System

Abstract

An object of a system according to an embodiment is to improve the efficiency and accuracy of in-chamber work.SOLUTION: A system according to an embodiment includes an inside information collection unit, an analysis unit, a visual information generation unit, and a display unit. The inside information collection unit collects inside information. The analysis unit analyzes the inside information collected by the inside information collection unit. The visual information generation unit generates visual information on the basis of the information analyzed by the analysis unit. The display unit displays the visual information generated by the visual information generation unit on the AR glasses.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology leaves room for improvement in terms of efficiency and accuracy of in-warehouse operations.

[0005] The system according to the embodiment aims to improve the efficiency and accuracy of work inside the warehouse. [Means for solving the problem]

[0006] The system according to the embodiment includes an inside fridge information collection unit, an analysis unit, a visual information generation unit, and a display unit. The inside fridge information collection unit collects inside fridge information. The analysis unit analyzes the inside fridge information collected by the inside fridge information collection unit. The visual information generation unit generates visual information based on the information analyzed by the analysis unit. The display unit displays the visual information generated by the visual information generation unit on the AR glasses. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency and accuracy of in-warehouse work. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The picking navigation system according to an embodiment of the present invention is a system that combines Gemini, AR glasses, and warehouse information to provide navigation support for the picking of target products based on the visual information displayed on the AR glasses. As a result, the picking navigation system can support workers in efficiently picking products.

[0029] A picking operation navigation system according to an embodiment includes an in-warehouse information collection unit, an analysis unit, a visual information generation unit, and a display unit. The in-warehouse information collection unit collects in-warehouse information. For example, the in-warehouse information collection unit acquires product location information, inventory status, shelf layout, and the like from a database. The in-warehouse information collection unit can also use drones to update product location information in real time. For example, drones are equipped with high-precision cameras and sensors that read product barcodes or QR codes to acquire accurate location information. The analysis unit analyzes the in-warehouse information collected by the in-warehouse information collection unit. For example, the analysis unit calculates an optimal picking route based on the product location information and inventory data. The analysis unit can also use physical characteristics of the products, such as their weight and size, for analysis. For example, a weight sensor installed on a shelf measures the weight of the product, and a size sensor measures the product dimensions. The visual information generation unit generates visual information based on the information analyzed by the analysis unit. For example, the visual information generation unit generates a graphical display showing the product location and the picking route. The visual information generation unit can also generate visual information including detailed product information (e.g., manufacturing date, expiration date). The display unit displays the visual information generated by the visual information generation unit on the AR glasses. For example, when a worker wears the AR glasses, the display unit displays the location of the product and the picking route in the worker's field of view. The display unit can also indicate to the worker the location of the next product to be picked by using arrows or highlights displayed on the AR glasses. In this way, the picking operation navigation system according to the embodiment can assist the worker in picking products efficiently. For example, by wearing the AR glasses, the worker is visually guided and can pick products efficiently. Furthermore, Gemini can calculate the optimal route, thereby reducing unnecessary movements and further improving work efficiency.

[0030] The warehouse information collection unit can acquire product location information, inventory status, and shelf layout from the database. The warehouse information collection unit acquires, for example, product location information, inventory status, and shelf layout from the database. For example, product location information can be acquired based on GPS data or RFID tag information. In addition, inventory status can be acquired based on real-time inventory data or periodic inventory check results. Furthermore, shelf layout can be acquired based on shelf layout diagrams and layout data. In this way, by acquiring product location information, inventory status, and shelf layout from the database, the efficiency of picking work can be improved.

[0031] When a worker wears the AR glasses, the display unit can display the product location and picking route in the worker's field of view. For example, when a worker wears the AR glasses, the display unit displays the product location and picking route in the worker's field of view. For example, the product location is displayed graphically on the AR glasses' display, and the picking route is indicated by arrows and lines. The display unit can also display physical characteristics of the product, such as weight and size. For example, sensors that automatically measure the weight and size of products can be installed in the warehouse and the display can be based on the collected data. This allows workers to visually check the product location and picking route, allowing them to perform picking work efficiently.

