system

Abstract

The system according to the embodiment aims to efficiently deliver meals within an apartment building and to notify residents appropriately. [Solution] The system according to the embodiment comprises a collection unit, a planning unit, a retrieval unit, a delivery unit, a notification unit, a confirmation unit, and a charging unit. The collection unit collects order information. The planning unit creates a delivery plan based on the order information collected by the collection unit. The retrieval unit retrieves meals from the refrigerated locker. The delivery unit delivers to each residence based on the delivery plan created by the planning unit. The notification unit notifies the resident when the delivery robot arrives at the resident's front door. The confirmation unit confirms receipt of the meal. The charging unit uses an automatic charging system.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately develop efficient delivery plans or notify residents in meal delivery services within apartment buildings, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently deliver meals within an apartment building and to notify residents appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a planning unit, a retrieval unit, a delivery unit, a notification unit, a confirmation unit, and a charging unit. The collection unit collects order information. The planning unit creates a delivery plan based on the order information collected by the collection unit. The retrieval unit retrieves meals from the refrigerated locker. The delivery unit delivers to each residence based on the delivery plan created by the planning unit. The notification unit notifies the resident when the delivery robot arrives at the resident's front door. The confirmation unit confirms receipt of the meal. The charging unit uses an automatic charging system. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently deliver meals within an apartment building and properly notify residents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In one embodiment of the present invention, an autonomous delivery robot located in an apartment building delivers three meals (breakfast, lunch, and dinner) with three soups to a refrigerated locker installed on the first floor of a resident's apartment at 6:00 a.m. every morning. In this delivery system, residents place orders using a dedicated app, which then transmits the order information to the delivery robot. The delivery robot then creates a delivery plan based on the order information and calculates the optimal route to each apartment's front door using map data from the apartment building. The delivery robot is equipped with an obstacle detection system using LIDAR and cameras as autonomous mobility technology, enabling safe movement. The delivery robot uses a robotic arm to retrieve meals from the refrigerated locker and deliver them to each apartment. When the delivery robot arrives at the resident's front door, it notifies the resident via the app and confirms receipt of the meals. After delivering the meals, the delivery robot returns to the refrigerated locker and recharges its battery using an automatic charging system. For example, when a resident places an order using a dedicated app, the app transmits the order information to the delivery robot. The delivery robot then makes a delivery plan based on the order information and uses map data from within the apartment complex to calculate the optimal route to each apartment's front door. The delivery robot is equipped with an obstacle detection system using LIDAR and cameras as autonomous mobility technology, allowing it to move safely. The delivery robot uses its robotic arm to retrieve meals from the refrigerated locker and deliver them to each apartment. When the delivery robot arrives at the resident's front door, it notifies the resident via an app and confirms that the meal has been received. After delivering the meal, the delivery robot returns to the refrigerated locker and charges its battery using an automatic charging system. This allows the delivery system to automate and efficiently carry out the entire process, from collecting residents' order information to delivery, notification, confirmation, and charging.

[0029] A delivery system according to an embodiment includes a collection unit, a planning unit, a retrieval unit, a delivery unit, a notification unit, a confirmation unit, and a charging unit. The collection unit collects order information. The order information includes, but is not limited to, the type of meal, quantity, and delivery time. The collection unit can, for example, retrieve information from a database or collect user input. The planning unit creates a delivery plan based on the order information collected by the collection unit. The delivery plan includes, but is not limited to, route optimization and delivery time setting. The planning unit uses map data within the apartment building to calculate an optimal route to the front door of each unit. The retrieval unit retrieves meals from a refrigerated locker. The retrieval unit can, for example, use a robotic arm to retrieve meals from the refrigerated locker. The delivery unit delivers to each unit based on the delivery plan created by the planning unit. The delivery unit is equipped with an obstacle detection system using, for example, LIDAR or a camera, allowing for safe movement. The notification unit notifies residents when the delivery robot arrives at their front door. The notification unit can notify the resident via a mobile application, for example. The confirmation unit confirms receipt of the meal. The confirmation unit can confirm receipt of the meal via a mobile application, for example. The charging unit uses an automatic charging system. For example, the charging unit can charge the battery using the automatic charging system after the delivery robot returns to the refrigerated locker. As a result, the delivery system according to the embodiment automates and efficiently performs a series of processes from collecting resident order information to delivery, notification, confirmation, and charging.

[0030] The delivery unit may be equipped with an obstacle detection system using LIDAR or a camera. For example, the delivery unit may use LIDAR to scan the surrounding environment and detect obstacles. LIDAR uses laser light to measure distance and identify the location of surrounding objects. For example, the delivery unit may use LIDAR to monitor the status of hallways and elevators in an apartment building in real time and determine a route that avoids obstacles. The delivery unit may also use a camera to capture images of the surrounding environment and detect obstacles using image processing technology. The camera acquires high-resolution images and identifies the location of obstacles using image processing algorithms. For example, the delivery unit may use a camera to monitor the status of common spaces and stairs in an apartment building and determine a route that avoids obstacles. Furthermore, the delivery unit may use a combination of LIDAR and a camera. For example, scanning the surrounding environment using LIDAR and acquiring detailed images using a camera may enable more accurate obstacle detection. This allows the delivery unit to detect obstacles and make safe deliveries.

[0031] The planning unit can calculate an efficient route to the front door of each dwelling unit using map data within the apartment building. The planning unit, for example, uses map data within the apartment building to calculate the optimal route to the front door of each dwelling unit. The map data includes, for example, location information for hallways, elevators, and staircases within the apartment building. The planning unit can calculate a route to reach each dwelling unit in the shortest distance and time based on the map data. For example, the planning unit uses the map data to select an optimal route taking into account elevator usage status and staircase congestion. The planning unit can also update the map data in real time and recalculate routes based on the latest information. For example, the planning unit monitors event information and usage status of common spaces within the apartment building in real time to select a route that avoids congestion. In this way, the planning unit can calculate the optimal route, enabling efficient deliveries.

[0032] The retrieval unit can retrieve meals from the refrigerated locker using a robotic arm. For example, the retrieval unit retrieves meals from the refrigerated locker using a robotic arm. The robotic arm is equipped with a high-precision control system and can retrieve meals accurately. For example, the retrieval unit uses the robotic arm to pick up meals from the refrigerated locker and deliver them to each apartment. The retrieval unit can also program the operation of the robotic arm to perform efficient retrieval. For example, the retrieval unit optimizes the movement pattern of the robotic arm to reduce retrieval time. Furthermore, the retrieval unit has a function to monitor the operation of the robotic arm in real time and automatically stop it if an abnormality occurs. This allows the retrieval unit to retrieve meals accurately by using the robotic arm.

[0033] The notification unit can notify residents through a mobile application. For example, the notification unit notifies residents through a mobile application. The mobile application is installed on the resident's smartphone or tablet and sends a notification when the delivery robot arrives at the resident's front door. For example, the notification unit can notify residents of the delivery robot's arrival time and how to receive the meal through the mobile application. The notification unit can also allow residents to check the delivery status in real time through the mobile application. For example, the notification unit has a function to display the delivery robot's current location and delivery progress. Furthermore, the notification unit can receive feedback from residents through the mobile application. For example, the notification unit provides an interface for residents to send questions or requests regarding delivery. This allows the notification unit to reliably notify residents by notifying them through the app.

[0034] The confirmation unit can confirm meal receipt through a mobile application. The confirmation unit, for example, confirms meal receipt through a mobile application. The mobile application is installed on a resident's smartphone or tablet and sends a notification when the delivery robot arrives at the resident's front door. The resident can confirm meal receipt through the mobile application. For example, the confirmation unit provides a button for the resident to confirm that they have received the meal. The confirmation unit also provides an interface for the resident to submit feedback regarding the meal receipt. For example, the confirmation unit provides a form for the resident to submit an evaluation regarding the quality of the meal and the timing of delivery. Furthermore, the confirmation unit can receive feedback from the resident in real time through the mobile application. This allows the confirmation unit to reliably confirm meal receipt by confirming meal receipt through the app.

[0035] The collection unit can analyze the user's past order history and select the optimal collection method. The collection unit, for example, analyzes the user's past order history and selects the optimal collection method. The past order history includes the frequency of orders and the types of orders. The collection unit provides the user with the optimal collection method based on the past order history. For example, the collection unit prioritizes displaying menu items that the user has frequently ordered in the past. The collection unit can also analyze the user's tendency to order during specific time periods based on the user's past order history and suggest the optimal collection timing. Furthermore, the collection unit can automatically suggest preferred menu items based on the user's past order history. In this way, the collection unit can provide the user with the optimal collection method by analyzing the past order history.

[0036] The collection unit can perform filtering based on the user's current dietary preferences and allergy information when collecting order information. For example, the collection unit performs filtering based on the user's current dietary preferences and allergy information when collecting order information. Dietary preferences include the user's preferences and past order history. Allergy information includes the user's health information and medical data. The collection unit suggests an appropriate menu based on this information. For example, the collection unit automatically excludes menus that include ingredients to which the user is allergic. The collection unit can also preferentially display related menus based on the user's current dietary preferences. Furthermore, the collection unit can also suggest an optimal menu based on the user's dietary preferences and allergy information. As a result, the collection unit can suggest an appropriate menu by filtering based on the user's dietary preferences and allergy information.

