System

Abstract

To provide a system according to an embodiment that moves to an optimal place to receive luggage.SOLUTION: A system according to an embodiment comprises: a collection unit; an analysis unit; a movement unit; and a reception unit. The collection unit is configured to collect data. The analysis unit is configured to analyze the data collected by the collection unit. The movement unit is configured to move to an optimal place on the basis of an analysis result obtained by the analysis unit. The reception unit is the reception unit for receiving luggage.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there were limited locations where parcel lockers could be installed, making it difficult for elderly people and others to collect their parcels.

[0005] The system according to the embodiment aims to move to an optimal location to pick up the package. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a movement unit, and a receiving unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The movement unit moves to an optimal location based on the analysis results obtained by the analysis unit. The receiving unit is a unit for receiving packages. Effect of the Invention

[0007] The system according to the embodiment can move to the optimal location to pick up the package. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The parcel locker system according to the embodiment of the present invention is a system that realizes automatic driving by mounting a parcel locker on a box-shaped car, and moves / places it in an optimal location to collect / deliver. This system aims to solve the problem that it is difficult for elderly people and people with physical disabilities to go to the parcel locker, and the problem of a shortage of parcel workers. First, a parcel locker is mounted on a box-shaped car. This car is operated using automatic driving technology. The automatic driving technology uses AI, and moves safely while recognizing the surrounding environment. For example, a camera or a sensor is used to detect road conditions and obstacles, and an appropriate route is selected. Next, this automatic driving vehicle moves to the optimal location based on people flow data and home delivery reservation / performance data. The people flow data is analyzed by AI to understand which areas have many people gathering. For example, it identifies places where many people gather, such as commercial facilities and residential areas. In addition, the home delivery reservation / performance data is also analyzed by AI to understand which areas have many home deliveries. This makes it possible to place automatic driving vehicles in areas with high demand. When the automatic driving vehicle arrives at the optimal location, the parcel locker is opened, and the user can receive the package. For example, elderly people and people with disabilities can access an autonomous vehicle near their home to receive their parcels. In addition, the delivery company can alleviate the labor shortage problem because they only need to leave their parcels with the autonomous vehicle. In addition, this system allows users to check the location of the parcel locker using a smartphone. For example, a dedicated app can be used to check the current location and estimated arrival time of the autonomous vehicle. This allows users to receive their parcels efficiently. In this way, the parcel locker system is a groundbreaking system that solves the problem of elderly people and people with disabilities finding it difficult to get to the parcel locker and the problem of a shortage of delivery personnel. By utilizing autonomous driving technology and AI, it is possible to move / place parcels in the optimal location and efficiently collect / deliver them. As a result, the parcel locker system allows elderly people and people with disabilities to receive parcels near their homes, and alleviates the problem of a shortage of delivery personnel.

[0029] The parcel locker system according to the embodiment includes a collection unit, an analysis unit, a movement unit, and a receiving unit. The collection unit collects data. The collection unit can collect, for example, people flow data and home delivery reservation / performance data. The collection unit can collect people flow data using, for example, GPS data. The collection unit can also collect home delivery reservation / performance data using online order data. For example, the collection unit can collect people flow data using Wi-Fi connection data. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, AI, and determine which area many people are gathering in. The analysis unit can identify places where many people gather, such as commercial facilities and residential areas, using AI. The analysis unit can also analyze the home delivery reservation / performance data using AI and determine which area many home deliveries are being made in. For example, the analysis unit can identify areas with high demand using AI. The movement unit moves to an optimal location based on the analysis results obtained by the analysis unit. The movement unit can select an optimal route using AI, and move to an area with high demand. The moving unit can detect road conditions and obstacles using, for example, a camera or a sensor, and move safely. The moving unit can also use AI to select an appropriate route and move efficiently. For example, the moving unit can move to areas with high demand, such as commercial facilities and residential areas, using AI. When the autonomous vehicle arrives at the optimal location, the receiving unit opens the parcel locker and allows the user to receive the parcel. The receiving unit can allow, for example, elderly people and people with disabilities to access an autonomous vehicle near their home and receive the parcel. The receiving unit can also allow the user to check the location of the parcel locker using a smartphone. For example, the receiving unit can use a dedicated app to check the current location and estimated arrival time of the autonomous vehicle. As a result, the parcel locker system according to the embodiment can efficiently collect, analyze, move, and receive data.

[0030] The collection unit collects data. The collection unit can collect, for example, people flow data and home delivery reservation / performance data. Specifically, the collection unit collects people flow data using GPS data. The GPS data is obtained from a GPS device mounted on a smartphone or a vehicle, and is used to grasp people's movement patterns and stay times in a specific area. In addition, the collection unit collects home delivery reservation / performance data using online order data. The online order data is obtained from an e-commerce platform or a home delivery company's system, and is used to grasp what products are ordered in which area and the delivery status. In addition, the collection unit can also collect people flow data using Wi-Fi connection data. The Wi-Fi connection data is obtained from public Wi-Fi access points or Wi-Fi networks in commercial facilities, and is used to grasp people's stay status and movement patterns in a specific area. This allows the collection unit to collect a wide range of data from various data sources and grasp the situation in real time. In addition, the collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and the movement unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data and determine which areas many people gather in. Specifically, the AI ​​uses a machine learning algorithm to analyze GPS data and Wi-Fi connection data to identify people's movement patterns and stay times. This makes it possible to identify places where many people gather, such as commercial facilities and residential areas. The analysis unit also uses AI to analyze delivery reservation / performance data and determine which areas are performing a lot of deliveries. For example, the AI ​​can analyze past order data and predict trends in delivery demand in specific areas and time periods. Furthermore, the analysis unit can also use AI to identify areas with high demand. This allows the analysis unit to quickly and accurately analyze the collected data and identify areas and time periods with high demand in real time. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term demand forecasts and trend analysis. For example, based on past delivery data, it predicts fluctuations in demand during specific seasons and events and plans future measures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term demand management and anomaly detection, improving the reliability and efficiency of the entire system.