[0032] The display unit can use arrows and highlights displayed on the AR glasses to indicate to the worker the location of the next item to be picked. The display unit can, for example, use arrows and highlights displayed on the AR glasses to indicate to the worker the location of the next item to be picked. For example, the arrow indicates the direction of the item, and the highlight emphasizes the location of the item. The display unit can also use an emotion estimation function to monitor the worker's stress level, collect information on areas with high stress, and reflect this information in the analysis. For example, to monitor the worker's stress level, the worker wears a wearable device that measures heart rate and electrodermal activity. This can support efficient picking work by indicating the location of the next item to be picked to the worker using arrows and highlights.

[0033] The display unit can display a confirmation message on the AR glasses when scanning a product barcode. For example, the display unit displays a confirmation message on the AR glasses when scanning a product barcode. For example, when a barcode scanner reads a product barcode, a confirmation message is displayed on the display of the AR glasses. The display unit can also change the content of the confirmation message depending on the type of barcode and the scanning method. For example, a scanner compatible with one-dimensional barcodes and two-dimensional barcodes can be used. This allows for accurate picking by displaying a confirmation message when scanning a product barcode.

[0034] The display unit can add the manufacturing date and expiration date as detailed product information, allowing the worker to check the necessary information at a glance. The display unit can add, for example, the manufacturing date and expiration date as detailed product information, allowing the worker to check the necessary information at a glance. For example, when the worker directs his or her gaze toward the product, the manufacturing date and expiration date are displayed on the display of the AR glasses. The display unit can also retrieve detailed product information from a database and display it in real time. In this way, by adding detailed product information, the worker can check the necessary information at a glance.

[0035] The display unit can use the worker's gaze tracking technology to display information at the optimal position according to the worker's gaze movement. The display unit can use the worker's gaze tracking technology to display information at the optimal position according to the worker's gaze movement, for example. For example, an eye-tracking sensor can be installed in the AR glasses to track the worker's gaze. Product information can be displayed where the worker directs their gaze. The display unit can also dynamically change the display content according to the worker's gaze movement. For example, the display content can be adjusted based on the direction of the gaze and the speed at which the viewpoint moves. This allows the worker's efficiency to be improved by using gaze tracking technology to display information at the optimal position according to the worker's gaze movement.

[0036] The display unit can add audio guidance and navigate the worker both visually and audibly. The display unit can add audio guidance and navigate the worker both visually and audibly. For example, an audio guidance function can be added to AR glasses and linked to visual information. Detailed product information and picking instructions can be provided by voice for the product the worker is looking at. The display unit can also dynamically change the content of the audio guidance. For example, the content of the audio guidance can be adjusted depending on the importance and priority of the product. In this way, adding audio guidance can navigate the worker both visually and audibly, supporting efficient work.

[0037] The display unit allows multiple workers to share information in real time, thereby promoting teamwork. The display unit, for example, allows multiple workers to share information in real time, thereby promoting teamwork. For example, using AR glasses, information about products picked by a worker can be displayed on the AR glasses of other workers, allowing the entire team to understand the progress of the work. The display unit also updates information in real time, facilitating smooth communication between workers. This allows multiple workers to share information in real time, thereby promoting teamwork.

[0038] The display unit can change the color of the arrows and highlights displayed on the AR glasses according to the importance and priority of the product. For example, the display unit changes the color of the arrows and highlights displayed on the AR glasses according to the importance and priority of the product. For example, red arrows are displayed for high-importance products, and blue arrows are displayed for low-importance products. The display unit can also dynamically change the display content based on the importance and priority of the product. For example, the display unit evaluates the importance of the product based on sales data and inventory status, and evaluates the priority based on the shipping deadline and customer importance. This allows workers to perform picking work efficiently by changing the color of the arrows and highlights according to the importance and priority of the product.

