system

Abstract

The system according to this embodiment aims to streamline inventory management within a warehouse and generate an optimal picking sequence. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a navigation unit. The collection unit collects data within the warehouse. The analysis unit analyzes the data collected by the collection unit and monitors inventory fluctuations. The generation unit generates the optimal picking sequence based on the data analyzed by the analysis unit. The navigation unit shows the route to the worker based on the picking list generated by the generation unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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[0003] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is room for improvement in inventory management in a warehouse and the efficiency of picking operations.

[0005] The system according to the embodiment aims to improve inventory management in a warehouse and generate an optimal picking order.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a navigation unit. The collection unit collects data within the warehouse. The analysis unit analyzes the data collected by the collection unit and monitors inventory fluctuations. The generation unit generates the optimal picking sequence based on the data analyzed by the analysis unit. The navigation unit shows the route to the worker based on the picking list generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline inventory management in a warehouse and generate an optimal picking sequence. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The warehouse management system according to an embodiment of the present invention is an innovative system that automates warehouse management by utilizing generative AI. This warehouse management system grasps the inventory status in the warehouse in real time and proposes an efficient picking route. Specifically, based on data obtained from sensors in the warehouse, the AI ​​monitors inventory fluctuations and generates the optimal picking order. Furthermore, when an operator gives instructions to the system via a smartphone or tablet, the AI ​​automatically creates a picking list and shows the route to collect the necessary inventory items. This process resolves issues such as labor shortages and work efficiency, and improves the overall productivity of operations. For example, the AI ​​collects data obtained from sensors in the warehouse and monitors inventory fluctuations. For example, it collects environmental data such as temperature and humidity, as well as information such as the location and quantity of inventory items in real time. This allows the inventory status to be constantly monitored. Next, the AI ​​analyzes the collected data and generates the optimal picking order. For example, it performs ABC analysis and determines the picking order based on the importance of inventory items. This allows for the proposal of an efficient picking route. Furthermore, when an operator gives instructions to the system via a smartphone or tablet, the AI ​​automatically creates a picking list. For example, if a worker gives instructions such as "Please pick product A, product B, and product C," the AI ​​will create a picking list based on those instructions. Finally, based on the picking list created by the AI, it will show a route to collect the necessary inventory items. For instance, when a worker moves around the warehouse with a smartphone or tablet, the AI ​​will navigate them along the optimal route. This reduces unnecessary movements and allows for more efficient work. This process addresses issues such as labor shortages and work efficiency, improving overall business productivity. For example, traditional manual inventory management was prone to problems such as stockouts and excess inventory, but these problems are resolved through AI automation. In addition, efficient picking route suggestions shorten working time and reduce the burden on workers. Furthermore, as the AI ​​continues to learn from past data, its accuracy improves over time, leading to more effective operation.This allows the warehouse management system to monitor inventory levels in the warehouse in real time and suggest efficient picking routes.

[0029] The warehouse management system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a navigation unit. The collection unit collects data within the warehouse. The collection unit collects environmental data such as temperature and humidity, and information such as the location and quantity of inventory items in real time. The collection unit can collect environmental data using, for example, temperature sensors and humidity sensors. The collection unit can also use RFID tags or barcode scanners to determine the location of inventory items. The collection unit can, for example, monitor the temperature inside the warehouse in real time using a temperature sensor and collect data. It can monitor the humidity inside the warehouse in real time using a humidity sensor and collect data. It can identify the location of inventory items using RFID tags and collect data. The analysis unit analyzes the data collected by the collection unit and monitors inventory fluctuations. The analysis unit can, for example, analyze the collected temperature data and humidity data to monitor the storage environment of inventory items. The analysis unit can also analyze the location data of inventory items and monitor inventory fluctuations. The analysis unit can, for example, analyze temperature data to monitor temperature fluctuations. It can analyze humidity data to monitor humidity fluctuations. The system analyzes the location data of inventory items and monitors inventory fluctuations. The generation unit generates the optimal picking order based on the data analyzed by the analysis unit. The generation unit can, for example, perform ABC analysis and determine the picking order based on the importance of inventory items. The generation unit can also generate efficient picking routes based on the location data of inventory items. The generation unit can, for example, perform ABC analysis and generate the picking order based on the importance of inventory items. The generation unit generates efficient picking routes based on the location data of inventory items. The generation unit can generate the optimal picking order using generation AI. The navigation unit shows the route to the worker based on the picking list generated by the generation unit. The navigation unit can, for example, navigate the worker to the optimal route using a smartphone or tablet. The navigation unit can, for example, display the optimal route to the worker using a smartphone application. It can display the optimal route to the worker using a tablet application. The navigation unit can navigate the worker to the optimal route using generation AI.This enables warehouse management to automate and streamline warehouse operations by collecting and analyzing data within the warehouse, generating optimal picking sequences, and showing workers routes.

[0030] The data collection unit collects data from within the warehouse. For example, it collects environmental data such as temperature and humidity, as well as information such as the location and quantity of inventory items, in real time. Specifically, temperature and humidity sensors are placed throughout the warehouse, and the data acquired by these sensors is transmitted to a central database. The temperature sensors monitor the warehouse temperature in real time and can immediately issue an alert if abnormal temperature fluctuations occur. Similarly, the humidity sensors monitor the warehouse humidity in real time and provide data to maintain an appropriate humidity range. This ensures that inventory items are always stored in the appropriate environment. The data collection unit also uses RFID tags and barcode scanners to determine the location of inventory items. RFID tags are attached to each item, and an RFID reader reads the information on the tag to identify the item's location. Barcode scanners read barcodes attached to inventory items to obtain information about them. This allows the data collection unit to accurately determine the location and quantity of inventory items, improving the accuracy of inventory management. Furthermore, the data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data collected by the collection unit and monitors inventory fluctuations. For example, the analysis unit can analyze collected temperature and humidity data to monitor the storage environment of inventory items. Specifically, it can analyze temperature data and monitor temperature fluctuations. For example, if the temperature exceeds a certain range, it can issue an alert and take appropriate measures. It can also analyze humidity data and monitor humidity fluctuations. For example, if the humidity is too high, it can take measures such as operating a dehumidifier. The analysis unit can also analyze inventory location data and monitor inventory fluctuations. For example, if inventory items are moved, it can analyze the location data to understand inventory fluctuations in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term inventory management and trend analysis. For example, based on historical inventory data, it can predict inventory fluctuations in specific seasons or events and formulate future inventory management plans. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term inventory management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates the optimal picking order based on the data analyzed by the analysis unit. For example, the generation unit can perform ABC analysis and determine the picking order based on the importance of inventory items. Specifically, inventory items are classified into three categories: A, B, and C, with category A items being the most important and to be picked frequently. Category B items are of medium importance, and category C items are considered the least important. This allows the generation unit to generate an efficient picking order based on the importance of inventory items. The generation unit can also generate an efficient picking route based on inventory item location data. For example, it can calculate the shortest picking route considering the location of inventory items within the warehouse. This minimizes the distance workers travel and improves the efficiency of picking operations. Furthermore, the generation unit can generate the optimal picking order using generation AI. The generation AI learns from past picking and inventory data and automatically generates the most efficient picking order. This allows the generation unit to always provide the optimal picking order based on the latest data, thereby improving the efficiency of warehouse management.