[0032] The moving unit moves to an optimal location based on the analysis results obtained by the analysis unit. The moving unit can, for example, use AI to select an optimal route and move to areas with high demand. Specifically, AI analyzes real-time traffic data and road condition data and calculates an optimal moving route. This allows the moving unit to move efficiently while avoiding traffic jams and obstacles. The moving unit can also detect road conditions and obstacles using cameras and sensors and move safely. For example, the camera monitors road conditions in real time, and the sensor detects obstacles and pedestrians. This allows the moving unit to reach the destination safely and efficiently. Furthermore, the moving unit can also use AI to select an appropriate route and move efficiently. For example, AI can analyze past movement data and demand data and plan an optimal movement schedule. This allows the moving unit to move quickly to areas with high demand and provide services efficiently. Furthermore, the moving unit can continuously optimize the movement route and schedule based on data updated in real time. This allows the moving unit to always realize optimal movement based on the latest information, improving the efficiency and reliability of the entire system.

[0033] When the autonomous vehicle arrives at the optimal location, the receiving unit opens the parcel locker, allowing the user to receive the parcel. Specifically, the receiving unit is designed to allow elderly people and people with disabilities to access an autonomous vehicle near their home and receive the parcel. For example, the receiving unit is equipped with an alert system for notifying the user of the arrival of the vehicle, and the user can receive the notification through a smartphone or a dedicated app. The receiving unit also allows the user to check the location of the parcel locker using a smartphone. The user can use a dedicated app to check the current location and estimated arrival time of the autonomous vehicle, so that the user can receive the parcel according to his or her schedule. Furthermore, the receiving unit uses a two-dimensional code (e.g., QR code (registered trademark)) or NFC technology to allow the user to easily open the locker. This allows the user to open the locker just by holding up their smartphone, allowing the user to smoothly receive the parcel. In addition, the receiving unit can collect user feedback and use it to improve the service. For example, the user can report problems and requests regarding reception through the app, and the system is improved based on that information. This allows the receiving unit to provide the user with a means to receive the parcel quickly and reliably, thereby improving the convenience and reliability of the entire system.

[0034] The collection unit can collect people flow data or home delivery reservation / performance data. The collection unit can collect people flow data using, for example, GPS data. For example, the collection unit can also collect people flow data using Wi-Fi connection data. The collection unit can also collect home delivery reservation / performance data using online order data. For example, the collection unit can also collect home delivery reservation / performance data using delivery history data of a home delivery company. This allows moving to an optimal location by collecting people flow data or home delivery reservation / performance data. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data to AI and cause AI to collect people flow data.

[0035] The analysis unit can analyze the data collected by the collection unit and grasp which area a large number of people are gathering. The analysis unit can analyze the collected data using, for example, AI and grasp which area a large number of people are gathering. For example, the analysis unit can use AI to identify places where many people gather, such as commercial facilities and residential areas. The analysis unit can also analyze delivery reservation / performance data using AI and grasp which area a large number of deliveries are being made in. For example, the analysis unit can also use AI to identify areas with high demand. This allows the delivery to be moved to the optimal location by analyzing the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and have AI perform the analysis.

[0036] The moving unit can move to areas with high demand based on the analysis results obtained by the analysis unit. The moving unit can, for example, use AI to select an optimal route and move to areas with high demand. For example, the moving unit can detect road conditions and obstacles using a camera or a sensor and move safely. The moving unit can also select an appropriate route using AI and move efficiently. For example, the moving unit can move to areas with high demand, such as commercial facilities and residential areas, using AI. As a result, by moving to areas with high demand, packages can be efficiently received. A part or all of the above-mentioned processing in the moving unit may be performed using AI, for example, or may be performed without using AI. For example, the moving unit can input the analysis results to AI and cause AI to select a moving route.

[0037] When the autonomous vehicle arrives at an appropriate location, the receiving unit opens the parcel locker, and the user can receive the parcel. The receiving unit allows, for example, elderly people and people with disabilities to access an autonomous vehicle near their home and receive the parcel. The receiving unit also allows the user to check the location of the parcel locker using a smartphone. For example, the receiving unit can use a dedicated app to check the current location and estimated arrival time of the autonomous vehicle. This allows the user to receive the parcel. Some or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the receiving unit can input location information of the smartphone into AI and have the AI ​​execute location confirmation of the parcel locker.

[0038] The receiving unit allows a user to confirm the location of the parcel locker using a mobile terminal. The receiving unit can confirm the location of the parcel locker using, for example, a smartphone. For example, the receiving unit can confirm the current location and estimated arrival time of the autonomous vehicle using a dedicated app. The receiving unit can also confirm the location of the parcel locker using a tablet. For example, the receiving unit can confirm the location of the autonomous vehicle using a GPS function of the tablet. This allows a user to confirm the location of the parcel locker using a smartphone. A part or all of the above-mentioned processing in the receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the receiving unit can input the location information of the smartphone into the AI ​​and cause the AI ​​to execute the location confirmation of the parcel locker.

[0039] The collection unit can analyze the past data collection history and select the optimal collection method. For example, the collection unit can improve efficiency by collecting data at a specific time period from the past data collection history. The collection unit can also prioritize data collection at a specific location based on the past data collection history. Furthermore, the collection unit can analyze the past data collection history and select the most effective collection method. In this way, the optimal collection method can be selected by analyzing the past data collection history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into AI and cause AI to select the optimal collection method.

[0040] The collection unit can filter the collection target based on a specific event or season when collecting data. For example, the collection unit can prioritize data collection in an area where a specific event is held. The collection unit can also filter the collection target by taking into account seasonal human flow data. Furthermore, the collection unit can dynamically change the collection target based on a specific event or season. This allows efficient data collection by filtering the collection target based on a specific event or season. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data related to a specific event or season to AI and have AI perform filtering of the collection target.

[0041] The collection unit can prioritize collection of highly relevant data based on the geographical location information when collecting data. The collection unit can prioritize collection of data in a specific area based on the geographical location information, for example. The collection unit can also collect highly relevant data taking into account the geographical location information. Furthermore, the collection unit can determine the priority order of data collection based on the geographical location information. As a result, data can be collected efficiently by preferentially collecting highly relevant data taking into account the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the geographical location information to an AI and cause the AI ​​to collect highly relevant data.

[0042] The collection unit can analyze social media activities and collect related data when collecting data. The collection unit can, for example, analyze social media activities and collect data related to a specific topic. The collection unit can also prioritize data collection in a specific area based on social media activities. Furthermore, the collection unit can analyze social media activities and collect highly relevant data. This allows data to be collected efficiently by analyzing social media activities and collecting related data. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without using AI. For example, the collection unit can input social media posting data into AI and cause AI to collect related data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit can perform a detailed analysis on data with high importance, for example. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis based on the importance of the data. This allows the analysis to be performed efficiently by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the data to an AI and cause the AI ​​to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit can select an optimal analysis algorithm depending on the category of data, for example. The analysis unit can also apply different analysis algorithms for each category of data. Furthermore, the analysis unit can dynamically change the analysis algorithm based on the category of data. This allows efficient analysis by applying different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to AI and cause the AI ​​to select the optimal analysis algorithm.

