overall operational efficiency.

[0070] The generation unit can automatically generate inventory information. For example, the generation unit integrates image data and sensor data to automatically generate inventory information. The generation unit can include AI processing. This reduces the workload by automatically generating inventory information.

[0071] The sensor data acquisition unit can acquire the location information and operating status of equipment in real time. For example, the sensor data acquisition unit uses IoT sensors to acquire the location information and operating status of equipment in real time. The sensor data acquisition unit may also include AI processing. By acquiring the location information and operating status of equipment in real time, accurate inventory information can be obtained.

[0072] The analysis unit can analyze acquired images to identify the type and quantity of equipment. For example, the analysis unit can use an image recognition algorithm to identify the type of equipment and count its quantity. The analysis unit can also incorporate AI processing. This allows for accurate identification of the type and quantity of equipment through image analysis.

[0073] The generation unit can perform anomaly detection and maintenance suggestions. For example, the generation unit can detect equipment anomalies, analyze the information to identify the cause of the anomaly, and propose appropriate countermeasures. The generation unit can also incorporate AI processing. This streamlines equipment management through anomaly detection and maintenance suggestions.

[0074] The acquisition unit can estimate the user's emotions and adjust the timing of image acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit can reduce the frequency of image acquisition to alleviate the burden. If the user is relaxed, the acquisition unit can also increase the frequency to acquire more detailed images. If the user is in a hurry, the acquisition unit can also acquire images quickly and prioritize analysis. The acquisition unit can include AI processing. This reduces the burden on the user by adjusting the timing of image acquisition 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The acquisition unit can acquire multiple images from different angles and distances, and the analysis unit can integrate them to improve accuracy. For example, the acquisition unit can acquire images from above and from the side, and the analysis unit can integrate them to obtain three-dimensional information. The acquisition unit can also acquire images from near and far distances, and the analysis unit can integrate them to obtain detailed information. The acquisition unit can also acquire images under different lighting conditions, and the analysis unit can integrate them to obtain accurate color information. The acquisition unit can include AI processing. This improves analysis accuracy by integrating multiple images.

[0076] The acquisition unit can acquire images based on specific time periods or days of the week, taking into account the store's congestion levels. For example, the acquisition unit can acquire images during weekday afternoons, and the analysis unit can identify less crowded times. The acquisition unit can also acquire images in the evenings on weekends, and the analysis unit can identify peak congestion times. The acquisition unit can also acquire images during specific event periods, and the analysis unit can analyze the impact of congestion. The acquisition unit can incorporate AI processing. This enables efficient image acquisition by considering congestion levels.

[0077] The acquisition unit can estimate the user's emotions and determine the priority of images to acquire based on the estimated emotions. For example, if the user is stressed, the acquisition unit will prioritize acquiring images of important equipment. If the user is relaxed, the acquisition unit can also acquire overall images. If the user is in a hurry, the acquisition unit can quickly acquire necessary images. The acquisition unit can include AI processing. This allows for the priority acquisition of important images by determining image priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The image acquisition unit can automatically adjust the lighting conditions of the store to acquire optimal images. For example, it can adjust the brightness of the lighting to optimize image contrast. It can also adjust the color temperature of the lighting to acquire accurate color information. Furthermore, it can adjust the angle of the lighting to minimize shadows. The image acquisition unit can incorporate AI processing, enabling it to acquire optimal images by automatically adjusting lighting conditions.

[0079] The image acquisition unit can detect changes in the store layout and adaptively change the image acquisition position. For example, the acquisition unit can detect a layout change and acquire images from the new position. The acquisition unit can also detect a layout change and acquire images from the optimal angle. The acquisition unit can also detect a layout change and prioritize the acquisition of images of necessary equipment. The acquisition unit can incorporate AI processing. This allows it to always acquire the optimal image by adapting to layout changes.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. The analysis unit can include AI processing. This allows for a user-friendly display by adjusting the display method of the analysis results 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.

[0081] The analysis unit can remove background noise from images, allowing for more accurate identification of the type and quantity of equipment. For example, the analysis unit can filter out background noise and enhance the contours of equipment. It can also remove background noise and accurately acquire color information of equipment. Furthermore, it can remove background noise and accurately identify the shape of equipment. The analysis unit can incorporate AI processing, which allows for accurate identification of the type and quantity of equipment by removing background noise.

