System

Abstract

An object of a system according to an embodiment is to realize efficient delivery through selection of a deliverer and optimization of a delivery route.SOLUTION: A system according to an embodiment includes a deliverer selector, a delivery route generator, a health monitor, and an emotion estimator. The deliverer selection section analyzes the past delivery history of the deliverer to select the most efficient deliverer. The delivery route generation section generates an optimal delivery route for the deliverer selected by the deliverer selection section. The health monitoring unit monitors the health condition of the courier. The emotion estimator analyzes the emotional state of the delivery person.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not adequately optimized the selection of delivery personnel and delivery routes, making it difficult to deliver efficiently.

[0005] The system according to the embodiment aims to achieve efficient delivery through the selection of delivery personnel and the optimization of delivery routes. [Means for solving the problem]

[0006] The system according to the embodiment includes a delivery person selection unit, a delivery route generation unit, a health monitoring unit, and an emotion estimation unit. The delivery person selection unit analyzes the delivery history of delivery persons to select the most efficient delivery person. The delivery route generation unit generates an optimal delivery route for the delivery person selected by the delivery person selection unit. The health monitoring unit monitors the health status of the delivery person. The emotion estimation unit analyzes the emotional state of the delivery person. [Effects of the Invention]

[0007] The system according to the embodiment can achieve efficient delivery through the selection of delivery personnel and the optimization of delivery routes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The delivery system according to the embodiment of the present invention utilizes ordinary delivery personnel and establishes an advanced delivery model controlled by AI. This enables the delivery system to mitigate the 2024 logistics problem and provide efficient and convenient delivery services.

[0029] A delivery system according to an embodiment includes a delivery person selection unit, a delivery route generation unit, a health monitoring unit, and an emotion estimation unit. The delivery person selection unit analyzes the delivery history of delivery persons to select the most efficient delivery person. For example, the delivery person selection unit collects delivery history data and uses AI to analyze the data to evaluate delivery efficiency. The delivery history data is used to identify areas and time periods in which delivery persons excel, and the optimal delivery person is selected based on that information. The delivery history data is analyzed to develop an algorithm to evaluate the performance of delivery persons. The delivery route generation unit generates optimal delivery routes for delivery persons selected by the delivery person selection unit. For example, the delivery route generation unit uses AI to analyze delivery person location information, traffic conditions, and delivery item information in real time to generate optimal delivery routes. The delivery route generation unit assigns delivery of specific deliveries based on the delivery person's skills and specialties. The delivery route generation unit utilizes the delivery person's communication skills to promote direct communication with customers. The health monitoring unit monitors the health status of delivery persons. For example, the health monitoring unit uses a wearable device to collect data such as heart rate, number of steps, and sleep time. Based on the health status data, an algorithm is developed to evaluate the fatigue level of delivery personnel and suggest delivery routes that cause less fatigue. A system is constructed that monitors health status in real time and dynamically adjusts delivery routes according to the health status. The emotion estimation unit analyzes the emotional state of delivery personnel. For example, the emotion estimation unit collects facial expression and voice data, and AI analyzes the data to calculate an emotion score. Based on the emotion estimation data, an algorithm is developed that evaluates the stress level of delivery personnel and suggests delivery routes that cause less stress. A system is constructed that monitors emotional status in real time and dynamically adjusts delivery routes according to the emotional state. As a result, the delivery system according to the embodiment enables efficient delivery that takes into account the health status and emotional state.

[0030] The delivery person selection unit can analyze delivery history data and select the most efficient delivery person based on indicators such as delivery time, delivery success rate, and customer satisfaction. The delivery person selection unit, for example, collects delivery history data and analyzes it using AI to evaluate delivery efficiency. For example, it selects the most efficient delivery person based on indicators such as delivery time, delivery success rate, and customer satisfaction. It uses delivery history data to identify areas and time periods in which delivery people excel, and selects the most suitable delivery person based on that information. For example, it prioritizes the selection of delivery people with extensive delivery experience in specific areas. It analyzes delivery history data and develops an algorithm to evaluate delivery person performance. For example, it evaluates delivery people's skills and efficiency from past delivery history and selects the most suitable delivery person. This maximizes delivery person efficiency.