[0045] The analysis unit can determine the analysis priority based on the collection time of the data during the analysis. The analysis unit can, for example, preferentially analyze the latest data. The analysis unit can also determine the analysis priority based on past data. Furthermore, the analysis unit can adjust the order of analysis based on the collection time of the data. This allows efficient analysis by determining the analysis priority based on the collection time of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the data to AI and cause the AI ​​to determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit can, for example, prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of data. This allows analysis to be performed efficiently by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to AI and cause AI to adjust the order of analysis.

[0047] The movement unit can improve the accuracy of movement by considering the interrelationships of data during movement. The movement unit can select an optimal movement route based on, for example, the interrelationships of data. The movement unit can also improve the accuracy of movement by considering the interrelationships of data. Furthermore, the movement unit can adjust the order of movement based on the interrelationships of data. As a result, the movement can be performed efficiently by improving the accuracy of movement by considering the interrelationships of data. A part or all of the above-mentioned processing in the movement unit may be performed using, for example, AI, or may be performed without using AI. For example, the movement unit can input the interrelationships of data into AI and cause AI to execute the improvement of the accuracy of movement.

[0048] The movement unit can move while taking into account the attribute information of the region. The movement unit can select an optimal movement route based on, for example, the attribute information of the region. The movement unit can also improve the accuracy of the movement by taking into account the attribute information of the region. Furthermore, the movement unit can adjust the order of movement based on the attribute information of the region. As a result, the movement can be performed efficiently by taking into account the attribute information of the region. A part or all of the above-mentioned processing in the movement unit can be performed, for example, using AI or can be performed without using AI. For example, the movement unit can input the attribute information of the region into the AI ​​and cause the AI ​​to select a movement route.

[0049] The movement unit can move while taking the geographical distribution into consideration. The movement unit can select an optimal movement route based on the geographical distribution, for example. The movement unit can also improve the accuracy of the movement by taking the geographical distribution into consideration. Furthermore, the movement unit can also adjust the order of movement based on the geographical distribution. As a result, the movement can be performed efficiently by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the movement unit can be performed, for example, using AI, or can be performed without using AI. For example, the movement unit can input geographical distribution data to AI and cause AI to select a movement route.

[0050] The movement unit can improve the accuracy of the movement based on the related literature during the movement. The movement unit can select an optimal movement route based on the related literature, for example. The movement unit can also improve the accuracy of the movement by referring to the related literature. Furthermore, the movement unit can also adjust the order of the movement based on the related literature. Thereby, the movement can be efficiently performed by improving the accuracy of the movement by referring to the related literature. A part or all of the above-mentioned processing in the movement unit may be performed using, for example, AI, or may be performed without using AI. For example, the movement unit can input the related literature data into the AI ​​and cause the AI ​​to execute the improvement of the accuracy of the movement.

[0051] The receiving unit can select the optimal receiving method by analyzing the past receiving history at the time of receiving. The receiving unit can, for example, suggest the optimal receiving method based on the past receiving history. The receiving unit can also analyze the past receiving history and select a receiving method that suits the user. Furthermore, the receiving unit can also customize the receiving method by referring to the past receiving history. In this way, the optimal receiving method can be selected by analyzing the past receiving history. A part or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the receiving unit can input the past receiving history into AI and have the AI ​​select the optimal receiving method.

[0052] The receiving unit can customize the receiving means based on the user's current living situation at the time of receiving. For example, if the user is an elderly person, the receiving unit can provide a means for receiving with a simple operation. In addition, if the user is physically disabled, the receiving unit can provide voice guidance and support functions. Furthermore, if the user is busy, the receiving unit can provide a means for quickly receiving. This allows efficient receiving by customizing the receiving means based on the user's current living situation. Part or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the receiving unit can input the user's living situation data into AI and have the AI ​​execute the customization of the receiving means.

[0053] The receiving unit can select the optimal receiving method in consideration of the geographical location information at the time of receiving. The receiving unit can, for example, suggest the optimal receiving location based on the geographical location information. The receiving unit can also customize the receiving method in consideration of the geographical location information. Furthermore, the receiving unit can also determine the priority of receiving based on the geographical location information. As a result, by selecting the optimal receiving method in consideration of the geographical location information, receiving can be performed efficiently. A part or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input the geographical location information into AI and cause the AI ​​to select the optimal receiving method.

[0054] The receiving unit can analyze social media activity and suggest a receiving means at the time of receiving. The receiving unit can, for example, analyze social media activity and suggest a receiving means related to a specific topic. The receiving unit can also suggest a receiving means in a specific area based on the social media activity. Furthermore, the receiving unit can analyze social media activity and suggest a receiving means with high relevance. As a result, by analyzing social media activity and suggesting a receiving means, receiving can be performed efficiently. A part or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input social media posting data into AI and have the AI ​​execute the suggestion of a receiving means.

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

[0056] The parcel locker system further includes an energy management unit. The energy management unit can supply energy to the autonomous vehicle using solar panels or wind power generation equipment. For example, the energy management unit can install solar panels on the roof of the vehicle to store energy during the day. Also, a wind power generation device can be attached to the side of the vehicle to generate energy using wind power while driving. Furthermore, the energy management unit can monitor energy usage in real time and perform efficient energy management. This enables energy self-sufficiency and realizes an environmentally friendly system.

[0057] The collection unit can further collect health data of the user. For example, the collection unit can collect data such as heart rate and number of steps from a smart watch or a fitness tracker. The collection unit can also suggest an optimal delivery time based on the user's health condition. Furthermore, the collection unit can analyze the user's health data and provide a service according to the health condition. This makes it possible to provide a service that takes the user's health condition into consideration.

[0058] The analysis unit can further analyze weather data. For example, the analysis unit can collect weather data and reflect it in the selection of delivery routes. The analysis unit can also dynamically adjust delivery schedules in response to changes in the weather. Furthermore, the analysis unit can analyze seasonal delivery patterns based on past weather data and plan optimal delivery strategies. This enables efficient delivery with minimal impact from weather.

