system

Abstract

The system according to this embodiment aims to enable users to efficiently receive goods even when they are absent. [Solution] The system according to the embodiment comprises a reception unit, a storage unit, and a receiving unit. The reception unit receives an order from the user online and selects a locker as the delivery destination. Based on the information received by the reception unit, the storage unit receives the delivery from the delivery company and stores the goods in the locker. The receiving unit retrieves the goods stored by the storage unit from the locker using a passcode specified by the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, it is difficult to handle the situation when the user is absent during home delivery, and an efficient delivery method is required.

[0005] The system according to the embodiment aims to enable the user to efficiently receive goods even when absent. <​​​​​The system according to this embodiment comprises a reception unit, a storage unit, and a receiving unit. The reception unit receives an order from a user who orders goods online and selects a locker as the delivery destination. Based on the information received by the reception unit, the storage unit receives the goods from a delivery company who delivers the goods to the locker and stores the goods in the locker. The receiving unit retrieves the goods stored by the storage unit from the locker using a passcode specified by the user. [Effects of the Invention]

[0007] The system according to this embodiment allows users to efficiently receive goods even when they are absent. [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, and the like. The communication I / F controls 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 locker delivery system according to an embodiment of the present invention fundamentally revises conventional home delivery systems. This locker delivery system comprises a reception unit where the user orders goods online and selects a locker as the delivery destination, a storage unit where a delivery company delivers the goods to the locker and stores the goods in the locker, and a receiving unit where the user retrieves the goods from the locker using a designated passcode. For example, when a user orders goods online, they select a locker as the delivery destination. In this case, the user can choose a locker near their home or workplace. For example, they can choose a locker installed at a train station or shopping mall. This allows the user to receive goods at their convenience. Next, the delivery company delivers the goods to the locker and stores them in the locker. The delivery company delivers the goods to the designated locker and stores them in the locker. In this case, a passcode is set for the locker to ensure that the goods are stored securely. For example, when the delivery company stores the goods in the locker and closes the locker door, the passcode is automatically set. The user is notified that the goods have been delivered to the locker and retrieves the goods from the locker using the designated passcode. The user is notified via smartphone or email that the goods have been delivered. The notification includes the location of the locker and the passcode. Users can open the locker door using a designated passcode and retrieve their items. For example, when a user arrives at the locker and enters the passcode displayed on their smartphone, the locker door opens and they can retrieve their items. This system eliminates the need for users to wait at home for delivery and allows delivery companies to deliver more efficiently. Users can receive their items at their convenience, and delivery companies can deliver to multiple lockers at once, enabling more efficient deliveries. For example, by delivering to multiple lockers simultaneously, delivery companies can reduce delivery times and deliver more efficiently. In short, the locker delivery system eliminates the need for users to wait at home for delivery and allows delivery companies to deliver more efficiently.

[0029] The locker delivery system according to this embodiment comprises a reception unit, a storage unit, and a receiving unit. The reception unit allows users to order goods online and select a locker as the delivery destination. For example, users can choose a locker near their home or workplace. The reception unit provides an interface for users to select a locker, for example, through a website or mobile app. The storage unit allows delivery personnel to deliver goods to the locker and store the goods in the locker. For example, a delivery personnel deliver goods to a designated locker and store the goods in the locker. At this time, a passcode is set for the locker to ensure the goods are stored securely. The storage unit includes a mechanism that automatically sets the passcode when the locker door is closed. The receiving unit allows users to retrieve goods from the locker using the designated passcode. For example, users are notified via smartphone or email that their goods have been delivered, and the notification includes the locker location and passcode. Users can open the locker door using the designated passcode and retrieve their goods. For example, when a user arrives at the locker and enters the passcode displayed on their smartphone, the locker door opens and they can retrieve their goods. As a result, the locker delivery system according to this embodiment eliminates the need for users to wait at home for delivery, and allows delivery companies to deliver more efficiently.

[0030] The locker delivery system includes a management unit that manages the locations of the lockers. The management unit efficiently manages the locker locations. For example, it maintains a database of locker locations and manages information about them. Furthermore, the management unit can monitor the usage status of locker locations and select the optimal location. For instance, it analyzes locker usage frequency and the surrounding environment to select the best location. This allows for efficient management of locker locations.

[0031] The locker delivery system includes a passcode management unit that manages passcodes. The passcode management unit efficiently manages passcodes. For example, it manages passcode generation methods and security standards. It generates passcodes using techniques such as random generation or encryption. Furthermore, the passcode management unit can record passcode usage history and assess security risks. This enables efficient passcode management.

[0032] The locker delivery system includes a monitoring unit that monitors locker usage in real time. The monitoring unit monitors locker usage in real time. For example, it uses sensor technology and network monitoring to monitor locker usage. The monitoring unit can, for instance, monitor locker opening / closing status and usage frequency in real time and issue alerts if an anomaly is detected. This allows for real-time monitoring of locker usage.

[0033] The locker delivery system includes a history section that records the usage history of lockers. The history section efficiently records the locker usage history. For example, the history section records and stores usage history using a database. The history section records information such as the date and time of use and the number of uses, and allows for searching and analysis as needed. This enables efficient recording of locker usage history.

[0034] The reception desk allows users to select a locker near their home or workplace. For example, the reception desk provides an interface through a website or mobile app that allows users to select a locker near their home or workplace. This allows users to receive their items at their convenience.