[0031] The health monitoring unit can collect data on the delivery person's heart rate, number of steps, and sleep time. For example, the health monitoring unit uses a wearable device to monitor the delivery person's health condition and collects data such as heart rate, number of steps, and sleep time. This data is used to suggest delivery routes based on the delivery person's health condition. An algorithm is developed that evaluates the delivery person's fatigue level based on the health condition data and suggests delivery routes that cause less fatigue. For example, a route that avoids long delivery times is suggested. A system is built that monitors the delivery person's health condition in real time and dynamically adjusts the delivery route according to the health condition. For example, if the delivery person's health condition worsens, the delivery route is shortened. This allows for detailed monitoring of the delivery person's health condition.

[0032] The delivery route generation unit can assign delivery of items according to the skills and special abilities of delivery personnel. The delivery route generation unit, for example, registers the skills and special abilities of delivery personnel in a database and assigns the delivery of specific items based on that information. For example, it assigns to delivery personnel who are good at delivering heavy packages or fragile items. It builds a system that evaluates the skills and special abilities of delivery personnel and assigns the delivery of specific items based on the evaluation results. For example, it assigns specialized deliveries to delivery personnel with specific skills. It develops a system that evaluates the skills and special abilities of delivery personnel in real time and dynamically assigns the delivery of specific items based on that information. For example, it assigns the optimal delivery according to the skills of the delivery personnel. This makes it possible to make optimal deliveries according to the skills and special abilities of the delivery personnel.

[0033] The delivery route generation unit can utilize the communication skills of delivery personnel to promote direct communication with customers. The delivery route generation unit, for example, evaluates the communication skills of delivery personnel and builds a system that promotes direct communication with customers based on the evaluation results. For example, it assigns customer service to delivery personnel with high communication skills. It develops a training program to improve the communication skills of delivery personnel and promotes direct communication with customers through the program. It introduces an incentive system that utilizes the communication skills of delivery personnel to promote direct communication with customers. For example, it provides rewards to delivery personnel with high customer satisfaction. This improves communication with customers.

[0034] The delivery route generation unit can automatically suggest a packing method according to the characteristics of the delivery item. For example, the delivery route generation unit will collect data on the characteristics of the delivery item, and build a system that uses AI to analyze that data and automatically suggest the optimal packing method. For example, it will suggest appropriate packing methods for fragile items or items that require temperature control. An algorithm will be developed to suggest specialized packing methods according to the characteristics of the delivery item, and an automatic suggestion system will be built based on that algorithm. For example, it will suggest packing methods according to the size and weight of the delivery item. A system will be developed that analyzes the characteristic data of the delivery item in real time and dynamically suggest the optimal packing method based on the results. For example, if the characteristics of the delivery item change, the packing method will be changed immediately. This will make it possible to suggest the optimal packing method according to the characteristics of the delivery item.

[0035] The delivery route generation unit can suggest a delivery means based on the weight and size of the delivery item. For example, the delivery route generation unit collects weight and size data of delivery items, and builds a system in which AI analyzes the data to suggest the optimal delivery means. For example, it suggests delivery means such as bicycle, motorbike, or car. An algorithm is developed to suggest the optimal delivery means based on the weight and size of the delivery item, and an automatic suggestion system is built based on that algorithm. For example, it suggests a car for heavy items. A system is developed that analyzes weight and size data of delivery items in real time and dynamically suggests the optimal delivery means based on the results. For example, the delivery means can be changed immediately if the characteristics of the delivery item change. This makes it possible to suggest the optimal delivery means based on the weight and size of the delivery item.