[0037] When collecting order information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting order information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. The geographical location information is acquired using GPS data or a location information service. The collection unit provides highly relevant information to the user based on the geographical location information. For example, when the user is in a specific area, the collection unit can prioritize displaying menus that are popular in that area. The collection unit can also prioritize displaying menus of restaurants close to the user's current location. Furthermore, the collection unit can suggest an optimal delivery time based on the user's geographical location information. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0038] The collection unit can analyze the user's social media activity and collect related information when collecting order information. For example, the collection unit analyzes the user's social media activity and collects related information when collecting order information. Social media activity includes the content of posts and the number of likes. The collection unit provides information related to the user based on the social media activity. For example, the collection unit can suggest related menus based on photos of meals shared by the user on social media. The collection unit can also preferentially display menus of restaurants that the user follows on social media. Furthermore, the collection unit can analyze the user's social media activity and suggest preferred menus. In this way, the collection unit can collect related information by analyzing the user's social media activity.

[0039] The planning unit can select the optimal route by referring to past delivery data when creating a delivery plan. For example, the planning unit selects the optimal route by referring to past delivery data when creating a delivery plan. Past delivery data includes delivery times, delivery routes, etc. The planning unit selects the most efficient route based on past delivery data. For example, the planning unit selects a route that delivers the shortest distance and in the shortest time based on past delivery data. The planning unit can also select a route that avoids congestion based on past delivery data. Furthermore, the planning unit can analyze past delivery data and select the safest route. In this way, the planning unit can select the optimal route by referring to past delivery data.

[0040] The planning unit can adjust the route when creating a delivery plan, taking into account the congestion situation within the apartment building in real time. For example, the planning unit adjusts the route when creating a delivery plan, taking into account the congestion situation within the apartment building in real time. The congestion situation is obtained using real-time data and past data. The planning unit selects the optimal route based on the congestion situation. For example, the planning unit monitors the congestion situation within the apartment building in real time and selects a route that avoids congestion. The planning unit can also select the optimal route by taking into account the usage status of elevators within the apartment building. Furthermore, the planning unit can also select the optimal route by taking into account the congestion situation in common spaces within the apartment building. In this way, the planning unit can select the optimal route by taking into account the congestion situation within the apartment building in real time.

[0041] The planning unit can select the optimal route by taking weather information into consideration when creating a delivery plan. For example, the planning unit selects the optimal route by taking weather information into consideration when creating a delivery plan. Weather information is obtained using meteorological data and weather forecasts. The planning unit selects the optimal route based on the weather information. For example, the planning unit may prioritize selecting a route with a roof or an underground passage on rainy days. Furthermore, the planning unit may prioritize selecting a route with a good view on sunny days. Furthermore, the planning unit may prioritize selecting a route with less slippery surfaces on snowy days. In this way, the planning unit can select the optimal route by taking weather information into consideration.

[0042] The planning unit can adjust the route taking into account event information within the apartment building when creating a delivery plan. For example, the planning unit adjusts the route taking into account event information within the apartment building when creating a delivery plan. Event information is acquired based on the type of event, the event time, etc. The planning unit selects the optimal route based on the event information. For example, if an event is being held within the apartment building, the planning unit selects a route that avoids congestion. The planning unit can also select the optimal route if an event is being held in a common space within the apartment building. Furthermore, the planning unit can also suggest the optimal delivery time based on the event information within the apartment building. In this way, the planning unit can select the optimal route by taking into account event information within the apartment building.

[0043] The retrieval unit monitors the temperature and humidity inside the refrigerated locker at the time of retrieval and can select the optimal retrieval timing. For example, the retrieval unit monitors the temperature and humidity inside the refrigerated locker at the time of retrieval and selects the optimal retrieval timing. The temperature and humidity are monitored using a temperature sensor and a humidity sensor. The retrieval unit selects the optimal retrieval timing based on the temperature and humidity data. For example, the retrieval unit performs retrieval when the temperature inside the refrigerated locker is within an appropriate range. The retrieval unit can also perform retrieval when the humidity inside the refrigerated locker is within an appropriate range. Furthermore, the retrieval unit can postpone retrieval when the temperature or humidity inside the refrigerated locker is not within the appropriate range. In this way, the retrieval unit can select the optimal retrieval timing by monitoring the temperature and humidity inside the refrigerated locker.

[0044] The dispensing unit can apply different dispensing algorithms depending on the type of meal when dispensing. For example, the dispensing unit applies different dispensing algorithms depending on the type of meal when dispensing. Meal types are classified based on the type of menu, the type of ingredients, etc. The dispensing unit applies the optimal dispensing algorithm depending on the type of meal. For example, the dispensing unit applies an algorithm for quick dispensing for cold meals. In addition, the dispensing unit can apply an algorithm for careful dispensing for hot meals. Furthermore, the dispensing unit can also apply an algorithm for careful dispensing for delicate meals. In this way, the dispensing unit can maintain the quality of the meal by applying the optimal dispensing algorithm depending on the type of meal.

[0045] The retrieval unit can select the optimal retrieval method by taking into account the location information of the refrigerated locker when retrieving. For example, the retrieval unit selects the optimal retrieval method by taking into account the location information of the refrigerated locker when retrieving. Location information is acquired using GPS data or location information services. The retrieval unit selects the optimal retrieval method based on the location information. For example, if the refrigerated locker is far away, the retrieval unit selects a method that allows for quick retrieval. Also, if the refrigerated locker is close, the retrieval unit can select a method that allows for careful retrieval. Furthermore, the retrieval unit can select the optimal retrieval timing based on the location information of the refrigerated locker. This allows the retrieval unit to select the optimal retrieval method by taking into account the location information of the refrigerated locker.

[0046] The dispensing unit can monitor the packaging condition of the meal at the time of dispensing and select the optimal dispensing method. For example, the dispensing unit monitors the packaging condition of the meal at the time of dispensing and selects the optimal dispensing method. The packaging condition is monitored based on the packaging damage, sealing condition, etc. The dispensing unit selects the optimal dispensing method based on the packaging condition. For example, if the meal is securely packaged, the dispensing unit can select a quick dispensing method. Also, if the meal packaging is delicate, the dispensing unit can select a careful dispensing method. Furthermore, the dispensing unit can select the optimal timing for dispensing based on the packaging condition of the meal. In this way, the dispensing unit can select the optimal dispensing method by monitoring the packaging condition of the meal.

[0047] The delivery unit can acquire obstacle information within the apartment building in real time during delivery and select the optimal route. For example, the delivery unit acquires obstacle information within the apartment building in real time during delivery and selects the optimal route. The obstacle information is acquired using the type of sensor and the data analysis method. The delivery unit selects the optimal route based on the obstacle information. For example, the delivery unit monitors obstacle information within the apartment building in real time and selects a route that avoids obstacles. The delivery unit can also select the optimal route taking into account the usage status of elevators within the apartment building. Furthermore, the delivery unit can also select the optimal route taking into account obstacle information in common spaces within the apartment building. In this way, the delivery unit can select the optimal route by acquiring obstacle information within the apartment building in real time.

[0048] The delivery unit monitors the situation in front of the entrance of each dwelling unit at the time of delivery and can select the optimal delivery timing. For example, the delivery unit monitors the situation in front of the entrance of each dwelling unit at the time of delivery and selects the optimal delivery timing. The situation in front of the entrance is monitored by installing a camera and using criteria for judging the situation. The delivery unit selects the optimal delivery timing based on the situation in front of the entrance. For example, the delivery unit postpones delivery if the entrance of each dwelling unit is crowded. Also, the delivery unit can make a prompt delivery if the entrance of each dwelling unit is empty. Furthermore, the delivery unit can also select the optimal delivery timing based on the situation in front of the entrance of each dwelling unit. In this way, the delivery unit can select the optimal delivery timing by monitoring the situation in front of the entrance of each dwelling unit.

[0049] The delivery unit can select the optimal route at the time of delivery, taking into account the usage status of elevators in the apartment building. For example, the delivery unit selects the optimal route at the time of delivery, taking into account the usage status of elevators in the apartment building. Elevator usage status is obtained using real-time data and past data. The delivery unit selects the optimal route based on the elevator usage status. For example, the delivery unit monitors the usage status of elevators in the apartment building in real time and selects the optimal route. In addition, if the elevators are crowded, the delivery unit can select a route that uses the stairs. Furthermore, the delivery unit can also select the optimal delivery timing based on the elevator usage status. In this way, the delivery unit can select the optimal route by taking into account the usage status of elevators in the apartment building.

[0050] The delivery unit can select the optimal delivery timing by taking into account the resident's schedule information when making a delivery. For example, the delivery unit selects the optimal delivery timing by taking into account the resident's schedule information when making a delivery. The schedule information is acquired using a calendar app or schedule sharing. The delivery unit selects the optimal delivery timing based on the schedule information. For example, the delivery unit makes deliveries during times when the resident is at home based on the resident's schedule information. Furthermore, the delivery unit can make deliveries by avoiding times when the resident is absent based on the resident's schedule information. Furthermore, the delivery unit can also select the optimal delivery timing based on the resident's schedule information. In this way, the delivery unit can select the optimal delivery timing by taking into account the resident's schedule information.