[0035] The storage unit allows delivery personnel to place items in lockers and set a passcode. For example, a delivery person delivers items to a designated locker and places them inside. At this time, a passcode is set for the locker, ensuring the items are stored securely. The storage unit includes a mechanism that automatically sets the passcode when the locker door is closed, thus ensuring the items are stored securely.

[0036] The receiving system allows users to open the locker door using a passcode notified to them via smartphone or email. For example, a user is notified via smartphone or email that their item has been delivered, and the notification includes the locker's location and passcode. The user can then open the locker door using the designated passcode and retrieve their item. For instance, when a user arrives at the locker and enters the passcode displayed on their smartphone, the locker door opens, allowing them to retrieve their item. This makes it easy for users to receive their items.

[0037] The reception desk analyzes the user's past order history and suggests the most suitable locker option. For example, it prioritizes suggesting lockers the user has frequently used in the past. It predicts and suggests lockers the user will use at specific times based on their past order history. It analyzes the user's past order history to suggest the most efficient locker. This allows the system to suggest the optimal locker based on the user's past order history.

[0038] The reception desk automatically selects the nearest locker based on the user's current location. For example, when a user opens the app, it automatically retrieves their current location and suggests the nearest locker. When a user uses the app while on the move, it updates their location in real time and suggests the nearest locker. When the user arrives at their destination, it automatically selects the nearest locker. This allows the system to automatically select the nearest locker based on the user's current location.

[0039] The reception desk prioritizes displaying lockers that users frequently use, based on their past usage history. For example, it prioritizes displaying lockers that users have frequently used in the past. It predicts and displays lockers that users will use during specific time periods based on their past usage history. It analyzes users' past usage history to display the most efficient lockers. This allows for the priority display of the most suitable lockers based on the user's past usage history.

[0040] The reception desk suggests a locker that best suits the user's schedule based on their schedule information. For example, it might refer to the user's calendar information to suggest a locker that best suits their schedule. It might predict and suggest a locker that the user will use at a specific time based on their schedule information. It might suggest the most efficient locker based on the user's schedule information. This allows the system to suggest the optimal locker based on the user's schedule.

[0041] The storage unit automatically selects the optimal storage method according to the size and shape of the product. For example, it selects a locker with ample space for large products, a locker with less space for small products, and a flexible storage method for irregularly shaped products. This ensures that the optimal storage method is provided according to the size and shape of the product.

[0042] The storage unit selects lockers that maintain specific temperature and humidity conditions depending on the type of product. For example, fresh foods are stored in lockers with refrigeration functions. Books and electronic devices are stored in lockers with drying functions. Cosmetics and pharmaceuticals are stored in lockers that maintain specific temperature conditions. This ensures that optimal storage conditions are provided for each type of product.

[0043] The storage unit tracks the delivery status of products in real time and adjusts the optimal storage timing. For example, it monitors the delivery status in real time and adjusts the storage timing according to the estimated arrival time. If a delivery delay occurs, it readjusts the storage timing. If delivery is earlier than expected, it quickly adjusts the storage timing. This allows for the provision of optimal storage timing according to the delivery status of products.

[0044] The storage unit can set multiple security levels to ensure the safety of the goods. For example, a high security level can be set for expensive goods. A standard security level can be set for general goods. A security level that is only accessible under specific conditions can be set. This allows for the provision of the optimal security level to ensure the safety of the goods.

[0045] The receiving unit analyzes the user's past receiving history and proposes the optimal receiving method. For example, it proposes the optimal receiving method based on the receiving methods the user has used in the past. It predicts and proposes the receiving method the user will use during specific time periods based on their past receiving history. It analyzes the user's past receiving history and proposes the most efficient receiving method. This allows the system to propose the optimal receiving method based on the user's past receiving history.

[0046] The receiving unit presents the method for picking up the nearest locker based on the user's current location. For example, when a user opens the app, it automatically obtains their current location and presents the method for picking up the nearest locker. When a user uses the app while on the move, it updates their current location in real time and presents the method for picking up the nearest locker. When a user arrives at their destination, it automatically presents the method for picking up the nearest locker. This allows the system to provide the method for picking up the nearest locker based on the user's current location.

[0047] The receiving unit proposes the optimal receiving time based on the user's schedule information. For example, it may refer to the user's calendar information to propose the optimal receiving time. It may predict and propose a receiving time to be used during a specific time period based on the user's schedule information. It may propose the most efficient receiving time based on the user's schedule information. This allows the system to provide the optimal receiving time based on the user's schedule information.

[0048] The receiving unit selects the optimal notification method based on the user's device information. For example, if the user is using a smartphone, push notifications will be prioritized. If the user frequently uses email, email notifications will be prioritized. If the user uses multiple devices, notifications will be sent to the device that is used most frequently. This allows the system to provide the optimal notification method based on the user's device information.

[0049] The management department analyzes locker usage frequency and selects the optimal installation location. For example, they might install lockers in high-usage locations or relocate lockers from low-usage locations. They also analyze fluctuations in usage frequency to select the optimal installation location. This allows them to provide the best possible installation location based on locker usage frequency.

[0050] The management department evaluates the safety of potential locker locations and selects the most suitable place. For example, they might install lockers in areas with low crime rates, in locations with security cameras, or in well-lit areas at night. This ensures that the optimal location is provided based on the safety of the locker site.

[0051] The management department analyzes the surrounding environment of potential locker locations and selects the optimal location. For example, they might install lockers in areas where many people gather, or in areas with numerous commercial facilities. They might also analyze surrounding traffic conditions and install lockers in easily accessible locations. This allows them to provide the optimal location based on the surrounding environment of the locker installation site.