[0033] The navigation unit guides workers along a route based on the picking list generated by the generation unit. The navigation unit can, for example, navigate workers along the optimal route using a smartphone or tablet. Specifically, it displays the optimal route to workers using a smartphone application. The application, based on the picking list generated by the generation unit, shows workers a route that allows them to perform picking tasks efficiently. For example, a worker can move through the warehouse following the indicated route while viewing their smartphone screen and perform picking tasks. It can also display the optimal route to workers using a tablet application. The larger screen of a tablet allows for the display of more detailed route information and picking lists. Furthermore, the navigation unit can use generation AI to navigate the optimal route. The generation AI calculates the most efficient route based on real-time updated inventory data and worker location data, and provides instructions to the worker. This allows the navigation unit to provide workers with quick and accurate route guidance, improving the efficiency of picking operations. Additionally, the navigation unit can collect worker feedback and continuously improve the accuracy and effectiveness of route guidance. For example, it can revise route guidance and improve instructions based on worker feedback. Furthermore, the navigation unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the navigation unit to provide workers with quick and reliable route guidance, improving the efficiency of picking operations.

[0034] The data collection unit can collect environmental data such as temperature and humidity. For example, the data collection unit can use a temperature sensor to monitor the temperature inside the warehouse in real time and collect data. The data collection unit can also use a humidity sensor to monitor the humidity inside the warehouse in real time and collect data. The data collection unit can also use a pressure sensor to monitor the air pressure inside the warehouse in real time and collect data. By collecting environmental data in this way, it is possible to provide information necessary for inventory management in the warehouse. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from the temperature sensor into a generating AI and have the generating AI perform analysis of the temperature data.

[0035] The analysis unit can perform ABC analysis based on the collected data. For example, the analysis unit classifies inventory items into three categories, A, B, and C, based on the collected inventory data. The analysis unit can perform ABC analysis based on the importance of the inventory items to achieve efficient management. For example, the analysis unit can perform ABC analysis based on sales data of inventory items and classify high-importance inventory items into category A. The analysis unit can perform ABC analysis based on inventory turnover data and classify medium-importance inventory items into category B. The analysis unit can perform ABC analysis based on storage cost data of inventory items and classify low-importance inventory items into category C. In this way, by performing ABC analysis, efficient management based on the importance of inventory items becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected inventory data into a generating AI and have the generating AI perform ABC analysis.

[0036] The generation unit can generate the optimal picking order using a generation AI. For example, the generation unit can use the generation AI to generate the optimal picking order based on the importance and location data of inventory items. The generation unit can also use the generation AI to generate an efficient picking route. For example, the generation unit can use the generation AI to generate a picking order based on the importance of inventory items. The generation unit can use the generation AI to generate an efficient picking route based on the location data of inventory items. The generation unit can also use the generation AI to generate the optimal picking order based on the expiration date data of inventory items. In this way, by using the generation AI, the optimal picking order can be generated and work efficiency can be improved. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input inventory importance data into the generation AI and have the generation AI perform the generation of the optimal picking order.

[0037] The navigation unit can guide workers along the optimal route when they move around the warehouse with a smartphone or tablet. For example, the navigation unit can display the optimal route to the worker using a smartphone application. The navigation unit can also display the optimal route to the worker using a tablet application. The navigation unit can use generative AI to navigate along the optimal route. For example, the navigation unit can use the GPS function of a smartphone to determine the worker's location and display the optimal route. The navigation unit can use the GPS function of a tablet to determine the worker's location and display the optimal route. The navigation unit can use generative AI to generate the optimal route based on the worker's location data and navigate accordingly. This improves work efficiency by guiding workers to move efficiently within the warehouse. Some or all of the above-described processes in the navigation unit may be performed using AI, or not. For example, the navigation unit can input the worker's location data into the generative AI and have the generative AI generate the optimal route.

[0038] The generation unit can create a picking list based on the worker's instructions. For example, if a worker gives instructions via smartphone or tablet, such as "Please pick product A, product B, and product C," the generation unit will create a picking list based on those instructions. The generation unit can also create a picking list based on the worker's instructions using generation AI. For example, if a worker gives voice instructions, the generation unit will use voice recognition technology to convert the instructions into text data and create a picking list. The generation unit can also receive instructions from a worker using touch panel operation and create a picking list. The generation unit can create a picking list based on the worker's instructions using generation AI. This improves the accuracy and efficiency of the work by creating a picking list based on the worker's instructions. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the worker's voice instructions into the generation AI and have the generation AI create the picking list.

[0039] The data collection unit can perform dynamic filtering to prioritize the collection of data from specific areas within the warehouse. For example, the data collection unit can prioritize the collection of data from areas where high-value goods are stored. The data collection unit can prioritize the collection of data from areas with large fluctuations in temperature and humidity. The data collection unit can prioritize the collection of data from areas where worker traffic is concentrated. By prioritizing the collection of data from specific areas, important information can be efficiently obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from a specific area into a generating AI and have the generating AI perform dynamic filtering.

[0040] The data collection unit can be equipped with a function to detect sensor abnormalities during data collection and automatically notify when an abnormality occurs. For example, the data collection unit can detect and notify an abnormality when sensor data exceeds a certain range. The data collection unit can detect and notify an abnormality when sensor communication is interrupted. The data collection unit can detect and notify an abnormality when the sensor battery level drops low. This enables a rapid response by automatically detecting and notifying of sensor abnormalities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sensor data into a generating AI and have the generating AI perform abnormality detection.