[0059] The travel department can further select travel routes taking traffic congestion information into consideration. For example, the travel department can collect real-time traffic information and select routes that avoid congestion. The travel department can also analyze past traffic data and adjust travel schedules to avoid times when congestion is likely to occur. Furthermore, the travel department can determine the optimal delivery order based on traffic congestion information. This enables efficient delivery with minimal delays due to traffic congestion.

[0060] The receiving unit can further customize the receiving method based on the user's preferences. For example, the receiving unit can prioritize the receiving location and time period specified by the user. The receiving unit can also suggest the optimal receiving method based on the user's past receiving history. Furthermore, the receiving unit can collect user feedback and reflect it in improving the receiving method. This makes it possible to provide a flexible receiving method that meets the needs of the user.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects data. The collection unit can collect, for example, people flow data and home delivery reservation / performance data. The collection unit can collect people flow data using, for example, GPS data. The collection unit can also collect home delivery reservation / performance data using online order data. For example, the collection unit can collect people flow data using Wi-Fi connection data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected data and determine which areas many people gather in. The analysis unit can, for example, use AI to identify places where many people gather, such as commercial facilities and residential areas. The analysis unit can also use AI to analyze delivery reservation / performance data and determine which areas are seeing a lot of deliveries. For example, the analysis unit can use AI to identify areas with high demand. Step 3: The moving unit moves to the optimal location based on the analysis results obtained by the analysis unit. The moving unit can, for example, use AI to select the optimal route and move to areas with high demand. The moving unit can, for example, use cameras and sensors to detect road conditions and obstacles and move safely. The moving unit can also use AI to select an appropriate route and move efficiently. For example, the moving unit can use AI to move to areas with high demand, such as commercial facilities and residential areas. Step 4: When the autonomous vehicle arrives at the optimal location, the receiving unit opens the parcel locker, allowing the user to receive the parcel. The receiving unit allows, for example, elderly people or people with disabilities to access an autonomous vehicle near their home and receive their parcel. The receiving unit also allows the user to check the location of the parcel locker using a smartphone. For example, the receiving unit can use a dedicated app to check the current location and estimated arrival time of the autonomous vehicle.

[0063] (Example 2) The parcel locker system according to the embodiment of the present invention is a system that realizes automatic driving by mounting a parcel locker on a box-shaped car, and moves / places it in an optimal location to collect / deliver. This system aims to solve the problem that it is difficult for elderly people and people with physical disabilities to go to the parcel locker, and the problem of a shortage of parcel workers. First, a parcel locker is mounted on a box-shaped car. This car is operated using automatic driving technology. The automatic driving technology uses AI, and moves safely while recognizing the surrounding environment. For example, a camera or a sensor is used to detect road conditions and obstacles, and an appropriate route is selected. Next, this automatic driving vehicle moves to the optimal location based on people flow data and home delivery reservation / performance data. The people flow data is analyzed by AI to understand which areas have many people gathering. For example, it identifies places where many people gather, such as commercial facilities and residential areas. In addition, the home delivery reservation / performance data is also analyzed by AI to understand which areas have many home deliveries. This makes it possible to place automatic driving vehicles in areas with high demand. When the automatic driving vehicle arrives at the optimal location, the parcel locker is opened, and the user can receive the package. For example, elderly people and people with disabilities can access an autonomous vehicle near their home to receive their parcels. In addition, the delivery company can alleviate the labor shortage problem because they only need to leave their parcels with the autonomous vehicle. In addition, this system allows users to check the location of the parcel locker using a smartphone. For example, a dedicated app can be used to check the current location and estimated arrival time of the autonomous vehicle. This allows users to receive their parcels efficiently. In this way, the parcel locker system is a groundbreaking system that solves the problem of elderly people and people with disabilities finding it difficult to get to the parcel locker and the problem of a shortage of delivery personnel. By utilizing autonomous driving technology and AI, it is possible to move / place parcels in the optimal location and efficiently collect / deliver them. As a result, the parcel locker system allows elderly people and people with disabilities to receive parcels near their homes, and alleviates the problem of a shortage of delivery personnel.

[0064] The parcel locker system according to the embodiment includes a collection unit, an analysis unit, a movement unit, and a receiving unit. The collection unit collects data. The collection unit can collect, for example, people flow data and home delivery reservation / performance data. The collection unit can collect people flow data using, for example, GPS data. The collection unit can also collect home delivery reservation / performance data using online order data. For example, the collection unit can collect people flow data using Wi-Fi connection data. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, AI, and determine which area many people are gathering in. The analysis unit can identify places where many people gather, such as commercial facilities and residential areas, using AI. The analysis unit can also analyze the home delivery reservation / performance data using AI and determine which area many home deliveries are being made in. For example, the analysis unit can identify areas with high demand using AI. The movement unit moves to an optimal location based on the analysis results obtained by the analysis unit. The movement unit can select an optimal route using AI, and move to an area with high demand. The moving unit can detect road conditions and obstacles using, for example, a camera or a sensor, and move safely. The moving unit can also use AI to select an appropriate route and move efficiently. For example, the moving unit can move to areas with high demand, such as commercial facilities and residential areas, using AI. When the autonomous vehicle arrives at the optimal location, the receiving unit opens the parcel locker and allows the user to receive the parcel. The receiving unit can allow, for example, elderly people and people with disabilities to access an autonomous vehicle near their home and receive the parcel. The receiving unit can also allow the user to check the location of the parcel locker using a smartphone. For example, the receiving unit can use a dedicated app to check the current location and estimated arrival time of the autonomous vehicle. As a result, the parcel locker system according to the embodiment can efficiently collect, analyze, move, and receive data.

[0065] The collection unit collects data. The collection unit can collect, for example, people flow data and home delivery reservation / performance data. Specifically, the collection unit collects people flow data using GPS data. The GPS data is obtained from a GPS device mounted on a smartphone or a vehicle, and is used to grasp people's movement patterns and stay times in a specific area. In addition, the collection unit collects home delivery reservation / performance data using online order data. The online order data is obtained from an e-commerce platform or a home delivery company's system, and is used to grasp what products are ordered in which area and the delivery status. In addition, the collection unit can also collect people flow data using Wi-Fi connection data. The Wi-Fi connection data is obtained from public Wi-Fi access points or Wi-Fi networks in commercial facilities, and is used to grasp people's stay status and movement patterns in a specific area. This allows the collection unit to collect a wide range of data from various data sources and grasp the situation in real time. In addition, the collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and the movement unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0066] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data and determine which areas many people gather in. Specifically, the AI ​​uses a machine learning algorithm to analyze GPS data and Wi-Fi connection data to identify people's movement patterns and stay times. This makes it possible to identify places where many people gather, such as commercial facilities and residential areas. The analysis unit also uses AI to analyze delivery reservation / performance data and determine which areas are performing a lot of deliveries. For example, the AI ​​can analyze past order data and predict trends in delivery demand in specific areas and time periods. Furthermore, the analysis unit can also use AI to identify areas with high demand. This allows the analysis unit to quickly and accurately analyze the collected data and identify areas and time periods with high demand in real time. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term demand forecasts and trend analysis. For example, based on past delivery data, it predicts fluctuations in demand during specific seasons and events and plans future measures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term demand management and anomaly detection, improving the reliability and efficiency of the entire system.