[0052] The management department selects the optimal location for each locker based on access information. For example, they might install lockers near public transport stations, near parking lots, or near bicycle parking areas. This allows them to provide the most suitable location based on access information.

[0053] The Passcode Management Department analyzes passcode usage history and selects the optimal method for generating passcodes. For example, it may generate optimal passcodes based on patterns of passcodes previously used by the user. It may also analyze passcode usage frequency to generate optimal passcodes. Furthermore, it may evaluate security risks from passcode usage history and generate optimal passcodes. This allows for the provision of optimal passcodes based on passcode usage history.

[0054] The passcode management unit evaluates the security level of the passcode and generates the optimal passcode. For example, if a high security level is required, it generates a complex passcode. If a standard security level is required, it generates a passcode of moderate complexity. If a low security level is acceptable, it generates a simple passcode. This allows the system to provide the optimal passcode based on the required security level.

[0055] The passcode management unit monitors passcode usage in real time and generates the optimal passcode. For example, it monitors passcode usage in real time, assesses security risks, and generates the optimal passcode. It monitors passcode usage frequency in real time and generates the optimal passcode. It monitors passcode usage in real time and generates a new passcode when the expiration date approaches. This allows the system to provide the optimal passcode based on passcode usage.

[0056] The passcode management unit sets the passcode expiration date and generates the optimal passcode. For example, if a high security level is required, it generates a passcode with a short expiration date. If a standard security level is required, it generates a passcode with a moderate expiration date. If a lower security level is acceptable, it generates a passcode with a long expiration date. This allows the system to provide the optimal passcode based on the passcode expiration date.

[0057] The monitoring department analyzes locker usage and selects the optimal monitoring method. For example, frequently used lockers are monitored more frequently, while less frequently used lockers are monitored more regularly. The department analyzes fluctuations in usage and selects the optimal monitoring method. This allows the system to provide the most appropriate monitoring method based on locker usage.

[0058] The monitoring department evaluates the security status of the lockers and selects the most appropriate monitoring method. For example, lockers requiring high security will be monitored rigorously, lockers requiring standard security will be monitored moderately, and lockers where low security is acceptable will be monitored simply. This allows the system to provide the most suitable monitoring method based on the security status of each locker.

[0059] The monitoring unit monitors locker usage in real time and selects the optimal monitoring method. For example, it monitors usage in real time and issues an alert if an anomaly is detected. It monitors frequently used lockers in real time and selects the optimal monitoring method. It also monitors changes in usage in real time and selects the optimal monitoring method. This allows for the provision of the most appropriate monitoring method based on locker usage.

[0060] The monitoring unit monitors the security status of lockers in real time and selects the optimal monitoring method. For example, it monitors the security status in real time and issues an alert if an anomaly is detected. It monitors lockers requiring high security in real time and selects the optimal monitoring method. It monitors changes in security status in real time and selects the optimal monitoring method. This allows for the provision of the most appropriate monitoring method based on the security status of the lockers.

[0061] The history department analyzes locker usage history and selects the optimal recording method. For example, it records the history of frequently used lockers in detail and the history of less frequently used lockers concisely. It analyzes fluctuations in usage history and selects the optimal recording method. This allows the system to provide the most suitable recording method based on locker usage history.

[0062] The history department evaluates the security of locker usage history and selects the optimal recording method. For example, it provides a strict recording method for history requiring high security, a moderate recording method for history requiring standard security, and a simple recording method for history where low security is acceptable. This allows the department to provide the optimal recording method based on the security of the locker usage history.

[0063] The history unit records locker usage history in real time and selects the optimal recording method. For example, it records usage history in real time and issues an alert if an anomaly is detected. It records the history of frequently used lockers in real time and selects the optimal recording method. It records changes in usage history in real time and selects the optimal recording method. This allows the system to provide the optimal recording method based on locker usage history.

[0064] The history department monitors the security of locker usage history in real time and selects the optimal recording method. For example, it monitors the security of usage history in real time and issues an alert if an anomaly is detected. It monitors history requiring high security in real time and selects the optimal recording method. It monitors changes in the security of usage history in real time and selects the optimal recording method. This allows for the provision of the optimal recording method based on the security of the locker usage history.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The reception desk can also analyze a user's past order history and suggest the most suitable locker options. For example, it can prioritize suggesting lockers the user has frequently used in the past. It can predict and suggest lockers the user will use at specific times based on their past order history. It can analyze a user's past order history and suggest the most efficient locker. This allows the system to suggest the optimal locker based on the user's past order history.

[0067] The management department can also evaluate the safety of locker locations and select the most suitable locations. For example, they can install lockers in areas with low crime rates, in areas with security cameras, or in well-lit areas at night. This allows them to provide the best possible locations based on the safety of the locker installation site.

[0068] The passcode management unit can also analyze passcode usage history and select the optimal passcode generation method. For example, it can generate the optimal passcode based on patterns of passcodes previously used by the user. It can analyze passcode usage frequency and generate the optimal passcode. It can evaluate security risks from passcode usage history and generate the optimal passcode. This allows for the provision of the optimal passcode based on passcode usage history.

[0069] The monitoring unit can also monitor locker usage in real time and select the optimal monitoring method. For example, it can monitor usage in real time and issue an alert if an anomaly is detected. It can monitor frequently used lockers in real time and select the optimal monitoring method. It can monitor changes in usage in real time and select the optimal monitoring method. This allows the system to provide the most suitable monitoring method based on locker usage.