[0036] The delivery route generation unit can automatically propose an insurance plan according to the type of delivery item. For example, the delivery route generation unit collects data on the type of delivery item, and builds a system that uses AI to analyze that data and automatically propose the most appropriate insurance plan. For example, it proposes appropriate insurance plans for fragile or expensive items. An algorithm is developed to propose specific insurance plans according to the type of delivery item, and an automatic proposal system is built based on that algorithm. For example, it proposes an insurance plan with high compensation for expensive items. A system is developed that analyzes delivery type data in real time and dynamically proposes the most appropriate insurance plan based on the results. For example, the insurance plan can be changed immediately if the characteristics of the delivery item change. This makes it possible to propose the most appropriate insurance plan according to the type of delivery item.

[0037] The delivery route generation unit can propose delivery time slots according to the characteristics of the delivery item. For example, the delivery route generation unit will collect data on the characteristics of the delivery item, and build a system that uses AI to analyze that data and propose the optimal delivery time slot. For example, it may propose early morning for medicines and evening for daily necessities. An algorithm will be developed that proposes specific delivery time slots according to the characteristics of the delivery item, and an automatic proposal system will be built based on that algorithm. For example, it will propose cooler times for items that require temperature control. A system will be developed that analyzes the characteristic data of the delivery item in real time and dynamically proposes the optimal delivery time slot based on the results. For example, the delivery time slot can be changed immediately if the characteristics of the delivery item change. This will make it possible to propose the optimal delivery time slot according to the characteristics of the delivery item.

[0038] In addition to optimizing delivery routes, the delivery route generation unit can analyze traffic conditions and weather information during deliveries in real time and dynamically change the route. For example, in addition to optimizing delivery routes, the delivery route generation unit will build a system that collects traffic and weather information in real time and uses AI to analyze that data to dynamically suggest the optimal route. For example, it will propose routes that avoid traffic congestion and bad weather. We will develop an algorithm that analyzes traffic conditions and weather information during deliveries in real time and dynamically changes delivery routes based on the results. For example, it will propose routes that avoid traffic accidents and road construction. In addition to optimizing delivery routes, we will develop a system that monitors traffic conditions and weather information in real time and dynamically adjusts delivery routes based on that data. For example, it will propose routes that can adapt to sudden changes in weather. This will make it possible to dynamically change the optimal route according to traffic conditions and weather information.

[0039] The delivery route generation unit can optimize break times and meal times for delivery personnel in addition to managing their schedules. For example, in addition to managing delivery personnel's schedules, the delivery route generation unit develops an algorithm that optimizes break times and meal times, and builds a system that automatically adjusts schedules based on that algorithm. For example, it proposes schedules that take into account the health status of delivery personnel. It analyzes delivery personnel's schedule data and develops an algorithm that optimizes break times and meal times. For example, it proposes break times to avoid long periods of continuous delivery. In addition to managing delivery personnel's schedules, it develops a system that monitors break times and meal times in real time and dynamically adjusts schedules based on that data. For example, it proposes break times based on the delivery personnel's level of fatigue. This makes it possible to optimize break times and meal times for delivery personnel.

[0040] The delivery route generation unit can propose routes that minimize the energy consumption of delivery personnel in addition to optimizing delivery routes. For example, the delivery route generation unit will build a system that proposes routes that minimize the energy consumption of delivery personnel in addition to optimizing delivery routes. For example, it will propose routes that avoid slopes and long distances. It will develop an algorithm that evaluates the energy consumption of delivery personnel and propose routes that minimize energy consumption based on that algorithm. For example, it will propose routes that prioritize flat roads. In addition to optimizing delivery routes, it will develop a system that monitors the energy consumption of delivery personnel in real time and dynamically proposes routes that minimize energy consumption based on that data. For example, it will change the route if energy consumption increases. This will make it possible to propose routes that minimize the energy consumption of delivery personnel.

[0041] The delivery route generation unit can monitor the status of deliveries in real time. For example, the delivery route generation unit will build a system that not only tracks deliveries but also monitors the status of deliveries (for example, temperature and humidity) in real time. For example, it will constantly monitor the temperature of deliveries that require temperature control. A system will be developed that monitors the status of deliveries in real time and dynamically adjusts delivery routes and methods based on that data. For example, it will use cooling equipment if the temperature rises. In addition to tracking deliveries, a system will be built that analyzes the status of deliveries in real time and optimizes delivery routes and methods based on the results. For example, it will take moisture-proofing measures if the humidity is high. This will allow the status of deliveries to be monitored in real time.