[0067] The moving unit moves to an optimal location based on the analysis results obtained by the analysis unit. The moving unit can, for example, use AI to select an optimal route and move to areas with high demand. Specifically, AI analyzes real-time traffic data and road condition data and calculates an optimal moving route. This allows the moving unit to move efficiently while avoiding traffic jams and obstacles. The moving unit can also detect road conditions and obstacles using cameras and sensors and move safely. For example, the camera monitors road conditions in real time, and the sensor detects obstacles and pedestrians. This allows the moving unit to reach the destination safely and efficiently. Furthermore, the moving unit can also use AI to select an appropriate route and move efficiently. For example, AI can analyze past movement data and demand data and plan an optimal movement schedule. This allows the moving unit to move quickly to areas with high demand and provide services efficiently. Furthermore, the moving unit can continuously optimize the movement route and schedule based on data updated in real time. This allows the moving unit to always realize optimal movement based on the latest information, improving the efficiency and reliability of the entire system.

[0068] When the autonomous vehicle arrives at the optimal location, the receiving unit opens the parcel locker, allowing the user to receive the parcel. Specifically, the receiving unit is designed to allow elderly people and people with disabilities to access an autonomous vehicle near their home and receive their parcel. For example, the receiving unit is equipped with an alert system to notify the user of the arrival of the vehicle, and the user can receive the notification through a smartphone or a dedicated app. The receiving unit also allows the user to check the location of the parcel locker using a smartphone. The user can use a dedicated app to check the current location and estimated arrival time of the autonomous vehicle, so that the user can receive the parcel according to his or her schedule. Furthermore, the receiving unit uses a two-dimensional code (e.g., a QR code) or NFC technology to allow the user to easily open the locker. This allows the user to open the locker simply by holding up their smartphone, allowing the user to smoothly receive the parcel. In addition, the receiving unit can collect user feedback and use it to improve the service. For example, the user can report problems or requests regarding reception through the app, and the system is improved based on that information. This allows the receiving unit to provide the user with a means to receive the parcel quickly and reliably, thereby improving the convenience and reliability of the entire system.

[0069] The collection unit can collect people flow data or home delivery reservation / performance data. The collection unit can collect people flow data using, for example, GPS data. For example, the collection unit can also collect people flow data using Wi-Fi connection data. The collection unit can also collect home delivery reservation / performance data using online order data. For example, the collection unit can also collect home delivery reservation / performance data using delivery history data of a home delivery company. This allows moving to an optimal location by collecting people flow data or home delivery reservation / performance data. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data to AI and cause AI to collect people flow data.

[0070] The analysis unit can analyze the data collected by the collection unit and grasp which area a large number of people are gathering. The analysis unit can analyze the collected data using, for example, AI and grasp which area a large number of people are gathering. For example, the analysis unit can use AI to identify places where many people gather, such as commercial facilities and residential areas. The analysis unit can also analyze delivery reservation / performance data using AI and grasp which area a large number of deliveries are being made in. For example, the analysis unit can also use AI to identify areas with high demand. This allows the delivery to be moved to the optimal location by analyzing the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and have AI perform the analysis.

[0071] The moving unit can move to areas with high demand based on the analysis results obtained by the analysis unit. The moving unit can, for example, use AI to select an optimal route and move to areas with high demand. For example, the moving unit can detect road conditions and obstacles using a camera or a sensor and move safely. The moving unit can also select an appropriate route using AI and move efficiently. For example, the moving unit can move to areas with high demand, such as commercial facilities and residential areas, using AI. As a result, by moving to areas with high demand, packages can be efficiently received. A part or all of the above-mentioned processing in the moving unit may be performed using AI, for example, or may be performed without using AI. For example, the moving unit can input the analysis results to AI and cause AI to select a moving route.

[0072] When the autonomous vehicle arrives at an appropriate location, the receiving unit opens the parcel locker, and the user can receive the parcel. The receiving unit allows, for example, elderly people and people with disabilities to access an autonomous vehicle near their home and receive the parcel. The receiving unit also allows the user to check the location of the parcel locker using a smartphone. For example, the receiving unit can use a dedicated app to check the current location and estimated arrival time of the autonomous vehicle. This allows the user to receive the parcel. Some or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the receiving unit can input location information of the smartphone into AI and have the AI ​​execute location confirmation of the parcel locker.

[0073] The receiving unit allows a user to confirm the location of the parcel locker using a mobile terminal. The receiving unit can confirm the location of the parcel locker using, for example, a smartphone. For example, the receiving unit can confirm the current location and estimated arrival time of the autonomous vehicle using a dedicated app. The receiving unit can also confirm the location of the parcel locker using a tablet. For example, the receiving unit can confirm the location of the autonomous vehicle using a GPS function of the tablet. This allows a user to confirm the location of the parcel locker using a smartphone. A part or all of the above-mentioned processing in the receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the receiving unit can input the location information of the smartphone into the AI ​​and cause the AI ​​to execute the location confirmation of the parcel locker.

[0074] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. In addition, when the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. Furthermore, when the user is in a hurry, the collection unit can also shorten the timing of data collection and collect data quickly. This makes it possible to reduce the burden on the user by adjusting the timing of data collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The collection unit can analyze the past data collection history and select the optimal collection method. For example, the collection unit can improve efficiency by collecting data at a specific time period from the past data collection history. The collection unit can also prioritize data collection at a specific location based on the past data collection history. Furthermore, the collection unit can analyze the past data collection history and select the most effective collection method. In this way, the optimal collection method can be selected by analyzing the past data collection history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into AI and cause AI to select the optimal collection method.