[0039] The display unit monitors the progress of picking work in real time, and if a delay occurs, Gemini can automatically propose the optimal alternative route. The display unit, for example, monitors the progress of picking work in real time, and if a delay occurs, Gemini can automatically propose the optimal alternative route. For example, the display unit collects information on products picked by workers in real time to understand the progress. Furthermore, if a delay occurs, Gemini can calculate the optimal alternative route and display it on the AR glasses. For example, the display unit proposes an alternative route using a shortest path algorithm or a route based on priority. This makes it possible to monitor the progress of picking work in real time and propose the optimal alternative route if a delay occurs, thereby improving work efficiency.

[0040] The display unit can realize multimodal navigation by adding feedback such as vibration and sound in addition to the information displayed on the AR glasses. For example, the display unit can realize multimodal navigation by adding feedback such as vibration and sound in addition to the information displayed on the AR glasses. For example, a vibration feedback function can be added to the AR glasses and linked to visual information. When a worker looks in the direction they should go next, the AR glasses vibrate to indicate the direction. The display unit can also add an audio feedback function and link to visual information. For example, detailed product information and picking instructions can be provided by audio for each product the worker looks at. In this way, adding feedback such as vibration and sound can realize multimodal navigation and improve worker efficiency.

[0041] The display unit can display the location information of other workers and provide information to avoid collisions and congestion. For example, the display unit can display the location information of other workers and provide information to avoid collisions and congestion. For example, the display unit can display the location information of other workers on AR glasses, and if there is another worker in the direction the worker is looking, the display unit can display the location information and issue a warning. The display unit can also update the location information of other workers in real time and suggest routes to avoid congestion. For example, the display unit can suggest methods for measuring congestion levels and routes to avoid congestion. By displaying the location information of other workers, the display unit can provide information to avoid collisions and congestion, improving work efficiency.

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

[0043] The picking work navigation system can be equipped with a health management unit that monitors the health status of workers. For example, the health management unit can measure the worker's heart rate and body temperature in real time and issue an alert if an abnormality is detected. The health management unit can also evaluate the worker's fatigue level and send a notification encouraging them to take a break. Furthermore, the health management unit can record the worker's health data over the long term and generate reports for health management. This allows the system to monitor the worker's health status and encourage them to take a break at the appropriate time, thereby improving work efficiency.

[0044] The picking work navigation system can include a motion analysis unit that analyzes the motions of the worker and suggests efficient motions. For example, the motion analysis unit records the worker's motions using a camera or sensor and analyzes efficient motion patterns. The motion analysis unit can also suggest efficient motions to the worker and provide advice on reducing unnecessary motions. Furthermore, the motion analysis unit can create a training program based on the worker's motion data and help improve the worker's skills. In this way, work efficiency can be improved by analyzing the worker's motions and suggesting efficient motions.

[0045] The picking work navigation system can be equipped with a motion recording unit that can record the motions of workers and play them back for later review. For example, the motion recording unit can record the motions of workers using a camera or sensor and play them back for later review. The motion recording unit can also analyze the motions of workers and suggest efficient motion patterns. Furthermore, the motion recording unit can create training programs based on the motion data of workers and help improve their skills. This allows for work efficiency to be improved by recording the motions of workers and playing them back for later review.

[0046] The picking work navigation system can be equipped with a motion evaluation unit that evaluates the motion of the worker in real time and encourages efficient motion. For example, the motion evaluation unit evaluates the motion of the worker in real time using a camera or sensor and provides feedback to encourage efficient motion. The motion evaluation unit can also create a training program based on the motion data of the worker to help improve the worker's skills. Furthermore, the motion evaluation unit can record the motion of the worker and play it back for later review. This allows the system to evaluate the motion of the worker in real time and encourage efficient motion, thereby improving work efficiency.