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

[0043] The delivery staff selection unit can analyze delivery history data and select the most efficient delivery staff based on indicators such as delivery time, delivery success rate, and customer satisfaction. For example, delivery history data is collected and AI analyzes the data to evaluate delivery efficiency. For example, the most efficient delivery staff is selected based on indicators such as delivery time, delivery success rate, and customer satisfaction. Delivery history data is used to identify areas and time periods in which delivery staff excel, and the most suitable delivery staff is selected based on that information. For example, delivery staff with extensive delivery experience in a specific area are selected preferentially. Delivery history data is analyzed and an algorithm developed to evaluate delivery staff performance. For example, the skills and efficiency of delivery staff are evaluated from past delivery history, and the most suitable delivery staff is selected. This maximizes delivery staff efficiency.

[0044] The health monitoring unit can collect data on the delivery person's heart rate, number of steps, and sleep time. For example, to monitor the delivery person's health condition, a wearable device can be used to collect data such as heart rate, number of steps, and sleep time. This can then be used to suggest delivery routes based on their health condition. An algorithm can be developed that evaluates the delivery person's fatigue level based on the health condition data and suggests delivery routes that minimize fatigue. For example, a route that avoids long delivery times can be suggested. A system can be built that monitors health condition in real time and dynamically adjusts delivery routes according to health condition. For example, if the delivery condition worsens, the delivery route can be shortened. This allows for detailed monitoring of the delivery person's health condition.

[0045] The delivery route generation unit can assign delivery of items according to the skills and special abilities of delivery personnel. For example, the skills and special abilities of delivery personnel are registered in a database, and specific delivery items are assigned based on that information. For example, assignment is made to delivery personnel who are skilled in delivering heavy packages or fragile items. A system is built to evaluate the skills and special abilities of delivery personnel, and specific delivery items are assigned based on the evaluation results. For example, specialized deliveries are assigned to delivery personnel with specific skills. A system is developed that evaluates the skills and special abilities of delivery personnel in real time, and dynamically assigns specific delivery items based on that information. For example, the optimal delivery item is assigned based on the skills of the delivery personnel. This makes it possible to make optimal deliveries based on the skills and special abilities of the delivery personnel.

[0046] The delivery route generation unit can utilize the communication skills of delivery personnel to promote direct communication with customers. For example, a system can be constructed that evaluates the communication skills of delivery personnel and promotes direct communication with customers based on the evaluation results. For example, customer service can be assigned to delivery personnel with high communication skills. A training program can be developed to improve the communication skills of delivery personnel, and direct communication with customers can be promoted through the program. An incentive system can be introduced to utilize the communication skills of delivery personnel and promote direct communication with customers. For example, rewards can be provided to delivery personnel with high customer satisfaction. This will improve communication with customers.

[0047] The delivery route generation unit can automatically suggest a packing method according to the characteristics of the delivery item. For example, we will build a system that collects data on the characteristics of delivery items, analyzes that data using AI, and automatically suggests the optimal packing method. For example, we will suggest appropriate packing methods for fragile items or items that require temperature control. We will develop an algorithm that suggests specialized packing methods according to the characteristics of the delivery item, and build an automatic suggestion system based on that algorithm. For example, we will suggest packing methods according to the size and weight of the delivery item. We will develop a system that analyzes the characteristic data of delivery items in real time and dynamically suggests the optimal packing method based on the results. For example, if the characteristics of the delivery item change, the packing method will be changed immediately. This will make it possible to suggest the optimal packing method according to the characteristics of the delivery item.