[0076] The collection unit can filter the collection target based on a specific event or season when collecting data. For example, the collection unit can prioritize data collection in an area where a specific event is held. The collection unit can also filter the collection target by taking into account seasonal human flow data. Furthermore, the collection unit can dynamically change the collection target based on a specific event or season. This allows efficient data collection by filtering the collection target based on a specific event or season. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data related to a specific event or season to AI and have AI perform filtering of the collection target.

[0077] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit can postpone the collection of data with low importance. In addition, when the user is relaxed, the collection unit can prioritize the collection of detailed data. Furthermore, when the user is in a hurry, the collection unit can also prioritize the collection of data with high importance. In this way, data can be collected efficiently by determining the priority of data to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0078] The collection unit can prioritize collection of highly relevant data based on the geographical location information when collecting data. The collection unit can prioritize collection of data in a specific area based on the geographical location information, for example. The collection unit can also collect highly relevant data taking into account the geographical location information. Furthermore, the collection unit can determine the priority order of data collection based on the geographical location information. As a result, data can be collected efficiently by preferentially collecting highly relevant data taking into account the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the geographical location information to an AI and cause the AI ​​to collect highly relevant data.

[0079] The collection unit can analyze social media activities and collect related data when collecting data. The collection unit can, for example, analyze social media activities and collect data related to a specific topic. The collection unit can also prioritize data collection in a specific area based on social media activities. Furthermore, the collection unit can analyze social media activities and collect highly relevant data. This allows data to be collected efficiently by analyzing social media activities and collecting related data. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without using AI. For example, the collection unit can input social media posting data into AI and cause AI to collect related data.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit can provide a simple and highly visible analysis result. In addition, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can also provide an analysis result that covers the main points. In this way, by adjusting the expression method of the analysis according to the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit can perform a detailed analysis on data with high importance, for example. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis based on the importance of the data. This allows the analysis to be performed efficiently by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the data to an AI and cause the AI ​​to adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit can select an optimal analysis algorithm depending on the category of data, for example. The analysis unit can also apply different analysis algorithms for each category of data. Furthermore, the analysis unit can dynamically change the analysis algorithm based on the category of data. This allows efficient analysis by applying different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to AI and cause the AI ​​to select the optimal analysis algorithm.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit can provide a short and to the point analysis result. In addition, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is excited, the analysis unit can also provide a visually stimulating analysis result. In this way, by adjusting the length of the analysis according to the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0084] The analysis unit can determine the analysis priority based on the collection time of the data during the analysis. The analysis unit can, for example, preferentially analyze the latest data. The analysis unit can also determine the analysis priority based on past data. Furthermore, the analysis unit can adjust the order of analysis based on the collection time of the data. This allows efficient analysis by determining the analysis priority based on the collection time of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the data to AI and cause the AI ​​to determine the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit can, for example, prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of data. This allows analysis to be performed efficiently by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to AI and cause AI to adjust the order of analysis.

[0086] The moving unit can estimate the user's emotion and adjust the movement criterion based on the estimated user's emotion. For example, when the user is nervous, the moving unit can set a movement criterion that emphasizes safety. Also, when the user is relaxed, the moving unit can set a movement criterion that emphasizes efficiency. Furthermore, when the user is in a hurry, the moving unit can set a criterion that prioritizes quick movement. In this way, by adjusting the movement criterion according to the user's emotion, it is possible to move safely and efficiently. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the moving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the moving unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0087] The movement unit can improve the accuracy of movement by considering the interrelationships of data during movement. The movement unit can select an optimal movement route based on, for example, the interrelationships of data. The movement unit can also improve the accuracy of movement by considering the interrelationships of data. Furthermore, the movement unit can adjust the order of movement based on the interrelationships of data. As a result, the movement can be performed efficiently by improving the accuracy of movement by considering the interrelationships of data. A part or all of the above-mentioned processing in the movement unit may be performed using, for example, AI, or may be performed without using AI. For example, the movement unit can input the interrelationships of data into AI and cause AI to execute the improvement of the accuracy of movement.

[0088] The movement unit can move while taking into account the attribute information of the region. The movement unit can select an optimal movement route based on, for example, the attribute information of the region. The movement unit can also improve the accuracy of the movement by taking into account the attribute information of the region. Furthermore, the movement unit can adjust the order of movement based on the attribute information of the region. As a result, the movement can be performed efficiently by taking into account the attribute information of the region. A part or all of the above-mentioned processing in the movement unit can be performed, for example, using AI or can be performed without using AI. For example, the movement unit can input the attribute information of the region into the AI ​​and cause the AI ​​to select a movement route.

[0089] The moving unit can estimate the user's emotion and adjust the order of displaying the results of the movement based on the estimated emotion of the user. For example, when the user is nervous, the moving unit can display important information first. Furthermore, when the user is relaxed, the moving unit can sequentially display detailed information. Furthermore, when the user is in a hurry, the moving unit can display information with the main points first. In this way, by adjusting the order of displaying the results of the movement according to the user's emotion, it is possible to provide information that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the moving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the moving unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0090] The movement unit can move while taking the geographical distribution into consideration. The movement unit can select an optimal movement route based on the geographical distribution, for example. The movement unit can also improve the accuracy of the movement by taking the geographical distribution into consideration. Furthermore, the movement unit can also adjust the order of movement based on the geographical distribution. As a result, the movement can be performed efficiently by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the movement unit can be performed, for example, using AI, or can be performed without using AI. For example, the movement unit can input geographical distribution data to AI and cause AI to select a movement route.

[0091] The movement unit can improve the accuracy of the movement based on the related literature during the movement. The movement unit can select an optimal movement route based on the related literature, for example. The movement unit can also improve the accuracy of the movement by referring to the related literature. Furthermore, the movement unit can also adjust the order of the movement based on the related literature. Thereby, the movement can be efficiently performed by improving the accuracy of the movement by referring to the related literature. A part or all of the above-mentioned processing in the movement unit may be performed using, for example, AI, or may be performed without using AI. For example, the movement unit can input the related literature data into the AI ​​and cause the AI ​​to execute the improvement of the accuracy of the movement.

[0092] The receiving unit can estimate the user's emotion and adjust the receiving method based on the estimated user's emotion. For example, when the user is nervous, the receiving unit can provide a simple and highly visible receiving method. Furthermore, when the user is relaxed, the receiving unit can provide a detailed receiving method. Furthermore, when the user is in a hurry, the receiving unit can also provide a quick receiving method. In this way, by adjusting the receiving method according to the user's emotion, a receiving method that is easy for the user to understand can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the receiving unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0093] The receiving unit can select the optimal receiving method by analyzing the past receiving history at the time of receiving. The receiving unit can, for example, suggest the optimal receiving method based on the past receiving history. The receiving unit can also analyze the past receiving history and select a receiving method that suits the user. Furthermore, the receiving unit can also customize the receiving method by referring to the past receiving history. In this way, the optimal receiving method can be selected by analyzing the past receiving history. A part or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the receiving unit can input the past receiving history into AI and have the AI ​​select the optimal receiving method.