[0082] The analysis unit can detect changes in the color and shape of equipment, enabling early detection of abnormalities. For example, the analysis unit can detect changes in the color of equipment and identify abnormalities. It can also detect changes in the shape of equipment and identify abnormalities. Furthermore, it can simultaneously detect changes in both color and shape of equipment and identify abnormalities. The analysis unit can incorporate AI processing. This allows for early detection of abnormalities by detecting changes in color and shape.

[0083] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit will prioritize displaying important analysis results. If the user is relaxed, the analysis unit can also display overall analysis results. If the user is in a hurry, the analysis unit can quickly display the necessary analysis results. The analysis unit can include AI processing. This allows for the prioritization of analysis results according to the user's emotions, thereby prioritizing the display of important analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can improve analysis accuracy by combining different analysis algorithms. For example, it can improve analysis accuracy by combining machine learning algorithms and deep learning algorithms. It can also improve analysis accuracy by combining image analysis algorithms and sensor data analysis algorithms. The analysis unit can also improve analysis accuracy by processing different analysis algorithms in parallel. The analysis unit can include AI processing. This allows for improved analysis accuracy by combining different analysis algorithms.

[0085] The analysis unit can identify trends and patterns by comparing current data with past analysis data. For example, the analysis unit can identify trends by comparing current data with past analysis data. The analysis unit can also identify patterns by analyzing past analysis data. Furthermore, the analysis unit can integrate past and current analysis data to simultaneously identify trends and patterns. The analysis unit can incorporate AI processing, which allows it to identify trends and patterns by comparing them with past data.

[0086] The sensor data acquisition unit can estimate the user's emotions and adjust the frequency of sensor data acquisition based on the estimated emotions. For example, if the user is stressed, the sensor data acquisition unit can reduce the frequency of sensor data acquisition to alleviate the burden. If the user is relaxed, the sensor data acquisition unit can also increase the frequency to acquire more detailed data. If the user is in a hurry, the sensor data acquisition unit can also quickly acquire the necessary data. The sensor data acquisition unit may also include AI processing. This allows for reduced user burden by adjusting the frequency of sensor data acquisition 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.

[0087] The sensor data acquisition unit can acquire more multifaceted data by combining different types of sensors. For example, the sensor data acquisition unit can acquire environmental data by combining a temperature sensor and a humidity sensor. The sensor data acquisition unit can also acquire device operation data by combining a position sensor and an acceleration sensor. The sensor data acquisition unit can also acquire ambient environmental data by combining a light sensor and a sound sensor. The sensor data acquisition unit may also include AI processing. This allows for the acquisition of more multifaceted data by combining different types of sensors.

[0088] The sensor data acquisition unit can acquire data while considering environmental conditions (temperature, humidity, etc.). For example, the sensor data acquisition unit can use a temperature sensor to acquire data within a specific temperature range. The sensor data acquisition unit can also use a humidity sensor to acquire data within a specific humidity range. The sensor data acquisition unit can also monitor environmental conditions in real time and acquire data at the optimal timing. The sensor data acquisition unit may also include AI processing. This allows for the acquisition of more accurate data by considering environmental conditions.

[0089] The sensor data acquisition unit can estimate the user's emotions and determine the priority of sensor data to acquire based on the estimated user emotions. For example, if the user is stressed, the sensor data acquisition unit will prioritize acquiring important sensor data. If the user is relaxed, the sensor data acquisition unit can also acquire overall sensor data. If the user is in a hurry, the sensor data acquisition unit can also quickly acquire the necessary sensor data. The sensor data acquisition unit may also include AI processing. This allows for the priority of acquiring important data by determining the priority of sensor data 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.

[0090] The sensor data acquisition unit can acquire data from different locations within the store using movable sensors. For example, the sensor data acquisition unit can acquire temperature data from different locations within the store using movable sensors. The sensor data acquisition unit can also acquire humidity data from different locations within the store using movable sensors. The sensor data acquisition unit can also acquire location data from different locations within the store using movable sensors. The sensor data acquisition unit may also include AI processing. This allows data to be acquired from different locations within the store by using movable sensors.