[0051] The notification unit can select the optimal notification method by referring to the resident's past notification history when notifying. For example, the notification unit can select the optimal notification method by referring to the resident's past notification history when notifying. The past notification history includes the frequency of notifications and the type of notification. The notification unit provides the optimal notification method based on the past notification history. For example, the notification unit preferentially uses the notification method that the resident preferred in the past. The notification unit can also select the optimal notification timing based on the resident's past notification history. Furthermore, the notification unit can also select the optimal notification content based on the resident's past notification history. In this way, the notification unit can select the optimal notification method by referring to the resident's past notification history.

[0052] The notification unit can monitor the resident's current situation in real time at the time of notification and select the optimal notification timing. For example, the notification unit can monitor the resident's current situation in real time at the time of notification and select the optimal notification timing. The current situation is monitored using real-time data and situation judgment criteria. The notification unit selects the optimal notification timing based on the current situation. For example, the notification unit delays the notification if the resident is busy. Also, the notification unit can notify the resident immediately if the resident is relaxed. Furthermore, the notification unit can select the optimal notification timing based on the resident's current situation. As a result, the notification unit can select the optimal notification timing by monitoring the resident's current situation in real time.

[0053] The notification unit can select the optimal notification method by taking into consideration the resident's device information when sending a notification. For example, the notification unit selects the optimal notification method by taking into consideration the resident's device information when sending a notification. The device information is acquired based on the type of device and its usage status. The notification unit selects the optimal notification method based on the device information. For example, if the resident is using a smartphone, the notification unit uses push notification. Also, if the resident is using a tablet, the notification unit can provide notifications optimized for a large screen. Furthermore, if the resident is using a smartwatch, the notification unit can provide concise, highly visible notifications. This allows the notification unit to select the optimal notification method by taking into consideration the resident's device information.

[0054] The notification unit can select the optimal notification timing by taking into consideration the resident's schedule information when notifying. For example, the notification unit selects the optimal notification timing by taking into consideration the resident's schedule information when notifying. The schedule information is acquired using a calendar app or schedule sharing. The notification unit selects the optimal notification timing based on the schedule information. For example, the notification unit may send a notification during a time period when the resident is at home based on the resident's schedule information. The notification unit may also send a notification by avoiding a time period when the resident is absent based on the resident's schedule information. Furthermore, the notification unit may also select the optimal notification timing based on the resident's schedule information. In this way, the notification unit can select the optimal notification timing by taking into consideration the resident's schedule information.

[0055] The confirmation unit can select the optimal confirmation method by referring to the resident's past confirmation history when confirming. For example, the confirmation unit selects the optimal confirmation method by referring to the resident's past confirmation history when confirming. The past confirmation history includes the frequency of confirmation, the type of confirmation, etc. The confirmation unit provides the optimal confirmation method based on the past confirmation history. For example, the confirmation unit preferentially uses a confirmation method that the resident has previously preferred. The confirmation unit can also select the optimal confirmation timing based on the resident's past confirmation history. Furthermore, the confirmation unit can also select the optimal confirmation content based on the resident's past confirmation history. In this way, the confirmation unit can select the optimal confirmation method by referring to the resident's past confirmation history.

[0056] The confirmation unit can monitor the resident's current situation in real time during confirmation and select the optimal confirmation timing. For example, the confirmation unit can monitor the resident's current situation in real time during confirmation and select the optimal confirmation timing. The current situation is monitored using real-time data and situation judgment criteria. The confirmation unit selects the optimal confirmation timing based on the current situation. For example, the confirmation unit delays confirmation if the resident is busy. Also, the confirmation unit can check immediately if the resident is relaxed. Furthermore, the confirmation unit can select the optimal confirmation timing based on the resident's current situation. As a result, the confirmation unit can select the optimal confirmation timing by monitoring the resident's current situation in real time.

[0057] The confirmation unit can select the optimal confirmation method by taking into consideration the resident's device information when making a confirmation. For example, the confirmation unit selects the optimal confirmation method by taking into consideration the resident's device information when making a confirmation. The device information is acquired based on the type of device and its usage status. The confirmation unit selects the optimal confirmation method based on the device information. For example, if the resident is using a smartphone, the confirmation unit uses push notification. Also, if the resident is using a tablet, the confirmation unit can perform confirmation optimized for a large screen. Furthermore, if the resident is using a smartwatch, the confirmation unit can also perform a simple and highly visible confirmation. This allows the confirmation unit to select the optimal confirmation method by taking into consideration the resident's device information.

[0058] The confirmation unit can select the optimal confirmation timing when making a confirmation by taking into consideration the resident's schedule information. For example, the confirmation unit selects the optimal confirmation timing when making a confirmation by taking into consideration the resident's schedule information. The schedule information is acquired using a calendar app or schedule sharing. The confirmation unit selects the optimal confirmation timing based on the schedule information. For example, the confirmation unit performs confirmation during a time period when the resident is at home based on the resident's schedule information. Furthermore, the confirmation unit can perform confirmation while avoiding a time period when the resident is absent based on the resident's schedule information. Furthermore, the confirmation unit can also select the optimal confirmation timing based on the resident's schedule information. In this way, the confirmation unit can select the optimal confirmation timing by taking into consideration the resident's schedule information.

[0059] The charging unit monitors the battery status of the delivery robot in real time during charging and can select the optimal charging timing. For example, the charging unit monitors the battery status of the delivery robot in real time during charging and can select the optimal charging timing. The battery status is monitored based on the remaining battery level and charging cycle. The charging unit selects the optimal charging timing based on the battery status. For example, the charging unit immediately charges the delivery robot when its battery is low. The charging unit can also delay charging if the delivery robot's battery has sufficient power. Furthermore, the charging unit can select the optimal charging timing based on the delivery robot's battery status. As a result, the charging unit can select the optimal charging timing by monitoring the delivery robot's battery status in real time.

[0060] The charging unit can select the optimal charging timing when charging, taking into account the power usage status within the apartment building. For example, the charging unit selects the optimal charging timing when charging, taking into account the power usage status within the apartment building. The power usage status is obtained using real-time data and past data. The charging unit selects the optimal charging timing based on the power usage status. For example, the charging unit charges during a time period when power usage within the apartment building is low. The charging unit can also charge by avoiding a time period when power usage within the apartment building is high. Furthermore, the charging unit can also select the optimal charging timing based on the power usage status within the apartment building. In this way, the charging unit can select the optimal charging timing by taking into account the power usage status within the apartment building.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The delivery system may further include a voice recognition unit. The voice recognition unit can analyze residents' voice commands and collect order information and adjust delivery plans. For example, if a resident verbally instructs, "Cancel tomorrow's breakfast," the voice recognition unit analyzes the instruction and sends cancellation information to the collection unit. Also, if a resident verbally instructs, "Change the delivery time," the voice recognition unit can analyze the instruction and instruct the planning unit to set a new delivery time. Furthermore, the voice recognition unit can adjust the operation of the delivery robot in real time based on the resident's voice commands. This allows residents to operate the delivery system more intuitively using voice commands.

[0063] The delivery unit is further equipped with a temperature sensor, which can monitor the temperature of meals in real time during delivery. The temperature sensor monitors meals to ensure they are delivered at the appropriate temperature and can adjust the temperature as necessary. For example, the delivery unit can use the temperature sensor to activate a keep-warm function if the temperature of the meal drops. Also, if the meal is overheated, the delivery unit can activate a cooling function. Furthermore, the delivery unit can select the optimal delivery route to maintain the quality of the meal based on the data from the temperature sensor. This allows the delivery unit to properly manage the temperature of the meal and deliver it while maintaining quality.

[0064] The planning unit can further take traffic information into consideration when creating a delivery plan. Traffic information is obtained using real-time data and past data. The planning unit selects the optimal delivery route based on the traffic information. For example, the planning unit monitors traffic congestion information around an apartment building in real time and selects a route that avoids congestion. The planning unit can also select the optimal route by taking into consideration traffic accidents and construction information. Furthermore, the planning unit can adjust delivery times based on traffic information. In this way, the planning unit can create an efficient delivery plan by taking traffic information into consideration.

[0065] The dispenser can further include a function for measuring the weight of the meal. A weight sensor is used to measure the weight of the meal when it is dispensed, and an appropriate dispenser method is selected. For example, the dispenser can use the weight sensor to select a careful dispenser method if the meal is heavy, or a quick dispenser method if the meal is light. Furthermore, the dispenser can also optimize the dispenser order for the meals based on the data from the weight sensor. This allows the dispenser to select an appropriate dispenser method by taking the weight of the meals into consideration.

[0066] The notification unit can also work with residents' calendar apps to automatically add delivery schedules to their calendars. For example, when a resident orders a delivery, the notification unit sends that information to the calendar app, which automatically adds the delivery schedule. If the delivery schedule is changed, the notification unit can send the change information to the calendar app and update the calendar. Furthermore, the notification unit can obtain schedule information from the resident's calendar app and adjust the delivery time. This allows the notification unit to make deliveries that fit the resident's schedule.