[0094] The receiving unit can customize the receiving means based on the user's current living situation at the time of receiving. For example, if the user is an elderly person, the receiving unit can provide a means for receiving with a simple operation. In addition, if the user is physically disabled, the receiving unit can provide voice guidance and support functions. Furthermore, if the user is busy, the receiving unit can provide a means for quickly receiving. This allows efficient receiving by customizing the receiving means based on the user's current living situation. Part or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the receiving unit can input the user's living situation data into AI and have the AI ​​execute the customization of the receiving means.

[0095] The receiving unit can estimate the user's emotion and determine the priority of receiving based on the estimated user's emotion. For example, when the user is stressed, the receiving unit can postpone receiving with low importance. Also, when the user is relaxed, the receiving unit can provide detailed receiving procedures. Furthermore, when the user is in a hurry, the receiving unit can prioritize receiving with high importance. In this way, by determining the priority of receiving according to the user's emotion, it is possible to efficiently receive. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the receiving unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0096] The receiving unit can select the optimal receiving method in consideration of the geographical location information at the time of receiving. The receiving unit can, for example, suggest the optimal receiving location based on the geographical location information. The receiving unit can also customize the receiving method in consideration of the geographical location information. Furthermore, the receiving unit can also determine the priority of receiving based on the geographical location information. As a result, by selecting the optimal receiving method in consideration of the geographical location information, receiving can be performed efficiently. A part or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input the geographical location information into AI and cause the AI ​​to select the optimal receiving method.

[0097] The receiving unit can analyze social media activity and suggest a receiving means at the time of receiving. The receiving unit can, for example, analyze social media activity and suggest a receiving means related to a specific topic. The receiving unit can also suggest a receiving means in a specific area based on the social media activity. Furthermore, the receiving unit can analyze social media activity and suggest a receiving means with high relevance. As a result, by analyzing social media activity and suggesting a receiving means, receiving can be performed efficiently. A part or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input social media posting data into AI and have the AI ​​execute the suggestion of a receiving means.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The parcel locker system further includes an energy management unit. The energy management unit can supply energy to the autonomous vehicle using solar panels or wind power generation equipment. For example, the energy management unit can install solar panels on the roof of the vehicle to store energy during the day. Also, a wind power generation device can be attached to the side of the vehicle to generate energy using wind power while driving. Furthermore, the energy management unit can monitor energy usage in real time and perform efficient energy management. This enables energy self-sufficiency and realizes an environmentally friendly system.

[0100] The collection unit can further collect health data of the user. For example, the collection unit can collect data such as heart rate and number of steps from a smart watch or a fitness tracker. The collection unit can also suggest an optimal delivery time based on the user's health condition. Furthermore, the collection unit can analyze the user's health data and provide a service according to the health condition. This makes it possible to provide a service that takes the user's health condition into consideration.

[0101] The analysis unit can further analyze weather data. For example, the analysis unit can collect weather data and reflect it in the selection of delivery routes. The analysis unit can also dynamically adjust delivery schedules in response to changes in the weather. Furthermore, the analysis unit can analyze seasonal delivery patterns based on past weather data and plan optimal delivery strategies. This enables efficient delivery with minimal impact from weather.

[0102] The travel department can further select travel routes taking traffic congestion information into consideration. For example, the travel department can collect real-time traffic information and select routes that avoid congestion. The travel department can also analyze past traffic data and adjust travel schedules to avoid times when congestion is likely to occur. Furthermore, the travel department can determine the optimal delivery order based on traffic congestion information. This enables efficient delivery with minimal delays due to traffic congestion.

[0103] The receiving unit can further customize the receiving method based on the user's preferences. For example, the receiving unit can prioritize the receiving location and time period specified by the user. The receiving unit can also suggest the optimal receiving method based on the user's past receiving history. Furthermore, the receiving unit can collect user feedback and reflect it in improving the receiving method. This makes it possible to provide a flexible receiving method that meets the needs of the user.

[0104] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, when the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. Furthermore, when the user is in a hurry, the collection unit can also shorten the timing of data collection to quickly collect data. In this way, the burden on the user can be reduced by adjusting the timing of data collection according to the user's emotions.

[0105] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, when the user is nervous, the analysis unit can provide a simple and highly visible analysis result. When the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can provide an analysis result that focuses on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand.

[0106] The movement unit can estimate the user's emotions and adjust the movement criteria based on the estimated user's emotions. For example, when the user is nervous, the movement unit can set movement criteria that emphasize safety. Also, when the user is relaxed, the movement unit can set movement criteria that emphasize efficiency. Furthermore, when the user is in a hurry, the movement unit can also set criteria that prioritize quick movement. In this way, by adjusting the movement criteria according to the user's emotions, movement can be performed safely and efficiently.

[0107] The receiving unit can estimate the user's emotion and adjust the receiving method based on the estimated user's emotion. For example, the receiving unit can provide a simple and highly visible receiving method when the user is nervous. Also, the receiving unit can provide a detailed receiving method when the user is relaxed. Furthermore, the receiving unit can provide a quick receiving method when the user is in a hurry. In this way, by adjusting the receiving method according to the user's emotion, it is possible to provide a receiving method that is easy for the user to understand.