[0047] The picking work navigation system can be equipped with a motion simulation unit that simulates the motions of a worker and proposes optimal motions. For example, the motion simulation unit simulates the motions of a worker and proposes optimal motion patterns. The motion simulation unit can also create training programs based on the motion data of the worker to help improve the worker's skills. Furthermore, the motion simulation unit can record the worker's motions and play them back for later review. This allows for improved work efficiency by simulating the worker's motions and proposing optimal motions.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The warehouse information collection unit collects warehouse information. For example, it obtains product location information, inventory status, shelf layout, and other information from a database. Product location information can also be updated in real time using drones. Drones are equipped with high-precision cameras and sensors, and obtain accurate location information by reading product barcodes and QR codes. Step 2: The analysis unit analyzes the warehouse information collected by the warehouse information collection unit. For example, it calculates the optimal picking route based on product location information and inventory data. Physical characteristics such as product weight and size can also be used in the analysis. Weight sensors installed on shelves measure the weight of products, and size sensors measure the product dimensions. Step 3: The visual information generation unit generates visual information based on the information analyzed by the analysis unit. For example, it generates a graphical display showing the product location and picking route. It can also generate visual information including detailed product information (e.g., manufacturing date, expiration date). Step 4: The display unit displays the visual information generated by the visual information generation unit on the AR glasses. For example, when a worker wears the AR glasses, the location of the product and the picking route are displayed in the worker's field of vision. In addition, arrows and highlights displayed on the AR glasses can be used to show the worker the location of the next product to be picked.

[0050] (Example 2) The picking navigation system according to an embodiment of the present invention is a system that combines Gemini, AR glasses, and warehouse information to provide navigation support for the picking of target products based on the visual information displayed on the AR glasses. As a result, the picking navigation system can support workers in efficiently picking products.

[0051] A picking operation navigation system according to an embodiment includes an in-warehouse information collection unit, an analysis unit, a visual information generation unit, and a display unit. The in-warehouse information collection unit collects in-warehouse information. For example, the in-warehouse information collection unit acquires product location information, inventory status, shelf layout, and the like from a database. The in-warehouse information collection unit can also use drones to update product location information in real time. For example, drones are equipped with high-precision cameras and sensors that read product barcodes or QR codes to acquire accurate location information. The analysis unit analyzes the in-warehouse information collected by the in-warehouse information collection unit. For example, the analysis unit calculates an optimal picking route based on the product location information and inventory data. The analysis unit can also use physical characteristics of the products, such as their weight and size, for analysis. For example, a weight sensor installed on a shelf measures the weight of the product, and a size sensor measures the product dimensions. The visual information generation unit generates visual information based on the information analyzed by the analysis unit. For example, the visual information generation unit generates a graphical display showing the product location and the picking route. The visual information generation unit can also generate visual information including detailed product information (e.g., manufacturing date, expiration date). The display unit displays the visual information generated by the visual information generation unit on the AR glasses. For example, when a worker wears the AR glasses, the display unit displays the location of the product and the picking route in the worker's field of view. The display unit can also indicate to the worker the location of the next product to be picked by using arrows or highlights displayed on the AR glasses. In this way, the picking operation navigation system according to the embodiment can assist the worker in picking products efficiently. For example, by wearing the AR glasses, the worker is visually guided and can pick products efficiently. Furthermore, Gemini can calculate the optimal route, thereby reducing unnecessary movements and further improving work efficiency.

[0052] The warehouse information collection unit can acquire product location information, inventory status, and shelf layout from the database. The warehouse information collection unit acquires, for example, product location information, inventory status, and shelf layout from the database. For example, product location information can be acquired based on GPS data or RFID tag information. In addition, inventory status can be acquired based on real-time inventory data or periodic inventory check results. Furthermore, shelf layout can be acquired based on shelf layout diagrams and layout data. In this way, by acquiring product location information, inventory status, and shelf layout from the database, the efficiency of picking work can be improved.