[0070] The history section can record locker usage history in real time and select the optimal recording method. For example, it can record usage history in real time and issue an alert if an anomaly is detected. It can also record the history of frequently used lockers in real time and select the optimal recording method. It can record changes in usage history in real time and select the optimal recording method. This allows the system to provide the optimal recording method based on locker usage history.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The reception desk allows users to order products online and select a locker as the delivery location. For example, users can choose a locker near their home or workplace. The reception desk provides an interface for users to select a locker via a website or mobile app. Step 2: The storage unit is where the delivery company delivers the goods to the locker and stores them. For example, the delivery company delivers the goods to the designated locker and stores them there. At this time, a passcode is set for the locker to ensure that the goods are stored securely. The storage unit is equipped with a mechanism that automatically sets the passcode when the locker door is closed. Step 3: The receiving department allows the user to retrieve the item from the locker using a designated passcode. For example, the user is notified via smartphone or email that the item has been delivered, and the notification includes the locker's location and passcode. The user can then open the locker door using the designated passcode and retrieve the item. For instance, when the user arrives at the locker and enters the passcode displayed on their smartphone, the locker door opens and they can retrieve their item.

[0073] (Example of form 2) The locker delivery system according to an embodiment of the present invention fundamentally revises conventional home delivery systems. This locker delivery system comprises a reception unit where the user orders goods online and selects a locker as the delivery destination, a storage unit where a delivery company delivers the goods to the locker and stores the goods in the locker, and a receiving unit where the user retrieves the goods from the locker using a designated passcode. For example, when a user orders goods online, they select a locker as the delivery destination. In this case, the user can choose a locker near their home or workplace. For example, they can choose a locker installed at a train station or shopping mall. This allows the user to receive goods at their convenience. Next, the delivery company delivers the goods to the locker and stores them in the locker. The delivery company delivers the goods to the designated locker and stores them in the locker. In this case, a passcode is set for the locker to ensure that the goods are stored securely. For example, when the delivery company stores the goods in the locker and closes the locker door, the passcode is automatically set. The user is notified that the goods have been delivered to the locker and retrieves the goods from the locker using the designated passcode. The user is notified via smartphone or email that the goods have been delivered. The notification includes the location of the locker and the passcode. Users can open the locker door using a designated passcode and retrieve their items. For example, when a user arrives at the locker and enters the passcode displayed on their smartphone, the locker door opens and they can retrieve their items. This system eliminates the need for users to wait at home for delivery and allows delivery companies to deliver more efficiently. Users can receive their items at their convenience, and delivery companies can deliver to multiple lockers at once, enabling more efficient deliveries. For example, by delivering to multiple lockers simultaneously, delivery companies can reduce delivery times and deliver more efficiently. In short, the locker delivery system eliminates the need for users to wait at home for delivery and allows delivery companies to deliver more efficiently.

[0074] The locker delivery system according to this embodiment comprises a reception unit, a storage unit, and a receiving unit. The reception unit allows users to order goods online and select a locker as the delivery destination. For example, users can choose a locker near their home or workplace. The reception unit provides an interface for users to select a locker, for example, through a website or mobile app. The storage unit allows delivery personnel to deliver goods to the locker and store the goods in the locker. For example, a delivery personnel deliver goods to a designated locker and store the goods in the locker. At this time, a passcode is set for the locker to ensure the goods are stored securely. The storage unit includes a mechanism that automatically sets the passcode when the locker door is closed. The receiving unit allows users to retrieve goods from the locker using the designated passcode. For example, users are notified via smartphone or email that their goods have been delivered, and the notification includes the locker location and passcode. Users can open the locker door using the designated passcode and retrieve their goods. For example, when a user arrives at the locker and enters the passcode displayed on their smartphone, the locker door opens and they can retrieve their goods. As a result, the locker delivery system according to this embodiment eliminates the need for users to wait at home for delivery, and allows delivery companies to deliver more efficiently.

[0075] The locker delivery system includes a management unit that manages the locations of the lockers. The management unit efficiently manages the locker locations. For example, it maintains a database of locker locations and manages information about them. Furthermore, the management unit can monitor the usage status of locker locations and select the optimal location. For instance, it analyzes locker usage frequency and the surrounding environment to select the best location. This allows for efficient management of locker locations.

[0076] The locker delivery system includes a passcode management unit that manages passcodes. The passcode management unit efficiently manages passcodes. For example, it manages passcode generation methods and security standards. It generates passcodes using techniques such as random generation or encryption. Furthermore, the passcode management unit can record passcode usage history and assess security risks. This enables efficient passcode management.

[0077] The locker delivery system includes a monitoring unit that monitors locker usage in real time. The monitoring unit monitors locker usage in real time. For example, it uses sensor technology and network monitoring to monitor locker usage. The monitoring unit can, for instance, monitor locker opening / closing status and usage frequency in real time and issue alerts if an anomaly is detected. This allows for real-time monitoring of locker usage.

[0078] The locker delivery system includes a history section that records the usage history of lockers. The history section efficiently records the locker usage history. For example, the history section records and stores usage history using a database. The history section records information such as the date and time of use and the number of uses, and allows for searching and analysis as needed. This enables efficient recording of locker usage history.

[0079] The reception desk allows users to select a locker near their home or workplace. For example, the reception desk provides an interface through a website or mobile app that allows users to select a locker near their home or workplace. This allows users to receive their items at their convenience.

[0080] The storage unit allows delivery personnel to place items in lockers and set a passcode. For example, a delivery person delivers items to a designated locker and places them inside. At this time, a passcode is set for the locker, ensuring the items are stored securely. The storage unit includes a mechanism that automatically sets the passcode when the locker door is closed, thus ensuring the items are stored securely.