[0108] The receiving unit can estimate the user's emotion and determine the priority of receiving based on the estimated emotion of the user. For example, when the user is stressed, the receiving unit can postpone receiving with low importance. Also, when the user is relaxed, the receiving unit can provide detailed receiving procedures. Furthermore, when the user is in a hurry, the receiving unit can prioritize receiving with high importance. In this way, by determining the priority of receiving according to the emotion of the user, it is possible to efficiently perform receiving.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects data. The collection unit can collect, for example, people flow data and home delivery reservation / performance data. The collection unit can collect people flow data using, for example, GPS data. The collection unit can also collect home delivery reservation / performance data using online order data. For example, the collection unit can collect people flow data using Wi-Fi connection data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected data and determine which areas many people gather in. The analysis unit can, for example, use AI to identify places where many people gather, such as commercial facilities and residential areas. The analysis unit can also use AI to analyze delivery reservation / performance data and determine which areas are seeing a lot of deliveries. For example, the analysis unit can use AI to identify areas with high demand. Step 3: The moving unit moves to the optimal location based on the analysis results obtained by the analysis unit. The moving unit can, for example, use AI to select the optimal route and move to areas with high demand. The moving unit can, for example, use cameras and sensors to detect road conditions and obstacles and move safely. The moving unit can also use AI to select an appropriate route and move efficiently. For example, the moving unit can use AI to move to areas with high demand, such as commercial facilities and residential areas. Step 4: When the autonomous vehicle arrives at the optimal location, the receiving unit opens the parcel locker, allowing the user to receive the parcel. The receiving unit allows, for example, elderly people or people with disabilities to access an autonomous vehicle near their home and receive their parcel. The receiving unit also allows the user to check the location of the parcel locker using a smartphone. For example, the receiving unit can use a dedicated app to check the current location and estimated arrival time of the autonomous vehicle.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0112] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned collection unit, analysis unit, movement unit, and receiving 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 can collect people flow data and home delivery reservation / performance data using the camera 42 and sensors of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The movement unit is realized, for example, by the control unit 46A of the smart device 14, and detects road conditions and obstacles using the camera 42 and sensors and moves safely. The receiving unit provides the user with package reception information using, for example, the output device 40 of the smart device 14. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0118] 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.

[0119] 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 the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] 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 performed in a secure state.

[0122] Fig. 4 shows an example of 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.

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

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

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

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned collection unit, analysis unit, movement unit, and receiving 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 can collect people flow data and home delivery reservation / performance data using the camera 42 and sensors of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The movement unit is realized, for example, by the control unit 46A of the smart glasses 214, and detects road conditions and obstacles using the camera 42 and sensors to move safely. The receiving unit provides the user with package reception information using, for example, the output device 40 of the smart glasses 214. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0134] 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.

[0135] 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 the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] 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 performed in a secure state.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0142] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] 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 voice indicating a user input for 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.

[0144] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned collection unit, analysis unit, movement unit, and receiving unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect people flow data and home delivery reservation / performance data using the camera 42 and sensors of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The movement unit is realized, for example, by the control unit 46A of the headset type terminal 314, and detects road conditions and obstacles using the camera 42 and sensors and moves safely. The receiving unit provides the user with package reception information using, for example, the output device 40 of the headset type terminal 314. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0150] 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.

[0151] 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 the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] 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 performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0155] 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.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0159] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 a voice indicating a user input for 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.

[0161] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0163] Each of the multiple elements including the above-mentioned collection unit, analysis unit, movement unit, and receiving unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect people flow data and home delivery reservation / performance data using the camera 42 and sensors of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The movement unit is realized, for example, by the control unit 46A of the robot 414, and detects road conditions and obstacles using the camera 42 and sensors and moves safely. The receiving unit provides the user with package reception information using, for example, the output device 40 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] 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 Universal Serial Bus (USB) 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.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0177] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a moving unit that moves to an optimal location based on the analysis result obtained by the analysis unit; A receiving section for receiving luggage is provided. A system characterized by: (Appendix 2) The collecting unit includes: Collecting people flow data or delivery reservation / performance data 2. The system of claim 1, (Appendix 3) The analysis unit is The data collected by the collection unit is analyzed to determine in which areas a large number of people are gathering. 2. The system of claim 1, (Appendix 4) The moving unit is Based on the analysis results obtained by the analysis unit, the device moves to an area with high demand. 2. The system of claim 1, (Appendix 5) The receiving unit is Once the autonomous vehicle arrives at the appropriate location, it will open the parcel locker and the user can collect their parcel. 2. The system of claim 1, (Appendix 6) The receiving unit is Users can check the location of parcel lockers using their mobile devices. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze past data collection history and select the optimal collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: Filter data collection based on specific events or seasons as it occurs 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, prioritize collection of relevant data based on geographic location 2. The system of claim 1, (Appendix 12) The collecting unit includes: During data collection, analyze social media activity and collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 19) The moving unit is Estimating a user's emotion and adjusting a movement criterion based on the estimated user's emotion 2. The system of claim 1, (Appendix 20) The moving unit is During migration, data correlation is taken into account to improve migration accuracy 2. The system of claim 1, (Appendix 21) The moving unit is When moving, local attribute information is taken into consideration 2. The system of claim 1, (Appendix 22) The moving unit is Estimate user emotions and adjust the order in which travel results are displayed based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The moving unit is When moving, consider geographical distribution. 2. The system of claim 1, (Appendix 24) The moving unit is During migration, improve the accuracy of migration based on related literature 2. The system of claim 1, (Appendix 25) The receiving unit is Estimate the user's emotions and adjust the method of receipt based on the estimated user's emotions 2. The system of claim 1, (Appendix 26) The receiving unit is When receiving a payment, the system analyzes past receipt history to select the optimal receipt method. 2. The system of claim 1, (Appendix 27) The receiving unit is At the time of receipt, customize the receipt method based on the user's current life situation 2. The system of claim 1, (Appendix 28) The receiving unit is Estimate the user's emotions and determine the priority of receipt based on the estimated user's emotions 2. The system of claim 1, (Appendix 29) The receiving unit is At the time of collection, geolocation is taken into account to find the optimal collection method 2. The system of claim 1, (Appendix 30) The receiving unit is At the time of collection, analyze social media activity to suggest collection methods 2. The system of claim 1, [Explanation of symbols]

[0183] 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 data; an analysis unit that analyzes the data collected by the collection unit; a moving unit that moves to an optimal location based on the analysis result obtained by the analysis unit; A receiving section for receiving luggage is provided. A system characterized by:

2. The collecting unit includes: Collecting people flow data or delivery reservation / performance data 2. The system of claim 1 .

3. The analysis unit is The data collected by the collection unit is analyzed to determine in which areas a large number of people are gathering.

2. The system of claim 1 .

4. The moving unit is Based on the analysis results obtained by the analysis unit, the device moves to an area with high demand.

2. The system of claim 1 .

5. The receiving unit is Once the autonomous vehicle arrives at the appropriate location, it will open the parcel locker and the user can collect their parcel.

2. The system of claim 1 .

6. The receiving unit is Users can check the location of parcel lockers using their mobile devices.

2. The system of claim 1 .

7. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

8. The collecting unit includes: Analyze past data collection history and select the optimal collection method 2. The system of claim 1 .

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