[0041] The data collection unit can integrate data from different sensors within the warehouse to generate comprehensive environmental data. For example, the data collection unit can integrate data from temperature and humidity sensors to generate comprehensive environmental data. The data collection unit can integrate data from illuminance and sound sensors to generate comprehensive environmental data. The data collection unit can integrate data from vibration and pressure sensors to generate comprehensive environmental data. In this way, comprehensive environmental data can be provided by integrating data from different sensors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from different sensors into a generating AI and have the generating AI perform the generation of comprehensive environmental data.

[0042] The data collection unit can refer to weather data outside the warehouse during data collection and consider factors that affect inventory management. For example, during rainy weather, the data collection unit can consider the effects of humidity during data collection. During sunny weather, the data collection unit can consider the effects of temperature during data collection. On snowy days, the data collection unit can consider temperature fluctuations inside the warehouse during data collection. This allows for the identification of factors affecting inventory management by considering weather data, enabling appropriate responses. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather data into a generating AI and have the generating AI consider factors that affect inventory management.

[0043] The analysis unit can predict inventory fluctuations by referring to past inventory data during analysis. For example, the analysis unit can predict seasonal inventory fluctuations based on past inventory data. The analysis unit can predict inventory fluctuations for a specific product category based on past inventory data. The analysis unit can predict inventory fluctuations over a specific period based on past inventory data. This makes it possible to predict inventory fluctuations by referring to past inventory data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past inventory data into a generating AI and have the generating AI perform inventory fluctuation predictions.

[0044] The analysis unit can be equipped with a function to detect abnormal inventory fluctuations during analysis and automatically issue alerts when an anomaly occurs. For example, the analysis unit can detect an anomaly and issue an alert when inventory decreases rapidly. The analysis unit can detect an anomaly and issue an alert when inventory increases rapidly. The analysis unit can detect an anomaly and issue an alert when inventory fluctuations exceed the normal range. This enables a rapid response by automatically detecting abnormal inventory fluctuations and issuing alerts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input inventory data into a generating AI and have the generating AI perform anomaly detection.

[0045] The analysis unit can integrate inventory data and sales data during analysis to optimize inventory management and sales strategies. For example, the analysis unit can integrate inventory data and sales data to perform demand forecasting. The analysis unit can integrate inventory data and sales data to optimize the timing of inventory replenishment. The analysis unit can integrate inventory data and sales data to analyze the effectiveness of sales promotion campaigns. As a result, integrating inventory data and sales data enables the optimization of inventory management and sales strategies. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input inventory data and sales data into a generating AI and have the generating AI perform the analysis of the integrated data.

[0046] The analysis unit can improve work efficiency by referring to worker movement data within the warehouse during analysis. For example, the analysis unit can propose efficient picking routes based on worker movement data. The analysis unit can optimize the layout of the work area based on worker movement data. The analysis unit can propose improvement measures to reduce the burden on workers based on worker movement data. In this way, work efficiency can be improved by referring to worker movement data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input worker movement data into a generating AI and have the generating AI propose efficient picking routes.

[0047] The generation unit can optimize the picking order by considering the expiration dates of inventory items during generation. For example, the generation unit can generate an order that prioritizes picking inventory items with approaching expiration dates. The generation unit can generate an order that postpones picking inventory items with longer expiration dates. The generation unit can generate an order that balances the picking of inventory items with different expiration dates. This improves the efficiency of inventory management by considering the expiration dates of inventory items. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input inventory expiration date data into a generation AI and have the generation AI perform the optimization of the picking order.

[0048] The generation unit can be equipped with a function to learn a more efficient picking sequence by referring to past picking data during generation. For example, the generation unit can learn an efficient picking sequence based on past picking data. The generation unit can learn a sequence that optimizes the worker's movement based on past picking data. The generation unit can learn a sequence that optimizes the placement of inventory items based on past picking data. As a result, by referring to past picking data, it is possible to learn an efficient picking sequence and improve work efficiency. Some or all of the above processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input past picking data into a generation AI and have the generation AI perform the learning of an efficient picking sequence.

[0049] The generation unit can optimize the picking order by considering the size and weight of the inventory items during generation. For example, the generation unit can generate an order that prioritizes picking large and heavy inventory items. The generation unit can generate an order that postpones picking small and light inventory items. The generation unit can generate an order that balances picking according to size and weight. In this way, an efficient picking order can be generated by considering the size and weight of the inventory items. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input inventory size and weight data into a generation AI and have the generation AI perform the optimization of the picking order.

[0050] The generation unit can adjust the picking order during generation, taking into account the schedules of multiple workers. For example, the generation unit can adjust the picking order so that multiple workers can work simultaneously. The generation unit can adjust the picking order, taking into account the workers' break times. The generation unit can generate an efficient picking order that matches the workers' schedules. In this way, an efficient picking order can be generated by considering the schedules of multiple workers. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input worker schedule data into a generation AI and have the generation AI perform the adjustment of the picking order.

[0051] The navigation unit can reflect real-time information about obstacles within the warehouse during navigation and present the optimal route. For example, the navigation unit can acquire real-time information about obstacles within the warehouse and present the optimal route. The navigation unit can also present detour routes, taking into account the location of obstacles. The navigation unit can reflect the movement status of obstacles in real time and present the optimal route. In this way, by reflecting real-time information about obstacles within the warehouse, the optimal route can be presented. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input obstacle information into a generating AI and have the generating AI perform the generation of the optimal route.

[0052] The navigation unit can be equipped with a function to learn more efficient routes by referring to the worker's past travel history during navigation. For example, the navigation unit can learn efficient routes based on the worker's past travel history. The navigation unit can learn routes that avoid congestion based on the worker's past travel history. The navigation unit can learn the shortest route based on the worker's past travel history. This allows for the learning of efficient routes and improved work efficiency by referring to the worker's past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the worker's travel history data into a generating AI and have the generating AI perform the learning of efficient routes.

[0053] The navigation unit can present highly visible routes during navigation, taking into account the lighting conditions within the warehouse. For example, the navigation unit can acquire the lighting conditions within the warehouse in real time and present highly visible routes. The navigation unit can present routes that avoid dimly lit areas. The navigation unit can present the optimal route according to the brightness of the lighting. In this way, by taking into account the lighting conditions within the warehouse, it is possible to present highly visible routes. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input lighting condition data into a generating AI and have the generating AI perform the generation of highly visible routes.