[0067] The confirmation unit can further use a two-dimensional code (e.g., a QR code (registered trademark)) to confirm receipt of the meal. For example, when the delivery robot arrives at the resident's front door, the two-dimensional code is displayed to the resident. The resident can scan the two-dimensional code using a smartphone camera to confirm receipt. The confirmation unit can also use the two-dimensional code to confirm the quality of the meal. For example, after the resident receives the meal, the resident can scan the two-dimensional code to send feedback on the quality of the meal. Furthermore, the confirmation unit can also use the two-dimensional code to provide meal tracking information. Thus, by using the two-dimensional code, the confirmation unit can easily and reliably confirm receipt.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects order information. The order information includes, but is not limited to, the type of meal, the quantity, and the delivery time. The collection unit can, for example, obtain the information from a database or collect user input. Step 2: The planning unit creates a delivery plan based on the order information collected by the collection unit. The delivery plan may include, but is not limited to, route optimization and delivery time setting. The planning unit uses map data within the apartment building to calculate the optimal route to the front door of each apartment. Step 3: The retrieval unit retrieves the meal from the refrigerated locker. The retrieval unit can, for example, use a robotic arm to retrieve the meal from the refrigerated locker. Step 4: The delivery department delivers to each apartment based on the delivery plan created by the planning department. The delivery department is equipped with an obstacle detection system using, for example, LIDAR or cameras, to move safely. Step 5: The notification unit notifies the resident when the delivery robot arrives at the resident's doorstep. The notification unit can notify the resident, for example, through a mobile application. Step 6: The confirmation unit confirms receipt of the meal. The confirmation unit can, for example, confirm receipt of the meal through a mobile application. Step 7: The charging unit uses the automatic charging system. For example, the charging unit can use the automatic charging system to charge the battery after the delivery robot returns to the refrigerated locker.

[0070] (Example 2) In one embodiment of the present invention, an autonomous delivery robot located in an apartment building delivers three meals (breakfast, lunch, and dinner) with three soups to a refrigerated locker installed on the first floor of a resident's apartment at 6:00 a.m. every morning. In this delivery system, residents place orders using a dedicated app, which then transmits the order information to the delivery robot. The delivery robot then creates a delivery plan based on the order information and calculates the optimal route to each apartment's front door using map data from the apartment building. The delivery robot is equipped with an obstacle detection system using LIDAR and cameras as autonomous mobility technology, enabling safe movement. The delivery robot uses a robotic arm to retrieve meals from the refrigerated locker and deliver them to each apartment. When the delivery robot arrives at the resident's front door, it notifies the resident via the app and confirms receipt of the meals. After delivering the meals, the delivery robot returns to the refrigerated locker and recharges its battery using an automatic charging system. For example, when a resident places an order using a dedicated app, the app transmits the order information to the delivery robot. The delivery robot then makes a delivery plan based on the order information and uses map data from within the apartment complex to calculate the optimal route to each apartment's front door. The delivery robot is equipped with an obstacle detection system using LIDAR and cameras as autonomous mobility technology, allowing it to move safely. The delivery robot uses its robotic arm to retrieve meals from the refrigerated locker and deliver them to each apartment. When the delivery robot arrives at the resident's front door, it notifies the resident via an app and confirms that the meal has been received. After delivering the meal, the delivery robot returns to the refrigerated locker and charges its battery using an automatic charging system. This allows the delivery system to automate and efficiently carry out the entire process, from collecting residents' order information to delivery, notification, confirmation, and charging.

[0071] A delivery system according to an embodiment includes a collection unit, a planning unit, a retrieval unit, a delivery unit, a notification unit, a confirmation unit, and a charging unit. The collection unit collects order information. The order information includes, but is not limited to, the type of meal, quantity, and delivery time. The collection unit can, for example, retrieve information from a database or collect user input. The planning unit creates a delivery plan based on the order information collected by the collection unit. The delivery plan includes, but is not limited to, route optimization and delivery time setting. The planning unit uses map data within the apartment building to calculate an optimal route to the front door of each unit. The retrieval unit retrieves meals from a refrigerated locker. The retrieval unit can, for example, use a robotic arm to retrieve meals from the refrigerated locker. The delivery unit delivers to each unit based on the delivery plan created by the planning unit. The delivery unit is equipped with an obstacle detection system using, for example, LIDAR or a camera, allowing for safe movement. The notification unit notifies residents when the delivery robot arrives at their front door. The notification unit can notify the resident via a mobile application, for example. The confirmation unit confirms receipt of the meal. The confirmation unit can confirm receipt of the meal via a mobile application, for example. The charging unit uses an automatic charging system. For example, the charging unit can charge the battery using the automatic charging system after the delivery robot returns to the refrigerated locker. As a result, the delivery system according to the embodiment automates and efficiently performs a series of processes from collecting resident order information to delivery, notification, confirmation, and charging.

[0072] The delivery unit may be equipped with an obstacle detection system using LIDAR or a camera. For example, the delivery unit may use LIDAR to scan the surrounding environment and detect obstacles. LIDAR uses laser light to measure distance and identify the location of surrounding objects. For example, the delivery unit may use LIDAR to monitor the status of hallways and elevators in an apartment building in real time and determine a route that avoids obstacles. The delivery unit may also use a camera to capture images of the surrounding environment and detect obstacles using image processing technology. The camera acquires high-resolution images and identifies the location of obstacles using image processing algorithms. For example, the delivery unit may use a camera to monitor the status of common spaces and stairs in an apartment building and determine a route that avoids obstacles. Furthermore, the delivery unit may use a combination of LIDAR and a camera. For example, scanning the surrounding environment using LIDAR and acquiring detailed images using a camera may enable more accurate obstacle detection. This allows the delivery unit to detect obstacles and make safe deliveries.

[0073] The planning unit can calculate an efficient route to the front door of each dwelling unit using map data within the apartment building. The planning unit, for example, uses map data within the apartment building to calculate the optimal route to the front door of each dwelling unit. The map data includes, for example, location information for hallways, elevators, and staircases within the apartment building. The planning unit can calculate a route to reach each dwelling unit in the shortest distance and time based on the map data. For example, the planning unit uses the map data to select an optimal route taking into account elevator usage status and staircase congestion. The planning unit can also update the map data in real time and recalculate routes based on the latest information. For example, the planning unit monitors event information and usage status of common spaces within the apartment building in real time to select a route that avoids congestion. In this way, the planning unit can calculate the optimal route, enabling efficient deliveries.

[0074] The retrieval unit can retrieve meals from the refrigerated locker using a robotic arm. For example, the retrieval unit retrieves meals from the refrigerated locker using a robotic arm. The robotic arm is equipped with a high-precision control system and can retrieve meals accurately. For example, the retrieval unit uses the robotic arm to pick up meals from the refrigerated locker and deliver them to each apartment. The retrieval unit can also program the operation of the robotic arm to perform efficient retrieval. For example, the retrieval unit optimizes the movement pattern of the robotic arm to reduce retrieval time. Furthermore, the retrieval unit has a function to monitor the operation of the robotic arm in real time and automatically stop it if an abnormality occurs. This allows the retrieval unit to retrieve meals accurately by using the robotic arm.

[0075] The notification unit can notify residents through a mobile application. For example, the notification unit notifies residents through a mobile application. The mobile application is installed on the resident's smartphone or tablet and sends a notification when the delivery robot arrives at the resident's front door. For example, the notification unit can notify residents of the delivery robot's arrival time and how to receive the meal through the mobile application. The notification unit can also allow residents to check the delivery status in real time through the mobile application. For example, the notification unit has a function to display the delivery robot's current location and delivery progress. Furthermore, the notification unit can receive feedback from residents through the mobile application. For example, the notification unit provides an interface for residents to send questions or requests regarding delivery. This allows the notification unit to reliably notify residents by notifying them through the app.

[0076] The confirmation unit can confirm meal receipt through a mobile application. The confirmation unit, for example, confirms meal receipt through a mobile application. The mobile application is installed on a resident's smartphone or tablet and sends a notification when the delivery robot arrives at the resident's front door. The resident can confirm meal receipt through the mobile application. For example, the confirmation unit provides a button for the resident to confirm that they have received the meal. The confirmation unit also provides an interface for the resident to submit feedback regarding the meal receipt. For example, the confirmation unit provides a form for the resident to submit an evaluation regarding the quality of the meal and the timing of delivery. Furthermore, the confirmation unit can receive feedback from the resident in real time through the mobile application. This allows the confirmation unit to reliably confirm meal receipt by confirming meal receipt through the app.

[0077] The collection unit can analyze the user's emotions and adjust the timing of collecting order information based on the analyzed user's emotions. The collection unit, for example, analyzes the user's emotions and adjusts the timing of collecting order information based on the analyzed user's emotions. The user's emotions are analyzed using an emotion analysis algorithm. For example, the collection unit can analyze the user's facial expressions and voice and calculate an emotion score. The collection unit adjusts the timing of collecting order information based on the emotion score. For example, if the user is feeling stressed, the order information can be collected during a time when the user is able to relax. Also, if the user is busy, the order information can be collected in a short time. Furthermore, if the user is relaxed, detailed order information can also be collected. In this way, the collection unit can adjust the timing of collecting order information according to the user's emotions and collect order information at a more appropriate time.