[0081] The receiving system allows users to open the locker door using a passcode notified to them via smartphone or email. For example, a user is notified via smartphone or email that their item has been delivered, and the notification includes the locker's location and passcode. The user can then open the locker door using the designated passcode and retrieve their item. For instance, when a user arrives at the locker and enters the passcode displayed on their smartphone, the locker door opens, allowing them to retrieve their item. This makes it easy for users to receive their items.

[0082] The reception desk estimates the user's emotions and presents locker options based on those estimates. For example, if the user is stressed, the nearest locker is prioritized. If the user is relaxed, multiple locker options are presented in detail. If the user is in a hurry, immediately available lockers are prioritized. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows the system to provide the most suitable locker options based on the user's emotions.

[0083] The reception desk analyzes the user's past order history and suggests the most suitable locker option. For example, it prioritizes suggesting lockers the user has frequently used in the past. It predicts and suggests lockers the user will use at specific times based on their past order history. It analyzes the user's past order history to suggest the most efficient locker. This allows the system to suggest the optimal locker based on the user's past order history.

[0084] The reception desk automatically selects the nearest locker based on the user's current location. For example, when a user opens the app, it automatically retrieves their current location and suggests the nearest locker. When a user uses the app while on the move, it updates their location in real time and suggests the nearest locker. When the user arrives at their destination, it automatically selects the nearest locker. This allows the system to automatically select the nearest locker based on the user's current location.

[0085] The reception desk estimates the user's emotions and prioritizes the locker options based on those emotions. For example, if the user is stressed, the nearest locker is displayed first. If the user is relaxed, multiple locker options are displayed in detail. If the user is in a hurry, immediately available lockers are displayed first. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. This allows for the provision of optimal locker option prioritization based on the user's emotions.

[0086] The reception desk prioritizes displaying lockers that users frequently use, based on their past usage history. For example, it prioritizes displaying lockers that users have frequently used in the past. It predicts and displays lockers that users will use during specific time periods based on their past usage history. It analyzes users' past usage history to display the most efficient lockers. This allows for the priority display of the most suitable lockers based on the user's past usage history.

[0087] The reception desk suggests a locker that best suits the user's schedule based on their schedule information. For example, it might refer to the user's calendar information to suggest a locker that best suits their schedule. It might predict and suggest a locker that the user will use at a specific time based on their schedule information. It might suggest the most efficient locker based on the user's schedule information. This allows the system to suggest the optimal locker based on the user's schedule.

[0088] The storage unit estimates the user's emotions and adjusts the storage method based on the estimated emotions. For example, if the user is stressed, it selects a method to store the items quickly. If the user is relaxed, it selects a method to store the items carefully. If the user is in a hurry, it selects a method to store the items in the shortest possible time. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows the system to provide the optimal storage method according to the user's emotions.

[0089] The storage unit automatically selects the optimal storage method according to the size and shape of the product. For example, it selects a locker with ample space for large products, a locker with less space for small products, and a flexible storage method for irregularly shaped products. This ensures that the optimal storage method is provided according to the size and shape of the product.

[0090] The storage unit selects lockers that maintain specific temperature and humidity conditions depending on the type of product. For example, fresh foods are stored in lockers with refrigeration functions. Books and electronic devices are stored in lockers with drying functions. Cosmetics and pharmaceuticals are stored in lockers that maintain specific temperature conditions. This ensures that optimal storage conditions are provided for each type of product.

[0091] The storage unit estimates the user's emotions and determines the priority of items to store based on those emotions. For example, if the user is stressed, important items will be stored first. If the user is relaxed, items will be stored in order. If the user is in a hurry, items that can be stored quickly will be stored first. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows for the provision of optimal item priorities according to the user's emotions.

[0092] The storage unit tracks the delivery status of products in real time and adjusts the optimal storage timing. For example, it monitors the delivery status in real time and adjusts the storage timing according to the estimated arrival time. If a delivery delay occurs, it readjusts the storage timing. If delivery is earlier than expected, it quickly adjusts the storage timing. This allows for the provision of optimal storage timing according to the delivery status of products.

[0093] The storage unit can set multiple security levels to ensure the safety of the goods. For example, a high security level can be set for expensive goods. A standard security level can be set for general goods. A security level that is only accessible under specific conditions can be set. This allows for the provision of the optimal security level to ensure the safety of the goods.

[0094] The receiving unit estimates the user's emotions and adjusts the receiving method based on the estimated emotions. For example, if the user is stressed, it provides a simple receiving method. If the user is relaxed, it provides a detailed receiving method. If the user is in a hurry, it provides a quick receiving method. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows for the provision of the optimal receiving method according to the user's emotions.

[0095] The receiving unit analyzes the user's past receiving history and proposes the optimal receiving method. For example, it proposes the optimal receiving method based on the receiving methods the user has used in the past. It predicts and proposes the receiving method the user will use during specific time periods based on their past receiving history. It analyzes the user's past receiving history and proposes the most efficient receiving method. This allows the system to propose the optimal receiving method based on the user's past receiving history.

[0096] The receiving unit presents the method for picking up the nearest locker based on the user's current location. For example, when a user opens the app, it automatically obtains their current location and presents the method for picking up the nearest locker. When a user uses the app while on the move, it updates their current location in real time and presents the method for picking up the nearest locker. When a user arrives at their destination, it automatically presents the method for picking up the nearest locker. This allows the system to provide the method for picking up the nearest locker based on the user's current location.