[0054] The navigation unit can monitor the worker's health condition during navigation and suggest a less strenuous route. For example, the navigation unit can monitor the worker's health condition in real time and suggest a less strenuous route. The navigation unit can suggest a route that includes rest points, taking into account the worker's fatigue level. The navigation unit can suggest the optimal route according to the worker's health condition. In this way, by monitoring the worker's health condition, a less strenuous route can be suggested. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input worker health data into a generating AI and have the generating AI generate a less strenuous route.

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

[0056] The analysis unit can manage inventory by considering the expiration dates of stock items based on the collected data. For example, it can prioritize managing stock items with approaching expiration dates and postpone managing stock items with longer expiration dates. This improves the efficiency of inventory management by considering the expiration dates of stock items.

[0057] The generation unit can use generation AI to optimize the picking order, taking into account the size and weight of inventory items. For example, it can generate an order that prioritizes picking large and heavy inventory items, or an order that postpones picking small and light inventory items. In this way, by considering the size and weight of inventory items, an efficient picking order can be generated.

[0058] The navigation unit can reflect real-time information about obstacles within the warehouse and suggest the optimal route. For example, it can acquire real-time information about obstacles within the warehouse and suggest the optimal route. It can also suggest detour routes considering the location of obstacles. In this way, by reflecting real-time information about obstacles within the warehouse, it can suggest the optimal route.

[0059] The analysis unit can manage inventory by considering the expiration dates of stock items based on the collected data. For example, it can prioritize managing stock items with approaching expiration dates and postpone managing stock items with longer expiration dates. This improves the efficiency of inventory management by considering the expiration dates of stock items.

[0060] The generation unit can use generation AI to optimize the picking order, taking into account the size and weight of inventory items. For example, it can generate an order that prioritizes picking large and heavy inventory items, or an order that postpones picking small and light inventory items. In this way, by considering the size and weight of inventory items, an efficient picking order can be generated.

[0061] The navigation unit can reflect real-time information about obstacles within the warehouse and suggest the optimal route. For example, it can acquire real-time information about obstacles within the warehouse and suggest the optimal route. It can also suggest detour routes considering the location of obstacles. In this way, by reflecting real-time information about obstacles within the warehouse, it can suggest the optimal route.

[0062] The analysis unit can manage inventory by considering the expiration dates of stock items based on the collected data. For example, it can prioritize managing stock items with approaching expiration dates and postpone managing stock items with longer expiration dates. This improves the efficiency of inventory management by considering the expiration dates of stock items.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects data within the warehouse. The data collection unit collects environmental data such as temperature and humidity, as well as information such as the location and quantity of inventory items, in real time. The data collection unit can collect environmental data using temperature and humidity sensors. The data collection unit can also use RFID tags or barcode scanners to determine the location of inventory items. Step 2: The analysis unit analyzes the data collected by the collection unit and monitors inventory fluctuations. The analysis unit can analyze the collected temperature and humidity data to monitor the storage environment of the inventory. The analysis unit can also analyze the location data of the inventory to monitor inventory fluctuations. Step 3: The generation unit generates the optimal picking order based on the data analyzed by the analysis unit. The generation unit can perform ABC analysis and determine the picking order based on the importance of the inventory items. The generation unit can also generate an efficient picking route based on the location data of the inventory items. Step 4: The navigation unit shows the worker the route based on the picking list generated by the generation unit. The navigation unit can navigate the worker to the optimal route using a smartphone or tablet.

[0065] (Example of form 2) The warehouse management system according to an embodiment of the present invention is an innovative system that automates warehouse management by utilizing generative AI. This warehouse management system grasps the inventory status in the warehouse in real time and proposes an efficient picking route. Specifically, based on data obtained from sensors in the warehouse, the AI ​​monitors inventory fluctuations and generates the optimal picking order. Furthermore, when an operator gives instructions to the system via a smartphone or tablet, the AI ​​automatically creates a picking list and shows the route to collect the necessary inventory items. This process resolves issues such as labor shortages and work efficiency, and improves the overall productivity of operations. For example, the AI ​​collects data obtained from sensors in the warehouse and monitors inventory fluctuations. For example, it collects environmental data such as temperature and humidity, as well as information such as the location and quantity of inventory items in real time. This allows the inventory status to be constantly monitored. Next, the AI ​​analyzes the collected data and generates the optimal picking order. For example, it performs ABC analysis and determines the picking order based on the importance of inventory items. This allows for the proposal of an efficient picking route. Furthermore, when an operator gives instructions to the system via a smartphone or tablet, the AI ​​automatically creates a picking list. For example, if a worker gives instructions such as "Please pick product A, product B, and product C," the AI ​​will create a picking list based on those instructions. Finally, based on the picking list created by the AI, it will show a route to collect the necessary inventory items. For instance, when a worker moves around the warehouse with a smartphone or tablet, the AI ​​will navigate them along the optimal route. This reduces unnecessary movements and allows for more efficient work. This process addresses issues such as labor shortages and work efficiency, improving overall business productivity. For example, traditional manual inventory management was prone to problems such as stockouts and excess inventory, but these problems are resolved through AI automation. In addition, efficient picking route suggestions shorten working time and reduce the burden on workers. Furthermore, as the AI ​​continues to learn from past data, its accuracy improves over time, leading to more effective operation.This allows the warehouse management system to monitor inventory levels in the warehouse in real time and suggest efficient picking routes.