[0078] The collection unit can analyze the user's past order history and select the optimal collection method. The collection unit, for example, analyzes the user's past order history and selects the optimal collection method. The past order history includes the frequency of orders and the types of orders. The collection unit provides the user with the optimal collection method based on the past order history. For example, the collection unit prioritizes displaying menu items that the user has frequently ordered in the past. The collection unit can also analyze the user's tendency to order during specific time periods based on the user's past order history and suggest the optimal collection timing. Furthermore, the collection unit can automatically suggest preferred menu items based on the user's past order history. In this way, the collection unit can provide the user with the optimal collection method by analyzing the past order history.

[0079] The collection unit can perform filtering based on the user's current dietary preferences and allergy information when collecting order information. For example, the collection unit performs filtering based on the user's current dietary preferences and allergy information when collecting order information. Dietary preferences include the user's preferences and past order history. Allergy information includes the user's health information and medical data. The collection unit suggests an appropriate menu based on this information. For example, the collection unit automatically excludes menus that include ingredients to which the user is allergic. The collection unit can also preferentially display related menus based on the user's current dietary preferences. Furthermore, the collection unit can also suggest an optimal menu based on the user's dietary preferences and allergy information. As a result, the collection unit can suggest an appropriate menu by filtering based on the user's dietary preferences and allergy information.

[0080] The collection unit can estimate the user's emotions and determine the priority of the order information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the order information to be collected based on the estimated user emotions. The user's emotions are analyzed using an emotion analysis algorithm. For example, the collection unit can analyze the user's facial expressions and voice and calculate an emotion score. The collection unit determines the priority of the order information based on the emotion score. For example, if the user is in a hurry, menus that are easy to prepare can be displayed preferentially. Also, if the user is relaxed, menus that can be enjoyed over time can be displayed preferentially. Furthermore, if the user is feeling stressed, menus that have a relaxing effect can be displayed preferentially. In this way, the collection unit can prioritize the order information by determining the priority of the order information according to the user's emotions, thereby preferentially collecting more appropriate information.

[0081] When collecting order information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting order information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. The geographical location information is acquired using GPS data or a location information service. The collection unit provides highly relevant information to the user based on the geographical location information. For example, when the user is in a specific area, the collection unit can prioritize displaying menus that are popular in that area. The collection unit can also prioritize displaying menus of restaurants close to the user's current location. Furthermore, the collection unit can suggest an optimal delivery time based on the user's geographical location information. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0082] The collection unit can analyze the user's social media activity and collect related information when collecting order information. For example, the collection unit analyzes the user's social media activity and collects related information when collecting order information. Social media activity includes the content of posts and the number of likes. The collection unit provides information related to the user based on the social media activity. For example, the collection unit can suggest related menus based on photos of meals shared by the user on social media. The collection unit can also preferentially display menus of restaurants that the user follows on social media. Furthermore, the collection unit can analyze the user's social media activity and suggest preferred menus. In this way, the collection unit can collect related information by analyzing the user's social media activity.

[0083] The planning unit can estimate the user's emotions and adjust the delivery plan creation method based on the estimated user emotions. The planning unit, for example, estimates the user's emotions and adjusts the delivery plan creation method based on the estimated user emotions. The user's emotions are analyzed using an emotion analysis algorithm. For example, the planning unit can analyze the user's facial expressions and voice and calculate an emotion score. The planning unit adjusts the delivery plan creation method based on the emotion score. For example, if the user is in a hurry, the delivery plan can be created by prioritizing the shortest route. Also, if the user is relaxed, the delivery plan can be created by prioritizing a scenic route. Furthermore, if the user is feeling stressed, the delivery plan can be created by prioritizing a route that reduces stress. In this way, the planning unit can create a more appropriate delivery plan by adjusting the delivery plan creation method according to the user's emotions.

[0084] The planning unit can select the optimal route by referring to past delivery data when creating a delivery plan. For example, the planning unit selects the optimal route by referring to past delivery data when creating a delivery plan. Past delivery data includes delivery times, delivery routes, etc. The planning unit selects the most efficient route based on past delivery data. For example, the planning unit selects a route that delivers the shortest distance and in the shortest time based on past delivery data. The planning unit can also select a route that avoids congestion based on past delivery data. Furthermore, the planning unit can analyze past delivery data and select the safest route. In this way, the planning unit can select the optimal route by referring to past delivery data.

[0085] The planning unit can adjust the route when creating a delivery plan, taking into account the congestion situation within the apartment building in real time. For example, the planning unit adjusts the route when creating a delivery plan, taking into account the congestion situation within the apartment building in real time. The congestion situation is obtained using real-time data and past data. The planning unit selects the optimal route based on the congestion situation. For example, the planning unit monitors the congestion situation within the apartment building in real time and selects a route that avoids congestion. The planning unit can also select the optimal route by taking into account the usage status of elevators within the apartment building. Furthermore, the planning unit can also select the optimal route by taking into account the congestion situation in common spaces within the apartment building. In this way, the planning unit can select the optimal route by taking into account the congestion situation within the apartment building in real time.

[0086] The planning unit can estimate the user's emotions and determine the priority of delivery plans based on the estimated user emotions. The planning unit, for example, estimates the user's emotions and determines the priority of delivery plans based on the estimated user emotions. The user's emotions are analyzed using an emotion analysis algorithm. For example, the planning unit can analyze the user's facial expressions and voice and calculate an emotion score. The planning unit determines the priority of delivery plans based on the emotion score. For example, if the user is in a hurry, the planning unit can prioritize the shortest route when creating a delivery plan. Also, if the user is relaxed, the planning unit can prioritize a scenic route when creating a delivery plan. Furthermore, if the user is feeling stressed, the planning unit can prioritize a route that will reduce stress when creating a delivery plan. In this way, the planning unit can create a more appropriate delivery plan by determining the priority of delivery plans according to the user's emotions.

[0087] The planning unit can select the optimal route by taking weather information into consideration when creating a delivery plan. For example, the planning unit selects the optimal route by taking weather information into consideration when creating a delivery plan. Weather information is obtained using meteorological data and weather forecasts. The planning unit selects the optimal route based on the weather information. For example, the planning unit may prioritize selecting a route with a roof or an underground passage on rainy days. Furthermore, the planning unit may prioritize selecting a route with a good view on sunny days. Furthermore, the planning unit may prioritize selecting a route with less slippery surfaces on snowy days. In this way, the planning unit can select the optimal route by taking weather information into consideration.

[0088] The planning unit can adjust the route taking into account event information within the apartment building when creating a delivery plan. For example, the planning unit adjusts the route taking into account event information within the apartment building when creating a delivery plan. Event information is acquired based on the type of event, the event time, etc. The planning unit selects the optimal route based on the event information. For example, if an event is being held within the apartment building, the planning unit selects a route that avoids congestion. The planning unit can also select the optimal route if an event is being held in a common space within the apartment building. Furthermore, the planning unit can also suggest the optimal delivery time based on the event information within the apartment building. In this way, the planning unit can select the optimal route by taking into account event information within the apartment building.

[0089] The retrieval unit can estimate the user's emotion and adjust the retrieval method based on the estimated user's emotion. The retrieval unit, for example, estimates the user's emotion and adjusts the retrieval method based on the estimated user's emotion. The user's emotion is analyzed using an emotion analysis algorithm. For example, the retrieval unit can analyze the user's facial expression and voice and calculate an emotion score. The retrieval unit adjusts the retrieval method based on the emotion score. For example, if the user is in a hurry, the retrieval unit can select a method that allows for quick retrieval. Also, if the user is relaxed, the retrieval unit can select a method that allows for careful retrieval. Furthermore, if the user is feeling stressed, the retrieval unit can select a retrieval method that reduces stress. In this way, the retrieval unit can adjust the retrieval method according to the user's emotion, thereby enabling more appropriate retrieval.

[0090] The retrieval unit monitors the temperature and humidity inside the refrigerated locker at the time of retrieval and can select the optimal retrieval timing. For example, the retrieval unit monitors the temperature and humidity inside the refrigerated locker at the time of retrieval and selects the optimal retrieval timing. The temperature and humidity are monitored using a temperature sensor and a humidity sensor. The retrieval unit selects the optimal retrieval timing based on the temperature and humidity data. For example, the retrieval unit performs retrieval when the temperature inside the refrigerated locker is within an appropriate range. The retrieval unit can also perform retrieval when the humidity inside the refrigerated locker is within an appropriate range. Furthermore, the retrieval unit can postpone retrieval when the temperature or humidity inside the refrigerated locker is not within the appropriate range. In this way, the retrieval unit can select the optimal retrieval timing by monitoring the temperature and humidity inside the refrigerated locker.

[0091] The dispensing unit can apply different dispensing algorithms depending on the type of meal when dispensing. For example, the dispensing unit applies different dispensing algorithms depending on the type of meal when dispensing. Meal types are classified based on the type of menu, the type of ingredients, etc. The dispensing unit applies the optimal dispensing algorithm depending on the type of meal. For example, the dispensing unit applies an algorithm for quick dispensing for cold meals. In addition, the dispensing unit can apply an algorithm for careful dispensing for hot meals. Furthermore, the dispensing unit can also apply an algorithm for careful dispensing for delicate meals. In this way, the dispensing unit can maintain the quality of the meal by applying the optimal dispensing algorithm depending on the type of meal.