[0097] The receiving unit estimates the user's emotions and determines the priority of receiving items based on those emotions. For example, if the user is stressed, it provides a method to receive important items first. If the user is relaxed, it provides a method to receive items in order. If the user is in a hurry, it provides a method to receive items that can be received quickly first. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows for the provision of an optimal receiving priority according to the user's emotions.

[0098] The receiving unit proposes the optimal receiving time based on the user's schedule information. For example, it may refer to the user's calendar information to propose the optimal receiving time. It may predict and propose a receiving time to be used during a specific time period based on the user's schedule information. It may propose the most efficient receiving time based on the user's schedule information. This allows the system to provide the optimal receiving time based on the user's schedule information.

[0099] The receiving unit selects the optimal notification method based on the user's device information. For example, if the user is using a smartphone, push notifications will be prioritized. If the user frequently uses email, email notifications will be prioritized. If the user uses multiple devices, notifications will be sent to the device that is used most frequently. This allows the system to provide the optimal notification method based on the user's device information.

[0100] The management department estimates the user's emotions and adjusts the locker placement based on those estimates. For example, if a user is stressed, a locker will be placed in an easily accessible location. If a user is relaxed, a locker will be placed in a quiet location. If a user is in a hurry, a locker will be placed in a convenient location. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows for the provision of optimal locker placement according to the user's emotions.

[0101] The management department analyzes locker usage frequency and selects the optimal installation location. For example, they might install lockers in high-usage locations or relocate lockers from low-usage locations. They also analyze fluctuations in usage frequency to select the optimal installation location. This allows them to provide the best possible installation location based on locker usage frequency.

[0102] The management department evaluates the safety of potential locker locations and selects the most suitable place. For example, they might install lockers in areas with low crime rates, in locations with security cameras, or in well-lit areas at night. This ensures that the optimal location is provided based on the safety of the locker site.

[0103] The management department estimates the user's emotions and prioritizes the placement of lockers based on those estimated emotions. For example, if a user is stressed, an easily accessible location will be prioritized. If a user is relaxed, a quiet location will be prioritized. If a user is in a hurry, a location with convenient transportation will be prioritized. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows for the provision of optimal locker placement priorities tailored to the user's emotions.

[0104] The management department analyzes the surrounding environment of potential locker locations and selects the optimal location. For example, they might install lockers in areas where many people gather, or in areas with numerous commercial facilities. They might also analyze surrounding traffic conditions and install lockers in easily accessible locations. This allows them to provide the optimal location based on the surrounding environment of the locker installation site.

[0105] The management department selects the optimal location for each locker based on access information. For example, they might install lockers near public transport stations, near parking lots, or near bicycle parking areas. This allows them to provide the most suitable location based on access information.

[0106] The passcode management unit estimates the user's emotions and adjusts the passcode generation method based on the estimated emotions. For example, if the user is stressed, it generates a simple passcode. If the user is relaxed, it generates a complex passcode. If the user is in a hurry, it provides a passcode that can be generated quickly. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows the system to provide an optimal passcode generation method that matches the user's emotions.

[0107] The Passcode Management Department analyzes passcode usage history and selects the optimal method for generating passcodes. For example, it may generate optimal passcodes based on patterns of passcodes previously used by the user. It may also analyze passcode usage frequency to generate optimal passcodes. Furthermore, it may evaluate security risks from passcode usage history and generate optimal passcodes. This allows for the provision of optimal passcodes based on passcode usage history.

[0108] The passcode management unit evaluates the security level of the passcode and generates the optimal passcode. For example, if a high security level is required, it generates a complex passcode. If a standard security level is required, it generates a passcode of moderate complexity. If a low security level is acceptable, it generates a simple passcode. This allows the system to provide the optimal passcode based on the required security level.

[0109] The passcode management unit estimates the user's emotions and determines passcode priorities based on those estimates. For example, if the user is stressed, a simple passcode is prioritized. If the user is relaxed, a complex passcode is prioritized. If the user is in a hurry, a passcode that can be generated quickly is prioritized. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows for the provision of optimal passcode priorities according to the user's emotions.

[0110] The passcode management unit monitors passcode usage in real time and generates the optimal passcode. For example, it monitors passcode usage in real time, assesses security risks, and generates the optimal passcode. It monitors passcode usage frequency in real time and generates the optimal passcode. It monitors passcode usage in real time and generates a new passcode when the expiration date approaches. This allows the system to provide the optimal passcode based on passcode usage.

[0111] The passcode management unit sets the passcode expiration date and generates the optimal passcode. For example, if a high security level is required, it generates a passcode with a short expiration date. If a standard security level is required, it generates a passcode with a moderate expiration date. If a lower security level is acceptable, it generates a passcode with a long expiration date. This allows the system to provide the optimal passcode based on the passcode expiration date.

[0112] The monitoring unit estimates the user's emotions and adjusts the monitoring method based on the estimated emotions. For example, if the user is stressed, it provides a monitoring method that quickly detects anomalies. If the user is relaxed, it provides detailed monitoring information. If the user is in a hurry, it provides concise monitoring information. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows for the provision of an optimal monitoring method tailored to the user's emotions.

[0113] The monitoring department analyzes locker usage and selects the optimal monitoring method. For example, frequently used lockers are monitored more frequently, while less frequently used lockers are monitored more regularly. The department analyzes fluctuations in usage and selects the optimal monitoring method. This allows the system to provide the most appropriate monitoring method based on locker usage.