[0091] The sensor data acquisition unit can collaborate with other IoT devices to complement each other's data. For example, the sensor data acquisition unit can collaborate with other IoT devices to complement temperature data. The sensor data acquisition unit can also collaborate with other IoT devices to complement humidity data. The sensor data acquisition unit can also collaborate with other IoT devices to complement location data. The sensor data acquisition unit may also include AI processing. This enables data complementation by collaborating with other IoT devices.

[0092] The generation unit can estimate the user's emotions and adjust the display method of the inventory information generated based on the estimated user emotions. For example, if the user is nervous, the generation unit can provide a simple and highly visible display method. If the user is relaxed, the generation unit can also provide a display method that includes detailed information. If the user is in a hurry, the generation unit can also provide a display method that gets straight to the point. The generation unit can include AI processing. This allows for a user-friendly display by adjusting the display method of the inventory information according to the user's emotions. 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.

[0093] The generation unit can propose specific countermeasures when an anomaly is detected. For example, the generation unit can detect an equipment anomaly and propose repair procedures. The generation unit can also detect an equipment anomaly and propose a list of replacement parts. The generation unit can detect an equipment anomaly and propose how to contact a specialist technician. The generation unit can incorporate AI processing. This enables a rapid response by proposing specific countermeasures when an anomaly is detected.

[0094] The generation unit can identify anomalies and trends by comparing current data with past inventory data. For example, the generation unit can compare current data with past inventory data to identify anomalies. The generation unit can also analyze past inventory data to identify trends. Furthermore, the generation unit can integrate past and current inventory data to simultaneously identify anomalies and trends. The generation unit can incorporate AI processing, which allows it to identify anomalies and trends by comparing them with past data.

[0095] The generation unit can estimate the user's emotions and determine the priority of the inventory information to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize displaying important inventory information. If the user is relaxed, the generation unit can also display overall inventory information. If the user is in a hurry, the generation unit can also quickly display the inventory information that is needed. The generation unit can include AI processing. This allows for the priority display of important information by determining the priority of inventory information according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generation AI. Generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The generation unit can integrate different data sources (e.g., sales data) to generate more comprehensive inventory information. For example, it can integrate sales data and inventory data to analyze inventory trends. It can also integrate customer data and inventory data to perform demand forecasting. Furthermore, it can integrate logistics data and inventory data to optimize the supply chain. The generation unit can incorporate AI processing, enabling it to generate comprehensive inventory information by integrating different data sources.

[0097] The generation unit can update inventory information in real time, always providing the latest information. For example, the generation unit can acquire sensor data in real time and update inventory information. The generation unit can also analyze image data in real time and update inventory information. The generation unit can perform anomaly detection in real time and update inventory information. The generation unit can incorporate AI processing. This allows for real-time inventory updates, ensuring that the latest information is always provided.

[0098] The generation unit can visually display inventory information, making it intuitively understandable to users. For example, it can display inventory information in graphs and charts, enabling intuitive user comprehension. The generation unit can also color-code inventory information to visually highlight anomalies and trends. Furthermore, it can display inventory information on an interactive dashboard, allowing users to access detailed information. The generation unit can incorporate AI processing, enabling intuitive user comprehension through the visual display of inventory information.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The acquisition unit can estimate the user's emotions and adjust the timing of image acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit can reduce the frequency of image acquisition to alleviate the burden. If the user is relaxed, the acquisition unit can also increase the frequency to acquire more detailed images. If the user is in a hurry, the acquisition unit can also acquire images quickly and prioritize analysis. The acquisition unit can include AI processing. This reduces the burden on the user by adjusting the timing of image acquisition 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The acquisition unit can acquire multiple images from different angles and distances, and the analysis unit can integrate them to improve accuracy. For example, the acquisition unit can acquire images from above and from the side, and the analysis unit can integrate them to obtain three-dimensional information. The acquisition unit can also acquire images from near and far distances, and the analysis unit can integrate them to obtain detailed information. The acquisition unit can also acquire images under different lighting conditions, and the analysis unit can integrate them to obtain accurate color information. The acquisition unit can include AI processing. This improves analysis accuracy by integrating multiple images.