[0053] When a worker wears the AR glasses, the display unit can display the product location and picking route in the worker's field of view. For example, when a worker wears the AR glasses, the display unit displays the product location and picking route in the worker's field of view. For example, the product location is displayed graphically on the AR glasses' display, and the picking route is indicated by arrows and lines. The display unit can also display physical characteristics of the product, such as weight and size. For example, sensors that automatically measure the weight and size of products can be installed in the warehouse and the display can be based on the collected data. This allows workers to visually check the product location and picking route, allowing them to perform picking work efficiently.

[0054] The display unit can use arrows and highlights displayed on the AR glasses to indicate to the worker the location of the next item to be picked. The display unit can, for example, use arrows and highlights displayed on the AR glasses to indicate to the worker the location of the next item to be picked. For example, the arrow indicates the direction of the item, and the highlight emphasizes the location of the item. The display unit can also use an emotion estimation function to monitor the worker's stress level, collect information on areas with high stress, and reflect this information in the analysis. For example, to monitor the worker's stress level, the worker wears a wearable device that measures heart rate and electrodermal activity. This can support efficient picking work by indicating the location of the next item to be picked to the worker using arrows and highlights.

[0055] The display unit can display a confirmation message on the AR glasses when scanning a product barcode. For example, the display unit displays a confirmation message on the AR glasses when scanning a product barcode. For example, when a barcode scanner reads a product barcode, a confirmation message is displayed on the display of the AR glasses. The display unit can also change the content of the confirmation message depending on the type of barcode and the scanning method. For example, a scanner compatible with one-dimensional barcodes and two-dimensional barcodes can be used. This allows for accurate picking by displaying a confirmation message when scanning a product barcode.

[0056] The display unit can add the manufacturing date and expiration date as detailed product information, allowing the worker to check the necessary information at a glance. The display unit can add, for example, the manufacturing date and expiration date as detailed product information, allowing the worker to check the necessary information at a glance. For example, when the worker directs his or her gaze toward the product, the manufacturing date and expiration date are displayed on the display of the AR glasses. The display unit can also retrieve detailed product information from a database and display it in real time. In this way, by adding detailed product information, the worker can check the necessary information at a glance.

[0057] The display unit can use the worker's gaze tracking technology to display information at the optimal position according to the worker's gaze movement. The display unit can use the worker's gaze tracking technology to display information at the optimal position according to the worker's gaze movement, for example. For example, an eye-tracking sensor can be installed in the AR glasses to track the worker's gaze. Product information can be displayed where the worker directs their gaze. The display unit can also dynamically change the display content according to the worker's gaze movement. For example, the display content can be adjusted based on the direction of the gaze and the speed at which the viewpoint moves. This allows the worker's efficiency to be improved by using gaze tracking technology to display information at the optimal position according to the worker's gaze movement.

[0058] The display unit uses the emotion estimation function to adjust the display method of visual information according to the emotional state of the worker, thereby reducing stress. The display unit, for example, uses the emotion estimation function to adjust the display method of visual information according to the emotional state of the worker, thereby reducing stress. For example, the display unit analyzes the facial expressions and voice of the worker to estimate the emotional state. The display unit adjusts the color and design of the information displayed on the AR glasses according to the emotional state. The display unit can also use the emotion estimation function to monitor the stress level of the worker, collect information on areas with high stress, and reflect this in the analysis. As a result, stress can be reduced by using the emotion estimation function to adjust the display method of visual information according to the emotional state of the worker.

[0059] The display unit can add audio guidance and navigate the worker both visually and audibly. The display unit can add audio guidance and navigate the worker both visually and audibly. For example, an audio guidance function can be added to AR glasses and linked to visual information. Detailed product information and picking instructions can be provided by voice for the product the worker is looking at. The display unit can also dynamically change the content of the audio guidance. For example, the content of the audio guidance can be adjusted depending on the importance and priority of the product. In this way, adding audio guidance can navigate the worker both visually and audibly, supporting efficient work.