[0092] The retrieval unit can estimate the user's emotions and determine the retrieval priorities based on the estimated user emotions. The retrieval unit, for example, estimates the user's emotions and determines the retrieval priorities based on the estimated user emotions. The user's emotions are analyzed using an emotion analysis algorithm. For example, the retrieval unit can analyze the user's facial expressions and voice and calculate an emotion score. The retrieval unit determines the retrieval priorities based on the emotion score. For example, if the user is in a hurry, the retrieval unit can select a method that allows for quick retrieval. Also, if the user is relaxed, the retrieval unit can select a method that allows for careful retrieval. Furthermore, if the user is feeling stressed, the retrieval unit can select a retrieval method that reduces stress. In this way, the retrieval unit can determine the retrieval priorities according to the user's emotions, thereby enabling more appropriate retrieval.

[0093] The retrieval unit can select the optimal retrieval method by taking into account the location information of the refrigerated locker when retrieving. For example, the retrieval unit selects the optimal retrieval method by taking into account the location information of the refrigerated locker when retrieving. Location information is acquired using GPS data or location information services. The retrieval unit selects the optimal retrieval method based on the location information. For example, if the refrigerated locker is far away, the retrieval unit selects a method that allows for quick retrieval. Also, if the refrigerated locker is close, the retrieval unit can select a method that allows for careful retrieval. Furthermore, the retrieval unit can select the optimal retrieval timing based on the location information of the refrigerated locker. This allows the retrieval unit to select the optimal retrieval method by taking into account the location information of the refrigerated locker.

[0094] The dispensing unit can monitor the packaging condition of the meal at the time of dispensing and select the optimal dispensing method. For example, the dispensing unit monitors the packaging condition of the meal at the time of dispensing and selects the optimal dispensing method. The packaging condition is monitored based on the packaging damage, sealing condition, etc. The dispensing unit selects the optimal dispensing method based on the packaging condition. For example, if the meal is securely packaged, the dispensing unit can select a quick dispensing method. Also, if the meal packaging is delicate, the dispensing unit can select a careful dispensing method. Furthermore, the dispensing unit can select the optimal timing for dispensing based on the packaging condition of the meal. In this way, the dispensing unit can select the optimal dispensing method by monitoring the packaging condition of the meal.

[0095] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated user's emotions. The delivery unit, for example, estimates the user's emotions and adjusts the delivery method based on the estimated user's emotions. The user's emotions are analyzed using an emotion analysis algorithm. For example, the delivery unit can analyze the user's facial expressions and voice and calculate an emotion score. The delivery unit adjusts the delivery method based on the emotion score. For example, if the user is in a hurry, the delivery unit can select a method that allows for quick delivery. Also, if the user is relaxed, the delivery unit can select a method that allows for careful delivery. Furthermore, if the user is feeling stressed, the delivery unit can select a delivery method that reduces stress. In this way, the delivery unit can adjust the delivery method according to the user's emotions, enabling more appropriate delivery.

[0096] The delivery unit can acquire obstacle information within the apartment building in real time during delivery and select the optimal route. For example, the delivery unit acquires obstacle information within the apartment building in real time during delivery and selects the optimal route. The obstacle information is acquired using the type of sensor and the data analysis method. The delivery unit selects the optimal route based on the obstacle information. For example, the delivery unit monitors obstacle information within the apartment building in real time and selects a route that avoids obstacles. The delivery unit can also select the optimal route taking into account the usage status of elevators within the apartment building. Furthermore, the delivery unit can also select the optimal route taking into account obstacle information in common spaces within the apartment building. In this way, the delivery unit can select the optimal route by acquiring obstacle information within the apartment building in real time.

[0097] The delivery unit monitors the situation in front of the entrance of each dwelling unit at the time of delivery and can select the optimal delivery timing. For example, the delivery unit monitors the situation in front of the entrance of each dwelling unit at the time of delivery and selects the optimal delivery timing. The situation in front of the entrance is monitored by installing a camera and using criteria for judging the situation. The delivery unit selects the optimal delivery timing based on the situation in front of the entrance. For example, the delivery unit postpones delivery if the entrance of each dwelling unit is crowded. Also, the delivery unit can make a prompt delivery if the entrance of each dwelling unit is empty. Furthermore, the delivery unit can also select the optimal delivery timing based on the situation in front of the entrance of each dwelling unit. In this way, the delivery unit can select the optimal delivery timing by monitoring the situation in front of the entrance of each dwelling unit.

[0098] The delivery unit can estimate the user's emotions and determine delivery priorities based on the estimated user emotions. The delivery unit, for example, estimates the user's emotions and determines delivery priorities based on the estimated user emotions. The user's emotions are analyzed using an emotion analysis algorithm. For example, the delivery unit can analyze the user's facial expressions and voice and calculate an emotion score. The delivery unit determines delivery priorities based on the emotion score. For example, if the user is in a hurry, a method that allows for quick delivery can be selected. Also, if the user is relaxed, a method that allows for careful delivery can be selected. Furthermore, if the user is feeling stressed, a delivery method that reduces stress can also be selected. In this way, the delivery unit can determine delivery priorities according to the user's emotions, enabling more appropriate deliveries.

[0099] The delivery unit can select the optimal route at the time of delivery, taking into account the usage status of elevators in the apartment building. For example, the delivery unit selects the optimal route at the time of delivery, taking into account the usage status of elevators in the apartment building. Elevator usage status is obtained using real-time data and past data. The delivery unit selects the optimal route based on the elevator usage status. For example, the delivery unit monitors the usage status of elevators in the apartment building in real time and selects the optimal route. In addition, if the elevators are crowded, the delivery unit can select a route that uses the stairs. Furthermore, the delivery unit can also select the optimal delivery timing based on the elevator usage status. In this way, the delivery unit can select the optimal route by taking into account the usage status of elevators in the apartment building.

[0100] The delivery unit can select the optimal delivery timing by taking into account the resident's schedule information when making a delivery. For example, the delivery unit selects the optimal delivery timing by taking into account the resident's schedule information when making a delivery. The schedule information is acquired using a calendar app or schedule sharing. The delivery unit selects the optimal delivery timing based on the schedule information. For example, the delivery unit makes deliveries during times when the resident is at home based on the resident's schedule information. Furthermore, the delivery unit can make deliveries by avoiding times when the resident is absent based on the resident's schedule information. Furthermore, the delivery unit can also select the optimal delivery timing based on the resident's schedule information. In this way, the delivery unit can select the optimal delivery timing by taking into account the resident's schedule information.

[0101] The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. For example, the notification unit estimates the user's emotion and adjusts the notification method based on the estimated user's emotion. The user's emotion is analyzed using an emotion analysis algorithm. For example, the notification unit can analyze the user's facial expression and voice and calculate an emotion score. The notification unit adjusts the notification method based on the emotion score. For example, if the user is nervous, the notification can be made in a calm tone. Also, if the user is relaxed, the notification can be made in a bright tone. Furthermore, if the user is in a hurry, the notification can be made quickly and concisely. In this way, the notification unit can adjust the notification method according to the user's emotion, thereby enabling more appropriate notification.

[0102] The notification unit can select the optimal notification method by referring to the resident's past notification history when notifying. For example, the notification unit can select the optimal notification method by referring to the resident's past notification history when notifying. The past notification history includes the frequency of notifications and the type of notification. The notification unit provides the optimal notification method based on the past notification history. For example, the notification unit preferentially uses the notification method that the resident preferred in the past. The notification unit can also select the optimal notification timing based on the resident's past notification history. Furthermore, the notification unit can also select the optimal notification content based on the resident's past notification history. In this way, the notification unit can select the optimal notification method by referring to the resident's past notification history.

[0103] The notification unit can monitor the resident's current situation in real time at the time of notification and select the optimal notification timing. For example, the notification unit can monitor the resident's current situation in real time at the time of notification and select the optimal notification timing. The current situation is monitored using real-time data and situation judgment criteria. The notification unit selects the optimal notification timing based on the current situation. For example, the notification unit delays the notification if the resident is busy. Also, the notification unit can notify the resident immediately if the resident is relaxed. Furthermore, the notification unit can select the optimal notification timing based on the resident's current situation. As a result, the notification unit can select the optimal notification timing by monitoring the resident's current situation in real time.

[0104] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit, for example, estimates the user's emotion and determines the priority of notifications based on the estimated user's emotion. The user's emotion is analyzed using an emotion analysis algorithm. For example, the notification unit can analyze the user's facial expression and voice and calculate an emotion score. The notification unit determines the priority of notifications based on the emotion score. For example, if the user is in a hurry, the notification can be sent quickly. Also, if the user is relaxed, the notification can be sent carefully. Furthermore, if the user is feeling stressed, a notification method that reduces stress can be selected. In this way, the notification unit can determine the priority of notifications according to the user's emotion, thereby enabling more appropriate notifications.