[0048] The delivery route generation unit can suggest delivery means based on the weight and size of the delivery item. For example, we will build a system in which data on the weight and size of delivery items is collected and AI analyzes that data to suggest the optimal delivery means. For example, we will suggest delivery means such as bicycles, motorbikes, and cars. We will develop an algorithm that suggests the optimal delivery means based on the weight and size of the delivery item, and build an automatic suggestion system based on that algorithm. For example, we will suggest cars for heavy items. We will develop a system that analyzes data on the weight and size of delivery items in real time and dynamically suggests the optimal delivery means based on the results. For example, we will instantly change the delivery means if the characteristics of the delivery item change. This will make it possible to suggest the optimal delivery means based on the weight and size of the delivery item.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The delivery person selection unit analyzes the delivery history of delivery people and selects the most efficient delivery person. For example, delivery history data is collected and analyzed by AI to evaluate delivery efficiency. Using the delivery history data, the areas and time periods in which delivery people excel are identified, and the optimal delivery person is selected based on that information. Step 2: The delivery route generation unit generates the optimal delivery route for the delivery person selected by the delivery person selection unit. For example, the delivery route generation unit uses AI to analyze the delivery person's location information, traffic conditions, and delivery item information in real time to generate the optimal delivery route. The delivery route generation unit assigns specific delivery items to delivery people based on their skills and special abilities. Step 3: The health monitoring unit monitors the health of delivery personnel. For example, wearable devices can be used to collect data such as heart rate, number of steps, and sleep time. Based on the health data, an algorithm is developed to evaluate the fatigue level of delivery personnel and suggest delivery routes that minimize fatigue. Step 4: The emotion estimation unit analyzes the emotional state of the delivery person. For example, facial expressions and voice data are collected, and the AI ​​analyzes the data to calculate an emotion score. Based on the emotion estimation data, an algorithm is developed to evaluate the stress level of the delivery person and suggest a delivery route with less stress.

[0051] (Example 2) The delivery system according to the embodiment of the present invention utilizes ordinary delivery personnel and establishes an advanced delivery model controlled by AI. This enables the delivery system to mitigate the 2024 logistics problem and provide efficient and convenient delivery services.

[0052] A delivery system according to an embodiment includes a delivery person selection unit, a delivery route generation unit, a health monitoring unit, and an emotion estimation unit. The delivery person selection unit analyzes the delivery history of delivery persons to select the most efficient delivery person. For example, the delivery person selection unit collects delivery history data and uses AI to analyze the data to evaluate delivery efficiency. The delivery history data is used to identify areas and time periods in which delivery persons excel, and the optimal delivery person is selected based on that information. The delivery history data is analyzed to develop an algorithm to evaluate the performance of delivery persons. The delivery route generation unit generates optimal delivery routes for delivery persons selected by the delivery person selection unit. For example, the delivery route generation unit uses AI to analyze delivery person location information, traffic conditions, and delivery item information in real time to generate optimal delivery routes. The delivery route generation unit assigns delivery of specific deliveries based on the delivery person's skills and specialties. The delivery route generation unit utilizes the delivery person's communication skills to promote direct communication with customers. The health monitoring unit monitors the health status of delivery persons. For example, the health monitoring unit uses a wearable device to collect data such as heart rate, number of steps, and sleep time. Based on the health status data, an algorithm is developed to evaluate the fatigue level of delivery personnel and suggest delivery routes that cause less fatigue. A system is constructed that monitors health status in real time and dynamically adjusts delivery routes according to the health status. The emotion estimation unit analyzes the emotional state of delivery personnel. For example, the emotion estimation unit collects facial expression and voice data, and AI analyzes the data to calculate an emotion score. Based on the emotion estimation data, an algorithm is developed that evaluates the stress level of delivery personnel and suggests delivery routes that cause less stress. A system is constructed that monitors emotional status in real time and dynamically adjusts delivery routes according to the emotional state. As a result, the delivery system according to the embodiment enables efficient delivery that takes into account the health status and emotional state.