[0060] The display unit allows multiple workers to share information in real time, thereby promoting teamwork. The display unit, for example, allows multiple workers to share information in real time, thereby promoting teamwork. For example, using AR glasses, information about products picked by a worker can be displayed on the AR glasses of other workers, allowing the entire team to understand the progress of the work. The display unit also updates information in real time, facilitating smooth communication between workers. This allows multiple workers to share information in real time, thereby promoting teamwork.

[0061] The display unit can use the emotion estimation function to change the color and design of visual information based on the worker's emotional state, thereby improving work efficiency. The display unit can use the emotion estimation function to change the color and design of visual information based on the worker's emotional state, thereby improving work efficiency. For example, the display unit can analyze the worker's facial expressions and voice to estimate the worker's emotional state. The display unit can also change the color and design of information displayed on the AR glasses depending on the worker's emotional state using the emotion estimation function, collect information on areas with high stress, and reflect this information in the analysis. This can improve work efficiency by using the emotion estimation function to change the color and design of visual information based on the worker's emotional state.

[0062] The display unit can change the color of the arrows and highlights displayed on the AR glasses according to the importance and priority of the product. For example, the display unit changes the color of the arrows and highlights displayed on the AR glasses according to the importance and priority of the product. For example, red arrows are displayed for high-importance products, and blue arrows are displayed for low-importance products. The display unit can also dynamically change the display content based on the importance and priority of the product. For example, the display unit evaluates the importance of the product based on sales data and inventory status, and evaluates the priority based on the shipping deadline and customer importance. This allows workers to perform picking work efficiently by changing the color of the arrows and highlights according to the importance and priority of the product.

[0063] The display unit monitors the progress of picking work in real time, and if a delay occurs, Gemini can automatically propose the optimal alternative route. The display unit, for example, monitors the progress of picking work in real time, and if a delay occurs, Gemini can automatically propose the optimal alternative route. For example, the display unit collects information on products picked by workers in real time to understand the progress. Furthermore, if a delay occurs, Gemini can calculate the optimal alternative route and display it on the AR glasses. For example, the display unit proposes an alternative route using a shortest path algorithm or a route based on priority. This makes it possible to monitor the progress of picking work in real time and propose the optimal alternative route if a delay occurs, thereby improving work efficiency.

[0064] The display unit uses the emotion estimation function to provide a navigation method according to the emotional state of the worker, thereby maintaining the worker's motivation. The display unit, for example, uses the emotion estimation function to provide a navigation method according to the emotional state of the worker, thereby maintaining the worker's motivation. For example, the display unit analyzes the worker's facial expressions and voice to estimate the emotional state. The navigation method is adjusted according to the emotional state. The display unit can also use the emotion estimation function to monitor the worker's stress level, collect information on areas with high stress, and reflect this in the analysis. In this way, the emotion estimation function can be used to provide a navigation method according to the worker's emotional state, thereby maintaining the worker's motivation.

[0065] The display unit can realize multimodal navigation by adding feedback such as vibration and sound in addition to the information displayed on the AR glasses. For example, the display unit can realize multimodal navigation by adding feedback such as vibration and sound in addition to the information displayed on the AR glasses. For example, a vibration feedback function can be added to the AR glasses and linked to visual information. When a worker looks in the direction they should go next, the AR glasses vibrate to indicate the direction. The display unit can also add an audio feedback function and link to visual information. For example, detailed product information and picking instructions can be provided by audio for each product the worker looks at. In this way, adding feedback such as vibration and sound can realize multimodal navigation and improve worker efficiency.

[0066] The display unit can display the location information of other workers and provide information to avoid collisions and congestion. For example, the display unit can display the location information of other workers and provide information to avoid collisions and congestion. For example, the display unit can display the location information of other workers on AR glasses, and if there is another worker in the direction the worker is looking, the display unit can display the location information and issue a warning. The display unit can also update the location information of other workers in real time and suggest routes to avoid congestion. For example, the display unit can suggest methods for measuring congestion levels and routes to avoid congestion. By displaying the location information of other workers, the display unit can provide information to avoid collisions and congestion, improving work efficiency.