[0066] The warehouse management system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a navigation unit. The collection unit collects data within the warehouse. The collection unit collects environmental data such as temperature and humidity, and information such as the location and quantity of inventory items in real time. The collection unit can collect environmental data using, for example, temperature sensors and humidity sensors. The collection unit can also use RFID tags or barcode scanners to determine the location of inventory items. The collection unit can, for example, monitor the temperature inside the warehouse in real time using a temperature sensor and collect data. It can monitor the humidity inside the warehouse in real time using a humidity sensor and collect data. It can identify the location of inventory items using RFID tags and collect data. The analysis unit analyzes the data collected by the collection unit and monitors inventory fluctuations. The analysis unit can, for example, analyze the collected temperature data and humidity data to monitor the storage environment of inventory items. The analysis unit can also analyze the location data of inventory items and monitor inventory fluctuations. The analysis unit can, for example, analyze temperature data to monitor temperature fluctuations. It can analyze humidity data to monitor humidity fluctuations. The system analyzes the location data of inventory items and monitors inventory fluctuations. The generation unit generates the optimal picking order based on the data analyzed by the analysis unit. The generation unit can, for example, perform ABC analysis and determine the picking order based on the importance of inventory items. The generation unit can also generate efficient picking routes based on the location data of inventory items. The generation unit can, for example, perform ABC analysis and generate the picking order based on the importance of inventory items. The generation unit generates efficient picking routes based on the location data of inventory items. The generation unit can generate the optimal picking order using generation AI. The navigation unit shows the route to the worker based on the picking list generated by the generation unit. The navigation unit can, for example, navigate the worker to the optimal route using a smartphone or tablet. The navigation unit can, for example, display the optimal route to the worker using a smartphone application. It can display the optimal route to the worker using a tablet application. The navigation unit can navigate the worker to the optimal route using generation AI.This enables warehouse management to automate and streamline warehouse operations by collecting and analyzing data within the warehouse, generating optimal picking sequences, and showing workers routes.

[0067] The data collection unit collects data from within the warehouse. For example, it collects environmental data such as temperature and humidity, as well as information such as the location and quantity of inventory items, in real time. Specifically, temperature and humidity sensors are placed throughout the warehouse, and the data acquired by these sensors is transmitted to a central database. The temperature sensors monitor the warehouse temperature in real time and can immediately issue an alert if abnormal temperature fluctuations occur. Similarly, the humidity sensors monitor the warehouse humidity in real time and provide data to maintain an appropriate humidity range. This ensures that inventory items are always stored in the appropriate environment. The data collection unit also uses RFID tags and barcode scanners to determine the location of inventory items. RFID tags are attached to each item, and an RFID reader reads the information on the tag to identify the item's location. Barcode scanners read barcodes attached to inventory items to obtain information about them. This allows the data collection unit to accurately determine the location and quantity of inventory items, improving the accuracy of inventory management. Furthermore, the data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0068] The analysis unit analyzes data collected by the collection unit and monitors inventory fluctuations. For example, the analysis unit can analyze collected temperature and humidity data to monitor the storage environment of inventory items. Specifically, it can analyze temperature data and monitor temperature fluctuations. For example, if the temperature exceeds a certain range, it can issue an alert and take appropriate measures. It can also analyze humidity data and monitor humidity fluctuations. For example, if the humidity is too high, it can take measures such as operating a dehumidifier. The analysis unit can also analyze inventory location data and monitor inventory fluctuations. For example, if inventory items are moved, it can analyze the location data to understand inventory fluctuations in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term inventory management and trend analysis. For example, based on historical inventory data, it can predict inventory fluctuations in specific seasons or events and formulate future inventory management plans. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term inventory management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0069] The generation unit generates the optimal picking order based on the data analyzed by the analysis unit. For example, the generation unit can perform ABC analysis and determine the picking order based on the importance of inventory items. Specifically, inventory items are classified into three categories: A, B, and C, with category A items being the most important and to be picked frequently. Category B items are of medium importance, and category C items are considered the least important. This allows the generation unit to generate an efficient picking order based on the importance of inventory items. The generation unit can also generate an efficient picking route based on inventory item location data. For example, it can calculate the shortest picking route considering the location of inventory items within the warehouse. This minimizes the distance workers travel and improves the efficiency of picking operations. Furthermore, the generation unit can generate the optimal picking order using generation AI. The generation AI learns from past picking and inventory data and automatically generates the most efficient picking order. This allows the generation unit to always provide the optimal picking order based on the latest data, thereby improving the efficiency of warehouse management.

[0070] The navigation unit guides workers along a route based on the picking list generated by the generation unit. The navigation unit can, for example, navigate workers along the optimal route using a smartphone or tablet. Specifically, it displays the optimal route to workers using a smartphone application. The application, based on the picking list generated by the generation unit, shows workers a route that allows them to perform picking tasks efficiently. For example, a worker can move through the warehouse following the indicated route while viewing their smartphone screen and perform picking tasks. It can also display the optimal route to workers using a tablet application. The larger screen of a tablet allows for the display of more detailed route information and picking lists. Furthermore, the navigation unit can use generation AI to navigate the optimal route. The generation AI calculates the most efficient route based on real-time updated inventory data and worker location data, and provides instructions to the worker. This allows the navigation unit to provide workers with quick and accurate route guidance, improving the efficiency of picking operations. Additionally, the navigation unit can collect worker feedback and continuously improve the accuracy and effectiveness of route guidance. For example, it can revise route guidance and improve instructions based on worker feedback. Furthermore, the navigation unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the navigation unit to provide workers with quick and reliable route guidance, improving the efficiency of picking operations.

[0071] The data collection unit can collect environmental data such as temperature and humidity. For example, the data collection unit can use a temperature sensor to monitor the temperature inside the warehouse in real time and collect data. The data collection unit can also use a humidity sensor to monitor the humidity inside the warehouse in real time and collect data. The data collection unit can also use a pressure sensor to monitor the air pressure inside the warehouse in real time and collect data. By collecting environmental data in this way, it is possible to provide information necessary for inventory management in the warehouse. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from the temperature sensor into a generating AI and have the generating AI perform analysis of the temperature data.

[0072] The analysis unit can perform ABC analysis based on the collected data. For example, the analysis unit classifies inventory items into three categories, A, B, and C, based on the collected inventory data. The analysis unit can perform ABC analysis based on the importance of the inventory items to achieve efficient management. For example, the analysis unit can perform ABC analysis based on sales data of inventory items and classify high-importance inventory items into category A. The analysis unit can perform ABC analysis based on inventory turnover data and classify medium-importance inventory items into category B. The analysis unit can perform ABC analysis based on storage cost data of inventory items and classify low-importance inventory items into category C. In this way, by performing ABC analysis, efficient management based on the importance of inventory items becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected inventory data into a generating AI and have the generating AI perform ABC analysis.