[0105] The notification unit can select the optimal notification method by taking into consideration the resident's device information when sending a notification. For example, the notification unit selects the optimal notification method by taking into consideration the resident's device information when sending a notification. The device information is acquired based on the type of device and its usage status. The notification unit selects the optimal notification method based on the device information. For example, if the resident is using a smartphone, the notification unit uses push notification. Also, if the resident is using a tablet, the notification unit can provide notifications optimized for a large screen. Furthermore, if the resident is using a smartwatch, the notification unit can provide concise, highly visible notifications. This allows the notification unit to select the optimal notification method by taking into consideration the resident's device information.

[0106] The notification unit can select the optimal notification timing by taking into consideration the resident's schedule information when notifying. For example, the notification unit selects the optimal notification timing by taking into consideration the resident's schedule information when notifying. The schedule information is acquired using a calendar app or schedule sharing. The notification unit selects the optimal notification timing based on the schedule information. For example, the notification unit may send a notification during a time period when the resident is at home based on the resident's schedule information. The notification unit may also send a notification by avoiding a time period when the resident is absent based on the resident's schedule information. Furthermore, the notification unit may also select the optimal notification timing based on the resident's schedule information. In this way, the notification unit can select the optimal notification timing by taking into consideration the resident's schedule information.

[0107] The confirmation unit can estimate the user's emotion and adjust the confirmation method based on the estimated user's emotion. For example, the confirmation unit estimates the user's emotion and adjusts the confirmation method based on the estimated user's emotion. The user's emotion is analyzed using an emotion analysis algorithm. For example, the confirmation unit can analyze the user's facial expression and voice and calculate an emotion score. The confirmation unit adjusts the confirmation method based on the emotion score. For example, if the user is nervous, the confirmation can be performed in a calm tone. Also, if the user is relaxed, the confirmation can be performed in a bright tone. Furthermore, if the user is in a hurry, the confirmation can be performed quickly and concisely. In this way, the confirmation unit can adjust the confirmation method according to the user's emotion, thereby enabling more appropriate confirmation.

[0108] The confirmation unit can select the optimal confirmation method by referring to the resident's past confirmation history when confirming. For example, the confirmation unit selects the optimal confirmation method by referring to the resident's past confirmation history when confirming. The past confirmation history includes the frequency of confirmation, the type of confirmation, etc. The confirmation unit provides the optimal confirmation method based on the past confirmation history. For example, the confirmation unit preferentially uses a confirmation method that the resident has previously preferred. The confirmation unit can also select the optimal confirmation timing based on the resident's past confirmation history. Furthermore, the confirmation unit can also select the optimal confirmation content based on the resident's past confirmation history. In this way, the confirmation unit can select the optimal confirmation method by referring to the resident's past confirmation history.

[0109] The confirmation unit can monitor the resident's current situation in real time during confirmation and select the optimal confirmation timing. For example, the confirmation unit can monitor the resident's current situation in real time during confirmation and select the optimal confirmation timing. The current situation is monitored using real-time data and situation judgment criteria. The confirmation unit selects the optimal confirmation timing based on the current situation. For example, the confirmation unit delays confirmation if the resident is busy. Also, the confirmation unit can check immediately if the resident is relaxed. Furthermore, the confirmation unit can select the optimal confirmation timing based on the resident's current situation. As a result, the confirmation unit can select the optimal confirmation timing by monitoring the resident's current situation in real time.

[0110] The confirmation unit can estimate the user's emotions and determine the priority of confirmation based on the estimated user emotions. The confirmation unit, for example, estimates the user's emotions and determines the priority of confirmation based on the estimated user emotions. The user's emotions are analyzed using an emotion analysis algorithm. For example, the confirmation unit can analyze the user's facial expressions and voice and calculate an emotion score. The confirmation unit determines the priority of confirmation based on the emotion score. For example, if the user is in a hurry, the confirmation can be performed quickly. Also, if the user is relaxed, the confirmation can be performed carefully. Furthermore, if the user is feeling stressed, a confirmation method that reduces stress can be selected. In this way, the confirmation unit can determine the priority of confirmation according to the user's emotions, thereby enabling more appropriate confirmation.

[0111] The confirmation unit can select the optimal confirmation method by taking into consideration the resident's device information when making a confirmation. For example, the confirmation unit selects the optimal confirmation method by taking into consideration the resident's device information when making a confirmation. The device information is acquired based on the type of device and its usage status. The confirmation unit selects the optimal confirmation method based on the device information. For example, if the resident is using a smartphone, the confirmation unit uses push notification. Also, if the resident is using a tablet, the confirmation unit can perform confirmation optimized for a large screen. Furthermore, if the resident is using a smartwatch, the confirmation unit can also perform a simple and highly visible confirmation. This allows the confirmation unit to select the optimal confirmation method by taking into consideration the resident's device information.

[0112] The confirmation unit can select the optimal confirmation timing when making a confirmation by taking into consideration the resident's schedule information. For example, the confirmation unit selects the optimal confirmation timing when making a confirmation by taking into consideration the resident's schedule information. The schedule information is acquired using a calendar app or schedule sharing. The confirmation unit selects the optimal confirmation timing based on the schedule information. For example, the confirmation unit performs confirmation during a time period when the resident is at home based on the resident's schedule information. Furthermore, the confirmation unit can perform confirmation while avoiding a time period when the resident is absent based on the resident's schedule information. Furthermore, the confirmation unit can also select the optimal confirmation timing based on the resident's schedule information. In this way, the confirmation unit can select the optimal confirmation timing by taking into consideration the resident's schedule information.

[0113] The charging unit can estimate the user's emotions and adjust the charging timing based on the estimated user emotions. For example, the charging unit estimates the user's emotions and adjusts the charging timing based on the estimated user emotions. The user's emotions are analyzed using an emotion analysis algorithm. For example, the charging unit can analyze the user's facial expressions and voice and calculate an emotion score. The charging unit adjusts the charging timing based on the emotion score. For example, if the user is in a hurry, the charging unit can charge quickly. Also, if the user is relaxed, the charging unit can charge carefully. Furthermore, if the user is feeling stressed, the charging unit can select a charging method that reduces stress. In this way, the charging unit can adjust the charging timing according to the user's emotions, enabling more appropriate charging.

[0114] The charging unit monitors the battery status of the delivery robot in real time during charging and can select the optimal charging timing. For example, the charging unit monitors the battery status of the delivery robot in real time during charging and can select the optimal charging timing. The battery status is monitored based on the remaining battery level and charging cycle. The charging unit selects the optimal charging timing based on the battery status. For example, the charging unit immediately charges the delivery robot when its battery is low. The charging unit can also delay charging if the delivery robot's battery has sufficient power. Furthermore, the charging unit can select the optimal charging timing based on the delivery robot's battery status. As a result, the charging unit can select the optimal charging timing by monitoring the delivery robot's battery status in real time.

[0115] The charging unit can estimate the user's emotions and determine charging priorities based on the estimated user emotions. The charging unit, for example, estimates the user's emotions and determines charging priorities based on the estimated user emotions. The user's emotions are analyzed using an emotion analysis algorithm. For example, the charging unit can analyze the user's facial expressions and voice and calculate an emotion score. The charging unit determines charging priorities based on the emotion score. For example, if the user is in a hurry, charging can be performed quickly. Also, if the user is relaxed, charging can be performed carefully. Furthermore, if the user is feeling stressed, a charging method that reduces stress can be selected. In this way, the charging unit can determine charging priorities according to the user's emotions, enabling more appropriate charging.