[0067] The display unit uses the emotion estimation function to adjust the frequency and level of detail of navigation based on the emotional state of the worker, thereby reducing the burden on the worker. The display unit, for example, uses the emotion estimation function to adjust the frequency and level of detail of navigation based on the emotional state of the worker, thereby reducing the burden on the worker. For example, the display unit analyzes the facial expression and voice of the worker to estimate the emotional state. The frequency and level of detail of navigation are adjusted according to the emotional state. The display unit can also use the emotion estimation function to monitor the stress level of the worker, collect information on areas with high stress, and reflect this in the analysis. In this way, the burden on the worker can be reduced by using the emotion estimation function to adjust the frequency and level of detail of navigation based on the emotional state of the worker.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The picking work navigation system can be equipped with a health management unit that monitors the health status of workers. For example, the health management unit can measure the worker's heart rate and body temperature in real time and issue an alert if an abnormality is detected. The health management unit can also evaluate the worker's fatigue level and send a notification encouraging them to take a break. Furthermore, the health management unit can record the worker's health data over the long term and generate reports for health management. This allows the system to monitor the worker's health status and encourage them to take a break at the appropriate time, thereby improving work efficiency.

[0070] The picking work navigation system can be equipped with an environmental adjustment unit that adjusts the music and lighting in the work environment based on the emotional state of the worker. For example, if the worker's emotional state is relaxed, soft music is played and the lighting is softened. If the worker is feeling stressed, relaxing music is played and the lighting is adjusted to reduce stress. Furthermore, the environmental adjustment unit can also adjust the temperature and humidity of the work environment according to the worker's emotional state. This allows for improved work efficiency by adjusting the work environment based on the worker's emotional state.

[0071] The picking work navigation system can include a motion analysis unit that analyzes the motions of the worker and suggests efficient motions. For example, the motion analysis unit records the worker's motions using a camera or sensor and analyzes efficient motion patterns. The motion analysis unit can also suggest efficient motions to the worker and provide advice on reducing unnecessary motions. Furthermore, the motion analysis unit can create a training program based on the worker's motion data and help improve the worker's skills. In this way, work efficiency can be improved by analyzing the worker's motions and suggesting efficient motions.

[0072] The picking task navigation system can include a difficulty adjustment unit that adjusts the difficulty of tasks based on the emotional state of the worker. For example, if the worker is feeling stressed, easy tasks are assigned preferentially, and if the worker is relaxed, more difficult tasks are assigned. The difficulty adjustment unit can also adjust the order and timing of tasks according to the worker's emotional state. Furthermore, the difficulty adjustment unit can reduce the burden on the worker by monitoring the worker's emotional state and adjusting the difficulty of the tasks at appropriate times. This allows for improved work efficiency by adjusting the difficulty of the tasks based on the worker's emotional state.

[0073] The picking task navigation system may include a feedback unit that provides real-time feedback on the progress of the task based on the worker's emotional state. For example, if the worker is feeling stressed, brief feedback on the progress of the task may be provided, whereas if the worker is relaxed, detailed feedback may be provided. The feedback unit may also adjust the frequency and content of the feedback depending on the worker's emotional state. Furthermore, the feedback unit may monitor the worker's emotional state and provide feedback at appropriate times to maintain the worker's motivation. This allows for improved work efficiency by providing feedback on the progress of the task based on the worker's emotional state.

[0074] The picking work navigation system can be equipped with a motion recording unit that can record the motions of workers and play them back for later review. For example, the motion recording unit can record the motions of workers using a camera or sensor and play them back for later review. The motion recording unit can also analyze the motions of workers and suggest efficient motion patterns. Furthermore, the motion recording unit can create training programs based on the motion data of workers and help improve their skills. This allows for work efficiency to be improved by recording the motions of workers and playing them back for later review.