[0102] The acquisition unit can acquire images based on specific time periods or days of the week, taking into account the store's congestion levels. For example, the acquisition unit can acquire images during weekday afternoons, and the analysis unit can identify less crowded times. The acquisition unit can also acquire images in the evenings on weekends, and the analysis unit can identify peak congestion times. The acquisition unit can also acquire images during specific event periods, and the analysis unit can analyze the impact of congestion. The acquisition unit can incorporate AI processing. This enables efficient image acquisition by considering congestion levels.

[0103] The acquisition unit can estimate the user's emotions and determine the priority of images to acquire based on the estimated emotions. For example, if the user is stressed, the acquisition unit will prioritize acquiring images of important equipment. If the user is relaxed, the acquisition unit can also acquire overall images. If the user is in a hurry, the acquisition unit can quickly acquire necessary images. The acquisition unit can include AI processing. This allows for the priority acquisition of important images by determining image priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The image acquisition unit can automatically adjust the lighting conditions of the store to acquire optimal images. For example, it can adjust the brightness of the lighting to optimize image contrast. It can also adjust the color temperature of the lighting to acquire accurate color information. Furthermore, it can adjust the angle of the lighting to minimize shadows. The image acquisition unit can incorporate AI processing, enabling it to acquire optimal images by automatically adjusting lighting conditions.

[0105] The image acquisition unit can detect changes in the store layout and adaptively change the image acquisition position. For example, the acquisition unit can detect a layout change and acquire images from the new position. The acquisition unit can also detect a layout change and acquire images from the optimal angle. The acquisition unit can also detect a layout change and prioritize the acquisition of images of necessary equipment. The acquisition unit can incorporate AI processing. This allows it to always acquire the optimal image by adapting to layout changes.

[0106] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. The analysis unit can include AI processing. This allows for a user-friendly display by adjusting the display method of the analysis results 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.

[0107] The analysis unit can remove background noise from images, allowing for more accurate identification of the type and quantity of equipment. For example, the analysis unit can filter out background noise and enhance the contours of equipment. It can also remove background noise and accurately acquire color information of equipment. Furthermore, it can remove background noise and accurately identify the shape of equipment. The analysis unit can incorporate AI processing, which allows for accurate identification of the type and quantity of equipment by removing background noise.

[0108] The analysis unit can detect changes in the color and shape of equipment, enabling early detection of abnormalities. For example, the analysis unit can detect changes in the color of equipment and identify abnormalities. It can also detect changes in the shape of equipment and identify abnormalities. Furthermore, it can simultaneously detect changes in both color and shape of equipment and identify abnormalities. The analysis unit can incorporate AI processing. This allows for early detection of abnormalities by detecting changes in color and shape.