[0073] The generation unit can generate the optimal picking order using a generation AI. For example, the generation unit can use the generation AI to generate the optimal picking order based on the importance and location data of inventory items. The generation unit can also use the generation AI to generate an efficient picking route. For example, the generation unit can use the generation AI to generate a picking order based on the importance of inventory items. The generation unit can use the generation AI to generate an efficient picking route based on the location data of inventory items. The generation unit can also use the generation AI to generate the optimal picking order based on the expiration date data of inventory items. In this way, by using the generation AI, the optimal picking order can be generated and work efficiency can be improved. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input inventory importance data into the generation AI and have the generation AI perform the generation of the optimal picking order.

[0074] The navigation unit can guide workers along the optimal route when they move around the warehouse with a smartphone or tablet. For example, the navigation unit can display the optimal route to the worker using a smartphone application. The navigation unit can also display the optimal route to the worker using a tablet application. The navigation unit can use generative AI to navigate along the optimal route. For example, the navigation unit can use the GPS function of a smartphone to determine the worker's location and display the optimal route. The navigation unit can use the GPS function of a tablet to determine the worker's location and display the optimal route. The navigation unit can use generative AI to generate the optimal route based on the worker's location data and navigate accordingly. This improves work efficiency by guiding workers to move efficiently within the warehouse. Some or all of the above-described processes in the navigation unit may be performed using AI, or not. For example, the navigation unit can input the worker's location data into the generative AI and have the generative AI generate the optimal route.

[0075] The generation unit can create a picking list based on the worker's instructions. For example, if a worker gives instructions via smartphone or tablet, such as "Please pick product A, product B, and product C," the generation unit will create a picking list based on those instructions. The generation unit can also create a picking list based on the worker's instructions using generation AI. For example, if a worker gives voice instructions, the generation unit will use voice recognition technology to convert the instructions into text data and create a picking list. The generation unit can also receive instructions from a worker using touch panel operation and create a picking list. The generation unit can create a picking list based on the worker's instructions using generation AI. This improves the accuracy and efficiency of the work by creating a picking list based on the worker's instructions. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the worker's voice instructions into the generation AI and have the generation AI create the picking list.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the workload. If the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can prioritize collecting only important data. This reduces the workload by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The data collection unit can perform dynamic filtering to prioritize the collection of data from specific areas within the warehouse. For example, the data collection unit can prioritize the collection of data from areas where high-value goods are stored. The data collection unit can prioritize the collection of data from areas with large fluctuations in temperature and humidity. The data collection unit can prioritize the collection of data from areas where worker traffic is concentrated. By prioritizing the collection of data from specific areas, important information can be efficiently obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from a specific area into a generating AI and have the generating AI perform dynamic filtering.

[0078] The data collection unit can be equipped with a function to detect sensor abnormalities during data collection and automatically notify when an abnormality occurs. For example, the data collection unit can detect and notify an abnormality when sensor data exceeds a certain range. The data collection unit can detect and notify an abnormality when sensor communication is interrupted. The data collection unit can detect and notify an abnormality when the sensor battery level drops low. This enables a rapid response by automatically detecting and notifying of sensor abnormalities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sensor data into a generating AI and have the generating AI perform abnormality detection.

[0079] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. If the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This allows for the efficient collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The data collection unit can integrate data from different sensors within the warehouse to generate comprehensive environmental data. For example, the data collection unit can integrate data from temperature and humidity sensors to generate comprehensive environmental data. The data collection unit can integrate data from illuminance and sound sensors to generate comprehensive environmental data. The data collection unit can integrate data from vibration and pressure sensors to generate comprehensive environmental data. In this way, comprehensive environmental data can be provided by integrating data from different sensors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from different sensors into a generating AI and have the generating AI perform the generation of comprehensive environmental data.

[0081] The data collection unit can refer to weather data outside the warehouse during data collection and consider factors that affect inventory management. For example, during rainy weather, the data collection unit can consider the effects of humidity during data collection. During sunny weather, the data collection unit can consider the effects of temperature during data collection. On snowy days, the data collection unit can consider temperature fluctuations inside the warehouse during data collection. This allows for the identification of factors affecting inventory management by considering weather data, enabling appropriate responses. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather data into a generating AI and have the generating AI consider factors that affect inventory management.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, visibility can be improved by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can predict inventory fluctuations by referring to past inventory data during analysis. For example, the analysis unit can predict seasonal inventory fluctuations based on past inventory data. The analysis unit can predict inventory fluctuations for a specific product category based on past inventory data. The analysis unit can predict inventory fluctuations over a specific period based on past inventory data. This makes it possible to predict inventory fluctuations by referring to past inventory data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past inventory data into a generating AI and have the generating AI perform inventory fluctuation predictions.

[0084] The analysis unit can be equipped with a function to detect abnormal inventory fluctuations during analysis and automatically issue alerts when an anomaly occurs. For example, the analysis unit can detect an anomaly and issue an alert when inventory decreases rapidly. The analysis unit can detect an anomaly and issue an alert when inventory increases rapidly. The analysis unit can detect an anomaly and issue an alert when inventory fluctuations exceed the normal range. This enables a rapid response by automatically detecting abnormal inventory fluctuations and issuing alerts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input inventory data into a generating AI and have the generating AI perform anomaly detection.

[0085] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying only important analysis results. If the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. If the user is in a hurry, the analysis unit can prioritize displaying analysis results that can be quickly reviewed. This allows for the efficient provision of important information by prioritizing analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit can integrate inventory data and sales data during analysis to optimize inventory management and sales strategies. For example, the analysis unit can integrate inventory data and sales data to perform demand forecasting. The analysis unit can integrate inventory data and sales data to optimize the timing of inventory replenishment. The analysis unit can integrate inventory data and sales data to analyze the effectiveness of sales promotion campaigns. As a result, integrating inventory data and sales data enables the optimization of inventory management and sales strategies. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input inventory data and sales data into a generating AI and have the generating AI perform the analysis of the integrated data.

[0087] The analysis unit can improve work efficiency by referring to worker movement data within the warehouse during analysis. For example, the analysis unit can propose efficient picking routes based on worker movement data. The analysis unit can optimize the layout of the work area based on worker movement data. The analysis unit can propose improvement measures to reduce the burden on workers based on worker movement data. In this way, work efficiency can be improved by referring to worker movement data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input worker movement data into a generating AI and have the generating AI propose efficient picking routes.