[0075] The picking task navigation system can include a priority adjustment unit that adjusts task priorities based on the emotional state of the worker. For example, if the worker is stressed, a low-priority task is performed first, and if the worker is relaxed, a high-priority task is performed first. The priority adjustment unit can also adjust the order and timing of tasks according to the worker's emotional state. Furthermore, the priority adjustment unit can reduce the burden on the worker by monitoring the worker's emotional state and adjusting the task priorities at the appropriate time. This allows for improved work efficiency by adjusting task priorities based on the worker's emotional state.

[0076] The picking work navigation system can be equipped with a motion evaluation unit that evaluates the motion of the worker in real time and encourages efficient motion. For example, the motion evaluation unit evaluates the motion of the worker in real time using a camera or sensor and provides feedback to encourage efficient motion. The motion evaluation unit can also create a training program based on the motion data of the worker to help improve the worker's skills. Furthermore, the motion evaluation unit can record the motion of the worker and play it back for later review. This allows the system to evaluate the motion of the worker in real time and encourage efficient motion, thereby improving work efficiency.

[0077] The picking work navigation system can include a progress sharing unit that shares work progress in real time based on the worker's emotional state. For example, if the worker is feeling stressed, the progress sharing unit can share a brief summary of the progress, and if the worker is relaxed, the progress sharing unit can share detailed progress. The progress sharing unit can also adjust the frequency and content of progress sharing depending on the worker's emotional state. Furthermore, the progress sharing unit can monitor the worker's emotional state and share the progress at appropriate times to maintain the worker's motivation. This allows work efficiency to be improved by sharing work progress based on the worker's emotional state.

[0078] The picking work navigation system can be equipped with a motion simulation unit that simulates the motions of a worker and proposes optimal motions. For example, the motion simulation unit simulates the motions of a worker and proposes optimal motion patterns. The motion simulation unit can also create training programs based on the motion data of the worker to help improve the worker's skills. Furthermore, the motion simulation unit can record the worker's motions and play them back for later review. This allows for improved work efficiency by simulating the worker's motions and proposing optimal motions.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The warehouse information collection unit collects warehouse information. For example, it obtains product location information, inventory status, shelf layout, and other information from a database. Product location information can also be updated in real time using drones. Drones are equipped with high-precision cameras and sensors, and obtain accurate location information by reading product barcodes and QR codes. Step 2: The analysis unit analyzes the warehouse information collected by the warehouse information collection unit. For example, it calculates the optimal picking route based on product location information and inventory data. Physical characteristics such as product weight and size can also be used in the analysis. Weight sensors installed on shelves measure the weight of products, and size sensors measure the product dimensions. Step 3: The visual information generation unit generates visual information based on the information analyzed by the analysis unit. For example, it generates a graphical display showing the product location and picking route. It can also generate visual information including detailed product information (e.g., manufacturing date, expiration date). Step 4: The display unit displays the visual information generated by the visual information generation unit on the AR glasses. For example, when a worker wears the AR glasses, the location of the product and the picking route are displayed in the worker's field of vision. In addition, arrows and highlights displayed on the AR glasses can be used to show the worker the location of the next product to be picked.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] 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.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an in-warehouse information collecting unit that collects in-warehouse information; An analysis unit that analyzes the in-fridge information collected by the in-fridge information collection unit; a visual information generation unit that generates visual information based on the information analyzed by the analysis unit; a display unit that displays the visual information generated by the visual information generation unit on the AR glasses. A system characterized by:

2. The in-warehouse information collection unit Obtain product location information, inventory status, and shelf layout from a database 2. The system of claim 1.

3. The display unit Add manufacturing date and expiration date as product details so workers can see the necessary information at a glance 2. The system of claim 1.

4. The display unit Sharing the information among multiple workers in real time promotes teamwork 2. The system of claim 1.

5. The display unit The color of the arrow and highlight displayed on the AR glasses is changed according to the importance and priority of the product.

2. The system of claim 1.

6. The display unit The display method of the visual information is adjusted according to the emotional state of the worker, thereby reducing stress.

2. The system of claim 1.

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