[0109] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit will prioritize displaying important analysis results. If the user is relaxed, the analysis unit can also display overall analysis results. If the user is in a hurry, the analysis unit can quickly display the necessary analysis results. The analysis unit can include AI processing. This allows for the prioritization of analysis results according to the user's emotions, thereby prioritizing the display of important analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The acquisition unit acquires images of the in-store equipment. The acquisition unit, for example, takes images of the equipment using a camera installed in the store. The acquisition unit may include AI processing. Step 2: The analysis unit analyzes the images acquired by the acquisition unit to identify the type and quantity of equipment. The analysis unit may, for example, use an image recognition algorithm to identify the type of equipment and count the quantity. The analysis unit may include AI processing. Step 3: The sensor data acquisition unit acquires the status and location information of the equipment. The sensor data acquisition unit acquires the location information and operating status of the equipment in real time, for example, using IoT sensors. The sensor data acquisition unit may also include AI processing. Step 4: The generation unit integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. For example, the generation unit integrates image data and sensor data to automatically generate inventory information. The generation unit may include AI processing.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the acquisition unit, analysis unit, sensor data acquisition unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires images of storefront equipment using the camera 42 of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired images to identify the type and quantity of equipment. The sensor data acquisition unit acquires equipment status and location information from IoT sensors via the communication I / F 44 of the smart device 14. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the acquisition unit, analysis unit, sensor data acquisition unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires images of in-store equipment using the camera 42 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired images to identify the type and quantity of equipment. The sensor data acquisition unit acquires equipment status and location information from IoT sensors via the communication I / F 44 of the smart glasses 214. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. The correspondence between each unit and the equipment and control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the acquisition unit, analysis unit, sensor data acquisition unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires images of in-store equipment using the camera 42 of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired images to identify the type and quantity of equipment. The sensor data acquisition unit acquires equipment status and location information from IoT sensors via the communication I / F 44 of the headset terminal 314. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. The correspondence between each unit and the equipment and control unit is not limited to the example described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the acquisition unit, analysis unit, sensor data acquisition unit, and generation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires images of storefront equipment using the camera 42 of the robot 414. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired images to identify the type and quantity of equipment. The sensor data acquisition unit acquires equipment status and location information from IoT sensors via the communication I / F 44 of the robot 414. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. The correspondence between each unit and the equipment and control unit is not limited to the example described above, and various changes are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) An acquisition unit that acquires images of in-store equipment, An analysis unit analyzes the images acquired by the acquisition unit to identify the type and quantity of equipment, A sensor data acquisition unit that acquires the status and location information of the equipment, The system includes a generation unit that integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. A system characterized by the following features. (Note 2) The generating unit is Automatically generate inventory information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned sensor data acquisition unit is Acquire the location and operating status of the equipment in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The acquired images are analyzed to identify the type and quantity of equipment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Anomaly detection and maintenance proposals are performed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of image acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, Multiple images are acquired from different angles and distances, and the analysis unit integrates them to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Images are retrieved based on specific time slots and days of the week, taking into account the store's congestion level. 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 determines the priority of images to retrieve based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, Automatically adjusts store lighting conditions to obtain optimal images. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, The system detects changes in the store layout and adaptively adjusts the image acquisition location. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned analysis unit, Remove background noise from images to more accurately identify the type and quantity of equipment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It detects changes in the color and shape of equipment to detect abnormalities early. 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 prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Combining different analysis algorithms improves analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Compare with past analysis data to identify trends and patterns. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned sensor data acquisition unit is The system estimates the user's emotions and adjusts the frequency of sensor data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned sensor data acquisition unit is By combining different types of sensors, we can acquire more multifaceted data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned sensor data acquisition unit is Data is acquired while taking environmental conditions into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned sensor data acquisition unit is The system estimates the user's emotions and determines the priority of sensor data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned sensor data acquisition unit is Use mobile sensors to collect data from different locations within the store. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned sensor data acquisition unit is It works in conjunction with other IoT devices to complement each other's data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is We estimate the user's emotions and adjust how inventory information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When an anomaly is detected, we propose specific countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is Compare with past inventory data to identify anomalies and trends. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates user sentiment and determines the priority of inventory information to be generated based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is Integrate with different data sources to generate more comprehensive inventory information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is We update inventory information in real time, providing you with the latest information at all times. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is Inventory information is displayed visually, making it easy for users to understand intuitively. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 images of in-store equipment, An analysis unit analyzes the images acquired by the acquisition unit to identify the type and quantity of equipment, A sensor data acquisition unit that acquires the status and location information of the equipment, The system includes a generation unit that integrates the data obtained by the analysis unit and the sensor data acquisition unit to generate inventory information. A system characterized by the following features.

2. The generating unit is Automatically generate inventory information. The system according to feature 1.

3. The aforementioned sensor data acquisition unit is Acquire the location and operating status of the equipment in real time. The system according to feature 1.

4. The aforementioned analysis unit, The acquired images are analyzed to identify the type and quantity of equipment. The system according to feature 1.

5. The generating unit is Anomaly detection and maintenance proposals are performed. The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of image acquisition based on those emotions. The system according to feature 1.

7. The acquisition unit is, Multiple images are acquired from different angles and distances, and the analysis unit integrates them to improve accuracy. The system according to feature 1.

8. The acquisition unit is, Images are retrieved based on specific time slots and days of the week, taking into account the store's congestion level. The system according to feature 1.

9. The acquisition unit is, It estimates the user's emotions and determines the priority of images to retrieve based on the estimated user emotions. The system according to feature 1.

10. The acquisition unit is, Automatically adjusts store lighting conditions to obtain optimal images. The system according to feature 1.

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