[0114] The monitoring department evaluates the security status of the lockers and selects the most appropriate monitoring method. For example, lockers requiring high security will be monitored rigorously, lockers requiring standard security will be monitored moderately, and lockers where low security is acceptable will be monitored simply. This allows the system to provide the most suitable monitoring method based on the security status of each locker.

[0115] The monitoring unit estimates the user's emotions and determines monitoring priorities based on those estimates. For example, if the user is stressed, important lockers will be monitored first. If the user is relaxed, lockers will be monitored in order. If the user is in a hurry, lockers that can be monitored quickly will be monitored first. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows for the provision of optimal monitoring priorities tailored to the user's emotions.

[0116] The monitoring unit monitors locker usage in real time and selects the optimal monitoring method. For example, it monitors usage in real time and issues an alert if an anomaly is detected. It monitors frequently used lockers in real time and selects the optimal monitoring method. It also monitors changes in usage in real time and selects the optimal monitoring method. This allows for the provision of the most appropriate monitoring method based on locker usage.

[0117] The monitoring unit monitors the security status of lockers in real time and selects the optimal monitoring method. For example, it monitors the security status in real time and issues an alert if an anomaly is detected. It monitors lockers requiring high security in real time and selects the optimal monitoring method. It monitors changes in security status in real time and selects the optimal monitoring method. This allows for the provision of the most appropriate monitoring method based on the security status of the lockers.

[0118] The history section estimates the user's emotions and adjusts the history recording method based on the estimated emotions. For example, if the user is stressed, it provides a concise history recording method. If the user is relaxed, it provides a detailed history recording method. If the user is in a hurry, it provides a history recording method that allows for quick recording. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. This allows for the provision of an optimal history recording method that corresponds to the user's emotions.

[0119] The history department analyzes locker usage history and selects the optimal recording method. For example, it records the history of frequently used lockers in detail and the history of less frequently used lockers concisely. It analyzes fluctuations in usage history and selects the optimal recording method. This allows the system to provide the most suitable recording method based on locker usage history.

[0120] The history department evaluates the security of locker usage history and selects the optimal recording method. For example, it provides a strict recording method for history requiring high security, a moderate recording method for history requiring standard security, and a simple recording method for history where low security is acceptable. This allows the department to provide the optimal recording method based on the security of the locker usage history.

[0121] The history section estimates the user's emotions and determines the priority of the history based on those emotions. For example, if the user is stressed, important history entries are prioritized. If the user is relaxed, history entries are recorded in order. If the user is in a hurry, history entries that can be recorded quickly are prioritized. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. This allows for the provision of optimal history priorities tailored to the user's emotions.

[0122] The history unit records locker usage history in real time and selects the optimal recording method. For example, it records usage history in real time and issues an alert if an anomaly is detected. It records the history of frequently used lockers in real time and selects the optimal recording method. It records changes in usage history in real time and selects the optimal recording method. This allows the system to provide the optimal recording method based on locker usage history.

[0123] The history department monitors the security of locker usage history in real time and selects the optimal recording method. For example, it monitors the security of usage history in real time and issues an alert if an anomaly is detected. It monitors history requiring high security in real time and selects the optimal recording method. It monitors changes in the security of usage history in real time and selects the optimal recording method. This allows for the provision of the optimal recording method based on the security of the locker usage history. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, storage unit, receiving unit, management unit, passcode management unit, monitoring unit, and history unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to order goods online and select a locker as the delivery destination. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12, where the delivery company delivers the goods to the locker and stores the goods in the locker. The receiving unit is implemented by the control unit 46A of the smart device 14, where the user retrieves the goods from the locker using a specified passcode. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and efficiently manages the location of the lockers. The passcode management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the passcode generation method and security standards. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the usage status of the lockers in real time. The history section is implemented by the specific processing unit 290 of the data processing device 12, and efficiently records the locker usage history. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, storage unit, receiving unit, management unit, passcode management unit, monitoring unit, and history unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to order goods online and select a locker as the delivery destination. The storage unit is implemented by the identification processing unit 290 of the data processing unit 12, where the delivery company delivers the goods to the locker and stores the goods in the locker. The receiving unit is implemented by the control unit 46A of the smart glasses 214, where the user retrieves the goods from the locker using a designated passcode. The management unit is implemented by the identification processing unit 290 of the data processing unit 12 and efficiently manages the location of the lockers. The passcode management unit is implemented by the identification processing unit 290 of the data processing unit 12 and manages the passcode generation method and security standards. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the usage status of the lockers in real time. The history section is implemented by the specific processing unit 290 of the data processing device 12, and efficiently records the locker usage history. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, storage unit, receiving unit, management unit, passcode management unit, monitoring unit, and history unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to order goods online and select a locker as the delivery destination. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12, where the delivery company delivers the goods to the locker and stores the goods in the locker. The receiving unit is implemented by the control unit 46A of the headset terminal 314, where the user retrieves the goods from the locker using a specified passcode. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and efficiently manages the location of the lockers. The passcode management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the passcode generation method and security standards. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the usage status of the lockers in real time. The history section is implemented by the specific processing unit 290 of the data processing device 12, and efficiently records the locker usage history. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, storage unit, receiving unit, management unit, passcode management unit, monitoring unit, and history unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to order goods online and select a locker as the delivery destination. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12, where the delivery company delivers the goods to the locker and stores the goods in the locker. The receiving unit is implemented by the control unit 46A of the robot 414, where the user retrieves the goods from the locker using a specified passcode. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and efficiently manages the location of the lockers. The passcode management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the passcode generation method and security standards. The monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the usage status of the lockers in real time. The history section is implemented by the specific processing unit 290 of the data processing device 12, and efficiently records the locker usage history.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The reception desk can also analyze a user's past order history and suggest the most suitable locker options. For example, it can prioritize suggesting lockers the user has frequently used in the past. It can predict and suggest lockers the user will use at specific times based on their past order history. It can analyze a user's past order history and suggest the most efficient locker. This allows the system to suggest the optimal locker based on the user's past order history.