[0088] The generation unit can estimate the user's emotions and adjust the method of generating the picking order based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a picking order that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate a picking order that emphasizes the shortest route. If the user is excited, the generation unit can generate a picking order with visually stimulating effects. This improves work efficiency by adjusting the method of generating the picking order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0089] The generation unit can optimize the picking order by considering the expiration dates of inventory items during generation. For example, the generation unit can generate an order that prioritizes picking inventory items with approaching expiration dates. The generation unit can generate an order that postpones picking inventory items with longer expiration dates. The generation unit can generate an order that balances the picking of inventory items with different expiration dates. This improves the efficiency of inventory management by considering the expiration dates of inventory items. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input inventory expiration date data into a generation AI and have the generation AI perform the optimization of the picking order.

[0090] The generation unit can be equipped with a function to learn a more efficient picking sequence by referring to past picking data during generation. For example, the generation unit can learn an efficient picking sequence based on past picking data. The generation unit can learn a sequence that optimizes the worker's movement based on past picking data. The generation unit can learn a sequence that optimizes the placement of inventory items based on past picking data. As a result, by referring to past picking data, it is possible to learn an efficient picking sequence and improve work efficiency. Some or all of the above processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input past picking data into a generation AI and have the generation AI perform the learning of an efficient picking sequence.

[0091] The generation unit can estimate the user's emotions and adjust the display method of the picking list based on the estimated user emotions. For example, if the user is nervous, the generation unit can provide a simple and highly visible display method. If the user is relaxed, the generation unit can provide a display method that includes detailed information. If the user is in a hurry, the generation unit can provide a display method that gets straight to the point. This improves visibility by adjusting the display method of the picking list according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0092] The generation unit can optimize the picking order by considering the size and weight of the inventory items during generation. For example, the generation unit can generate an order that prioritizes picking large and heavy inventory items. The generation unit can generate an order that postpones picking small and light inventory items. The generation unit can generate an order that balances picking according to size and weight. In this way, an efficient picking order can be generated by considering the size and weight of the inventory items. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input inventory size and weight data into a generation AI and have the generation AI perform the optimization of the picking order.

[0093] The generation unit can adjust the picking order during generation, taking into account the schedules of multiple workers. For example, the generation unit can adjust the picking order so that multiple workers can work simultaneously. The generation unit can adjust the picking order, taking into account the workers' break times. The generation unit can generate an efficient picking order that matches the workers' schedules. In this way, an efficient picking order can be generated by considering the schedules of multiple workers. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input worker schedule data into a generation AI and have the generation AI perform the adjustment of the picking order.

[0094] The navigation unit can estimate the user's emotions and adjust the display method of the navigation based on the estimated user emotions. For example, if the user is tense, the navigation unit can provide a simple and highly visible display method. If the user is relaxed, the navigation unit can provide a display method that includes detailed information. If the user is in a hurry, the navigation unit can provide a display method that gets straight to the point. In this way, visibility can be improved by adjusting the display method of the navigation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0095] The navigation unit can reflect real-time information about obstacles within the warehouse during navigation and present the optimal route. For example, the navigation unit can acquire real-time information about obstacles within the warehouse and present the optimal route. The navigation unit can also present detour routes, taking into account the location of obstacles. The navigation unit can reflect the movement status of obstacles in real time and present the optimal route. In this way, by reflecting real-time information about obstacles within the warehouse, the optimal route can be presented. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input obstacle information into a generating AI and have the generating AI perform the generation of the optimal route.

[0096] The navigation unit can be equipped with a function to learn more efficient routes by referring to the worker's past travel history during navigation. For example, the navigation unit can learn efficient routes based on the worker's past travel history. The navigation unit can learn routes that avoid congestion based on the worker's past travel history. The navigation unit can learn the shortest route based on the worker's past travel history. This allows for the learning of efficient routes and improved work efficiency by referring to the worker's past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the worker's travel history data into a generating AI and have the generating AI perform the learning of efficient routes.

[0097] The navigation unit can estimate the user's emotions and determine navigation priorities based on the estimated emotions. For example, if the user is stressed, the navigation unit can prioritize displaying only important navigation information. If the user is relaxed, the navigation unit can prioritize displaying detailed navigation information. If the user is in a hurry, the navigation unit can prioritize displaying navigation information that can be quickly viewed. This allows for the efficient provision of important information by prioritizing navigation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI, or not using AI. For example, the navigation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The navigation unit can present highly visible routes during navigation, taking into account the lighting conditions within the warehouse. For example, the navigation unit can acquire the lighting conditions within the warehouse in real time and present highly visible routes. The navigation unit can present routes that avoid dimly lit areas. The navigation unit can present the optimal route according to the brightness of the lighting. In this way, by taking into account the lighting conditions within the warehouse, it is possible to present highly visible routes. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input lighting condition data into a generating AI and have the generating AI perform the generation of highly visible routes.

[0099] The navigation unit can monitor the worker's health condition during navigation and suggest a less strenuous route. For example, the navigation unit can monitor the worker's health condition in real time and suggest a less strenuous route. The navigation unit can suggest a route that includes rest points, taking into account the worker's fatigue level. The navigation unit can suggest the optimal route according to the worker's health condition. In this way, by monitoring the worker's health condition, a less strenuous route can be suggested. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input worker health data into a generating AI and have the generating AI generate a less strenuous route.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can monitor the health status of workers while collecting data within the warehouse and adjust the data collection frequency accordingly. For example, if a worker is fatigued, the data collection frequency can be reduced to lessen their burden. If a worker is healthy, the data collection frequency can be increased to collect more detailed data. By adjusting the data collection frequency according to the worker's health status, the burden on workers can be reduced, and efficient data collection can be achieved.

[0102] The analysis unit can manage inventory by considering the expiration dates of stock items based on the collected data. For example, it can prioritize managing stock items with approaching expiration dates and postpone managing stock items with longer expiration dates. This improves the efficiency of inventory management by considering the expiration dates of stock items.

[0103] The generation unit can use generation AI to optimize the picking order, taking into account the size and weight of inventory items. For example, it can generate an order that prioritizes picking large and heavy inventory items, or an order that postpones picking small and light inventory items. In this way, by considering the size and weight of inventory items, an efficient picking order can be generated.

[0104] The navigation unit can reflect real-time information about obstacles within the warehouse and suggest the optimal route. For example, it can acquire real-time information about obstacles within the warehouse and suggest the optimal route. It can also suggest detour routes considering the location of obstacles. In this way, by reflecting real-time information about obstacles within the warehouse, it can suggest the optimal route.