[0116] The charging unit can select the optimal charging timing when charging, taking into account the power usage status within the apartment building. For example, the charging unit selects the optimal charging timing when charging, taking into account the power usage status within the apartment building. The power usage status is obtained using real-time data and past data. The charging unit selects the optimal charging timing based on the power usage status. For example, the charging unit charges during a time period when power usage within the apartment building is low. The charging unit can also charge by avoiding a time period when power usage within the apartment building is high. Furthermore, the charging unit can also select the optimal charging timing based on the power usage status within the apartment building. In this way, the charging unit can select the optimal charging timing by taking into account the power usage status within the apartment building. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, planning unit, retrieval unit, delivery unit, notification unit, confirmation unit, and charging unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects order information from residents. The planning unit is realized by the specific processing unit 290 of the data processing device 12 and creates a delivery plan based on the collected order information. The retrieval unit is realized by the control unit 46A of the smart device 14 and retrieves meals from the refrigerated locker. The delivery unit is realized by the control unit 46A of the smart device 14 and is equipped with an obstacle detection system using LIDAR and a camera to move safely. The notification unit and confirmation unit are realized by the control unit 46A of the smart device 14 and notify residents and confirm receipt of the meal. The charging unit is realized by the control unit 46A of the smart device 14 and charges the battery using an automatic charging system after the delivery robot returns to the refrigerated locker. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, planning unit, retrieval unit, delivery unit, notification unit, confirmation unit, and charging unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects order information from residents. The planning unit is realized by the specific processing unit 290 of the data processing device 12 and creates a delivery plan based on the collected order information. The retrieval unit is realized by the control unit 46A of the smart glasses 214 and retrieves meals from the refrigerated locker. The delivery unit is realized by the control unit 46A of the smart glasses 214 and is equipped with an obstacle detection system using LIDAR and a camera to ensure safe movement. The notification unit and confirmation unit are realized by the control unit 46A of the smart glasses 214 and notify residents and confirm receipt of the meal. The charging unit is realized by the control unit 46A of the smart glasses 214 and charges the battery using an automatic charging system after the delivery robot returns to the refrigerated locker. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, planning unit, retrieval unit, delivery unit, notification unit, confirmation unit, and charging unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset terminal 314 and collects resident order information. The planning unit is realized by the specific processing unit 290 of the data processing device 12 and creates a delivery plan based on the collected order information. The retrieval unit is realized by the control unit 46A of the headset terminal 314 and retrieves meals from the refrigerated locker. The delivery unit is realized by the control unit 46A of the headset terminal 314 and is equipped with an obstacle detection system using LIDAR and a camera to move safely. The notification unit and confirmation unit are realized by the control unit 46A of the headset terminal 314 and notify residents and confirm receipt of the meals. The charging unit is realized by the control unit 46A of the headset terminal 314 and charges the battery using an automatic charging system after the delivery robot returns to the refrigerated locker. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, planning unit, retrieval unit, delivery unit, notification unit, confirmation unit, and charging unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects order information from residents. The planning unit is realized by the specific processing unit 290 of the data processing device 12 and creates a delivery plan based on the collected order information. The retrieval unit is realized by the control unit 46A of the robot 414 and retrieves meals from the refrigerated locker. The delivery unit is realized by the control unit 46A of the robot 414 and is equipped with an obstacle detection system using LIDAR and a camera to move safely. The notification unit and confirmation unit are realized by the control unit 46A of the robot 414 and notify residents and confirm receipt of the meals. The charging unit is realized by the control unit 46A of the robot 414 and charges the battery using an automatic charging system after the delivery robot returns to the refrigerated locker.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The delivery system may further include a voice recognition unit. The voice recognition unit can analyze residents' voice commands and collect order information and adjust delivery plans. For example, if a resident verbally instructs, "Cancel tomorrow's breakfast," the voice recognition unit analyzes the instruction and sends cancellation information to the collection unit. Also, if a resident verbally instructs, "Change the delivery time," the voice recognition unit can analyze the instruction and instruct the planning unit to set a new delivery time. Furthermore, the voice recognition unit can adjust the operation of the delivery robot in real time based on the resident's voice commands. This allows residents to operate the delivery system more intuitively using voice commands.

[0119] The delivery unit is further equipped with a temperature sensor, which can monitor the temperature of meals in real time during delivery. The temperature sensor monitors meals to ensure they are delivered at the appropriate temperature and can adjust the temperature as necessary. For example, the delivery unit can use the temperature sensor to activate a keep-warm function if the temperature of the meal drops. Also, if the meal is overheated, the delivery unit can activate a cooling function. Furthermore, the delivery unit can select the optimal delivery route to maintain the quality of the meal based on the data from the temperature sensor. This allows the delivery unit to properly manage the temperature of the meal and deliver it while maintaining quality.

[0120] The planning unit can further take traffic information into consideration when creating a delivery plan. Traffic information is obtained using real-time data and past data. The planning unit selects the optimal delivery route based on the traffic information. For example, the planning unit monitors traffic congestion information around an apartment building in real time and selects a route that avoids congestion. The planning unit can also select the optimal route by taking into consideration traffic accidents and construction information. Furthermore, the planning unit can adjust delivery times based on traffic information. In this way, the planning unit can create an efficient delivery plan by taking traffic information into consideration.

[0121] The dispenser can further include a function for measuring the weight of the meal. A weight sensor is used to measure the weight of the meal when it is dispensed, and an appropriate dispenser method is selected. For example, the dispenser can use the weight sensor to select a careful dispenser method if the meal is heavy, or a quick dispenser method if the meal is light. Furthermore, the dispenser can also optimize the dispenser order for the meals based on the data from the weight sensor. This allows the dispenser to select an appropriate dispenser method by taking the weight of the meals into consideration.

[0122] The notification unit can also work with residents' calendar apps to automatically add delivery schedules to their calendars. For example, when a resident orders a delivery, the notification unit sends that information to the calendar app, which automatically adds the delivery schedule. If the delivery schedule is changed, the notification unit can send the change information to the calendar app and update the calendar. Furthermore, the notification unit can obtain schedule information from the resident's calendar app and adjust the delivery time. This allows the notification unit to make deliveries that fit the resident's schedule.

[0123] The confirmation unit can further use a two-dimensional code (e.g., a QR code) to confirm receipt of the meal. For example, when the delivery robot arrives at the resident's front door, the two-dimensional code is displayed to the resident. The resident can scan the two-dimensional code using a smartphone camera to confirm receipt. The confirmation unit can also use the two-dimensional code to confirm the quality of the meal. For example, after the resident receives the meal, the resident can scan the two-dimensional code to send feedback on the quality of the meal. The confirmation unit can also use the two-dimensional code to provide meal tracking information. Thus, the confirmation unit can easily and reliably confirm receipt by using the two-dimensional code.

[0124] The collection unit can analyze the user's emotions and adjust the timing of collecting order information based on the analyzed user's emotions. For example, if the user is feeling stressed, the order information can be collected during a time when the user is able to relax. If the user is busy, the order information can be collected in a short time. Furthermore, if the user is relaxed, detailed order information can also be collected. In this way, the collection unit can adjust the timing of collecting order information according to the user's emotions, thereby collecting order information at more appropriate times.

[0125] The planning unit can estimate the user's emotions and adjust the delivery plan creation method based on the estimated user's emotions. For example, if the user is in a hurry, the delivery plan can be created with priority given to the shortest route. Also, if the user is relaxed, the delivery plan can be created with priority given to a scenic route. Furthermore, if the user is feeling stressed, the delivery plan can be created with priority given to a route that will reduce stress. In this way, the planning unit can create a more appropriate delivery plan by adjusting the delivery plan creation method according to the user's emotions.

[0126] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated user's emotions. For example, if the user is in a hurry, a method that allows for quick delivery can be selected. Also, if the user is relaxed, a method that allows for careful delivery can be selected. Furthermore, if the user is feeling stressed, a delivery method that reduces stress can be selected. This allows the delivery unit to adjust the delivery method according to the user's emotions, enabling more appropriate delivery.

[0127] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user's emotions. For example, if the user is nervous, the notification can be made in a calm tone. If the user is relaxed, the notification can be made in a bright tone. Furthermore, if the user is in a hurry, the notification can be made quickly and concisely. This allows the notification unit to adjust the notification method according to the user's emotions, thereby enabling more appropriate notifications.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The collection unit collects order information. The order information includes, but is not limited to, the type of meal, the quantity, and the delivery time. The collection unit can, for example, obtain the information from a database or collect user input. Step 2: The planning unit creates a delivery plan based on the order information collected by the collection unit. The delivery plan may include, but is not limited to, route optimization and delivery time setting. The planning unit uses map data within the apartment building to calculate the optimal route to the front door of each apartment. Step 3: The retrieval unit retrieves the meal from the refrigerated locker. The retrieval unit can, for example, use a robotic arm to retrieve the meal from the refrigerated locker. Step 4: The delivery department delivers to each apartment based on the delivery plan created by the planning department. The delivery department is equipped with an obstacle detection system using, for example, LIDAR or cameras, to move safely. Step 5: The notification unit notifies the resident when the delivery robot arrives at the resident's doorstep. The notification unit can notify the resident, for example, through a mobile application. Step 6: The confirmation unit confirms receipt of the meal. The confirmation unit can, for example, confirm receipt of the meal through a mobile application. Step 7: The charging unit uses the automatic charging system. For example, the charging unit can use the automatic charging system to charge the battery after the delivery robot returns to the refrigerated locker.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0132] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0160] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0170] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0172] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0173] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0174] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0185] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0186] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0187] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0191] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0192] 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.

[0193] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0194] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0195] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0196] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0198] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0199] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0200] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0201] [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects order information; a planning unit that makes a delivery plan based on the order information collected by the collecting unit; A pickup area where meals are taken out of the refrigerated locker; a delivery unit that delivers to each dwelling unit based on the delivery plan established by the planning unit; a notification unit that notifies residents when the delivery robot arrives at their front door; a confirmation unit that confirms receipt of the meal; a charging unit that uses an automatic charging system; A system characterized by:

2. The delivery unit Equipped with an obstacle detection system using LIDAR or a camera 2. The system of claim 1.

3. The planning unit Calculates efficient routes to the front door of each apartment using map data within the apartment building 2. The system of claim 1.

4. The take-out section is Use a robotic arm to retrieve meals from a refrigerated locker 2. The system of claim 1.

5. The notification unit Notifying residents through a mobile application 2. The system of claim 1.

6. The confirmation unit Confirm meal pickup through a mobile application 2. The system of claim 1.

7. The collecting unit Analyze user emotions and adjust the timing of collecting order information based on the analyzed user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past order history and select the optimal collection method 2. The system of claim 1.

9. The collecting unit Filtering order information based on the user's current dietary preferences and allergies when collecting it 2. The system of claim 1.

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