[0126] The management department can also evaluate the safety of locker locations and select the most suitable locations. For example, they can install lockers in areas with low crime rates, in areas with security cameras, or in well-lit areas at night. This allows them to provide the best possible locations based on the safety of the locker installation site.

[0127] The passcode management unit can also analyze passcode usage history and select the optimal passcode generation method. For example, it can generate the optimal passcode based on patterns of passcodes previously used by the user. It can analyze passcode usage frequency and generate the optimal passcode. It can evaluate security risks from passcode usage history and generate the optimal passcode. This allows for the provision of the optimal passcode based on passcode usage history.

[0128] The monitoring unit can also monitor locker usage in real time and select the optimal monitoring method. For example, it can monitor usage in real time and issue an alert if an anomaly is detected. It can monitor frequently used lockers in real time and select the optimal monitoring method. It can monitor changes in usage in real time and select the optimal monitoring method. This allows the system to provide the most suitable monitoring method based on locker usage.

[0129] The history section can record locker usage history in real time and select the optimal recording method. For example, it can record usage history in real time and issue an alert if an anomaly is detected. It can also record the history of frequently used lockers in real time and select the optimal recording method. It can record changes in usage history in real time and select the optimal recording method. This allows the system to provide the optimal recording method based on locker usage history.

[0130] The reception desk can also estimate the user's emotions and present locker options based on those estimates. For example, if the user is stressed, the nearest locker will be prioritized. If the user is relaxed, multiple locker options will be presented in detail. If the user is in a hurry, immediately available lockers will be prioritized. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows the system to provide the most suitable locker options according to the user's emotions.

[0131] The storage unit can also estimate the user's emotions and adjust the storage method based on those emotions. For example, if the user is stressed, it will select a method to store the items quickly. If the user is relaxed, it will select a method to store the items carefully. If the user is in a hurry, it will select a method to store the items in the shortest possible time. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows the system to provide the optimal storage method according to the user's emotions.

[0132] The receiving unit can also estimate the user's emotions and adjust the receiving method based on the estimated emotions. For example, if the user is stressed, a simple receiving method is provided. If the user is relaxed, a detailed receiving method is provided. If the user is in a hurry, a quick receiving method is provided. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows for the provision of the optimal receiving method according to the user's emotions.

[0133] The management department can also estimate the user's emotions and adjust the locker placement based on those estimates. For example, if a user is stressed, a locker might be placed in an easily accessible location. If a user is relaxed, a locker might be placed in a quiet location. If a user is in a hurry, a locker might be placed in a convenient location. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows for the provision of optimal locker placements tailored to the user's emotions.

[0134] The passcode management unit can also estimate the user's emotions and adjust the passcode generation method based on the estimated emotions. For example, if the user is stressed, it can generate a simple passcode. If the user is relaxed, it can generate a complex passcode. If the user is in a hurry, it can provide a passcode that can be generated quickly. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and text analysis. This allows the system to provide the optimal passcode generation method according to the user's emotions.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The reception desk allows users to order products online and select a locker as the delivery location. For example, users can choose a locker near their home or workplace. The reception desk provides an interface for users to select a locker via a website or mobile app. Step 2: The storage unit is where the delivery company delivers the goods to the locker and stores them. For example, the delivery company delivers the goods to the designated locker and stores them there. At this time, a passcode is set for the locker to ensure that the goods are stored securely. The storage unit is equipped with a mechanism that automatically sets the passcode when the locker door is closed. Step 3: The receiving department allows the user to retrieve the item from the locker using a designated passcode. For example, the user is notified via smartphone or email that the item has been delivered, and the notification includes the locker's location and passcode. The user can then open the locker door using the designated passcode and retrieve the item. For instance, when the user arrives at the locker and enters the passcode displayed on their smartphone, the locker door opens and they can retrieve their item.

[0137] 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.

[0138] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0139] 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.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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. 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.

[0155] 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.

[0156] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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. 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.

[0171] 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.

[0172] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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. 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.

[0188] 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.

[0189] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Explanation of Symbols]

[0209] 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 reception desk where users order products online and select a locker as the delivery location, Based on the information received by the reception unit, the delivery company delivers the goods to the locker, and the storage unit stores the goods in the locker. The system includes a receiving unit in which the user can retrieve items stored in the storage unit from a locker using a specified passcode. A system characterized by the following features.

2. It has a management department that manages the location of the lockers. The system according to feature 1.

3. It has a passcode management unit for managing passcodes. The system according to feature 1.

4. It is equipped with a monitoring unit that monitors locker usage in real time. The system according to feature 1.

5. It is equipped with a history section to record the locker usage history. The system according to feature 1.

6. The aforementioned reception unit is Users can choose a locker near their home or workplace. The system according to feature 1.

7. The aforementioned storage unit is The delivery person places the item in the locker and sets a passcode. The system according to feature 1.

8. The receiving section is, The user opens the locker door using a passcode notified via smartphone or email. The system according to feature 1.

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