[0105] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the workload. If the user is relaxed, the frequency of data collection can be increased to collect more detailed data. This allows for reduced workload by adjusting the timing of data collection according to the user's emotions.

[0106] The analysis unit can manage inventory by considering the expiration dates of stock items based on the collected data. For example, it can prioritize managing stock items with approaching expiration dates and postpone managing stock items with longer expiration dates. This improves the efficiency of inventory management by considering the expiration dates of stock items.

[0107] The generation unit can use generation AI to optimize the picking order, taking into account the size and weight of inventory items. For example, it can generate an order that prioritizes picking large and heavy inventory items, or an order that postpones picking small and light inventory items. In this way, by considering the size and weight of inventory items, an efficient picking order can be generated.

[0108] The navigation unit can reflect real-time information about obstacles within the warehouse and suggest the optimal route. For example, it can acquire real-time information about obstacles within the warehouse and suggest the optimal route. It can also suggest detour routes considering the location of obstacles. In this way, by reflecting real-time information about obstacles within the warehouse, it can suggest the optimal route.

[0109] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the workload. If the user is relaxed, the frequency of data collection can be increased to collect more detailed data. This allows for reduced workload by adjusting the timing of data collection according to the user's emotions.

[0110] The analysis unit can manage inventory by considering the expiration dates of stock items based on the collected data. For example, it can prioritize managing stock items with approaching expiration dates and postpone managing stock items with longer expiration dates. This improves the efficiency of inventory management by considering the expiration dates of stock items.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects data within the warehouse. The data collection unit collects environmental data such as temperature and humidity, as well as information such as the location and quantity of inventory items, in real time. The data collection unit can collect environmental data using temperature and humidity sensors. The data collection unit can also use RFID tags or barcode scanners to determine the location of inventory items. Step 2: The analysis unit analyzes the data collected by the collection unit and monitors inventory fluctuations. The analysis unit can analyze the collected temperature and humidity data to monitor the storage environment of the inventory. The analysis unit can also analyze the location data of the inventory to monitor inventory fluctuations. Step 3: The generation unit generates the optimal picking order based on the data analyzed by the analysis unit. The generation unit can perform ABC analysis and determine the picking order based on the importance of the inventory items. The generation unit can also generate an efficient picking route based on the location data of the inventory items. Step 4: The navigation unit shows the worker the route based on the picking list generated by the generation unit. The navigation unit can navigate the worker to the optimal route using a smartphone or tablet.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and navigation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data in the warehouse using the camera 42 and sensors of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to monitor inventory fluctuations. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates the optimal picking order based on the analysis results. The navigation unit is implemented, for example, by the control unit 46A of the smart device 14, and navigates the worker to the optimal route based on the generated picking list. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and navigation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data in the warehouse using the camera 42 and sensors of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to monitor inventory fluctuations. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates the optimal picking order based on the analysis results. The navigation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and navigates the worker to the optimal route based on the generated picking list. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and navigation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data in the warehouse using the camera 42 and sensors of the headset terminal 314 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to monitor inventory fluctuations. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates the optimal picking order based on the analysis results. The navigation unit is implemented in the control unit 46A of the headset terminal 314 and navigates the worker to the optimal route based on the generated picking list. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and navigation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data in the warehouse using the camera 42 and sensors of the robot 414 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to monitor inventory fluctuations. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates the optimal picking order based on the analysis results. The navigation unit is implemented in the control unit 46A of the robot 414 and navigates the worker along the optimal route based on the generated picking list. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A data collection unit that collects data from within the warehouse, An analysis unit analyzes the data collected by the aforementioned collection unit and monitors fluctuations in inventory, A generation unit that generates an optimal picking order based on the data analyzed by the analysis unit, The system includes a navigation unit that shows the worker a route based on the picking list generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect environmental data such as temperature and humidity. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, ABC analysis will be performed based on the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The AI ​​generates the optimal picking order. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned navigation unit is Navigate workers to the optimal route when they move around the warehouse using their smartphones or tablets. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Create a picking list based on the worker's instructions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Dynamic filtering is performed to prioritize the collection of data from specific areas within the warehouse. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, a function will be added to detect sensor abnormalities and automatically notify if an abnormality occurs. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Integrating data from different sensors within the warehouse generates comprehensive environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, refer to weather data outside the warehouse and consider factors that affect inventory management. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, historical inventory data is referenced to predict inventory fluctuations. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, we will add a function that detects abnormal inventory fluctuations and automatically issues an alert if an anomaly occurs. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, inventory and sales data are integrated to optimize inventory management and sales strategies. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, data on the movement patterns of workers within the warehouse will be referenced to improve work efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the user's emotions and adjusts the picking order generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the picking order is optimized considering the expiration dates of inventory items. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, add a feature that references past picking data to learn a more efficient picking sequence. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates the user's emotions and adjusts how the picking list is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the picking order is optimized considering the size and weight of the inventory items. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the picking order is adjusted to take into account the schedules of multiple workers. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned navigation unit is It estimates the user's emotions and adjusts how navigation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned navigation unit is During navigation, the system reflects real-time information about obstacles within the warehouse and suggests the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned navigation unit is During navigation, add a feature that learns more efficient routes by referring to the worker's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned navigation unit is It estimates the user's emotions and determines navigation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned navigation unit is During navigation, the system takes into account the lighting conditions inside the warehouse and presents routes with high visibility. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned navigation unit is During navigation, the system monitors the worker's health condition and suggests routes that minimize strain. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from within the warehouse, An analysis unit analyzes the data collected by the aforementioned collection unit and monitors fluctuations in inventory, A generation unit that generates an optimal picking order based on the data analyzed by the analysis unit, The system includes a navigation unit that shows the worker a route based on the picking list generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect environmental data such as temperature and humidity. The system according to feature 1.

3. The aforementioned analysis unit, ABC analysis will be performed based on the collected data. The system according to feature 1.

4. The generating unit is The AI ​​generates the optimal picking order. The system according to feature 1.

5. The aforementioned navigation unit is Navigate workers to the optimal route when they move around the warehouse using their smartphones or tablets. The system according to feature 1.

6. The generating unit is Create a picking list based on the worker's instructions. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Dynamic filtering is performed to prioritize the collection of data from specific areas within the warehouse. The system according to feature 1.

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