[0053] The delivery person selection unit can analyze delivery history data and select the most efficient delivery person based on indicators such as delivery time, delivery success rate, and customer satisfaction. The delivery person selection unit, for example, collects delivery history data and analyzes it using AI to evaluate delivery efficiency. For example, it selects the most efficient delivery person based on indicators such as delivery time, delivery success rate, and customer satisfaction. It uses delivery history data to identify areas and time periods in which delivery people excel, and selects the most suitable delivery person based on that information. For example, it prioritizes the selection of delivery people with extensive delivery experience in specific areas. It analyzes delivery history data and develops an algorithm to evaluate delivery person performance. For example, it evaluates delivery people's skills and efficiency from past delivery history and selects the most suitable delivery person. This maximizes delivery person efficiency.

[0054] The health monitoring unit can collect data on the delivery person's heart rate, number of steps, and sleep time. For example, the health monitoring unit uses a wearable device to monitor the delivery person's health condition and collects data such as heart rate, number of steps, and sleep time. This data is used to suggest delivery routes based on the delivery person's health condition. An algorithm is developed that evaluates the delivery person's fatigue level based on the health condition data and suggests delivery routes that cause less fatigue. For example, a route that avoids long delivery times is suggested. A system is built that monitors the delivery person's health condition in real time and dynamically adjusts the delivery route according to the health condition. For example, if the delivery person's health condition worsens, the delivery route is shortened. This allows for detailed monitoring of the delivery person's health condition.

[0055] The emotion estimation unit can collect facial expression and voice data of delivery personnel and analyze their emotional state. For example, to analyze the emotional state of delivery personnel, the emotion estimation unit collects facial expression and voice data, and AI analyzes the data to calculate an emotion score. This allows for the proposal of a less stressful delivery route. Based on the emotion estimation data, an algorithm is developed to evaluate the stress level of delivery personnel and propose a less stressful delivery route. For example, a route with less traffic congestion is proposed. A system is built that monitors the emotional state in real time and dynamically adjusts the delivery route according to the emotional state. For example, if the emotional state worsens, the delivery route is changed. This allows for a detailed analysis of the emotional state of delivery personnel.

[0056] The delivery route generation unit can assign delivery of items according to the skills and special abilities of delivery personnel. The delivery route generation unit, for example, registers the skills and special abilities of delivery personnel in a database and assigns the delivery of specific items based on that information. For example, it assigns to delivery personnel who are good at delivering heavy packages or fragile items. It builds a system that evaluates the skills and special abilities of delivery personnel and assigns the delivery of specific items based on the evaluation results. For example, it assigns specialized deliveries to delivery personnel with specific skills. It develops a system that evaluates the skills and special abilities of delivery personnel in real time and dynamically assigns the delivery of specific items based on that information. For example, it assigns the optimal delivery according to the skills of the delivery personnel. This makes it possible to make optimal deliveries according to the skills and special abilities of the delivery personnel.

[0057] The delivery route generation unit can utilize the communication skills of delivery personnel to promote direct communication with customers. The delivery route generation unit, for example, evaluates the communication skills of delivery personnel and builds a system that promotes direct communication with customers based on the evaluation results. For example, it assigns customer service to delivery personnel with high communication skills. It develops a training program to improve the communication skills of delivery personnel and promotes direct communication with customers through the program. It introduces an incentive system that utilizes the communication skills of delivery personnel to promote direct communication with customers. For example, it provides rewards to delivery personnel with high customer satisfaction. This improves communication with customers.

[0058] The emotion estimation unit can provide incentives according to the emotional state of delivery personnel, thereby improving their motivation. The emotion estimation unit, for example, analyzes the emotional state of delivery personnel and builds a system that provides incentives according to the emotion score. For example, rewards are provided to delivery personnel with high positive emotion scores. An incentive system is introduced based on the emotion estimation data to improve the motivation of delivery personnel. For example, special rewards are provided to delivery personnel with high emotion scores. A system is developed that monitors the emotional state of delivery personnel in real time and dynamically provides incentives according to their emotional state. For example, rewards are provided immediately to delivery personnel with good emotional states. This improves the motivation of delivery personnel.

[0059] The delivery route generation unit can automatically suggest a packing method according to the characteristics of the delivery item. For example, the delivery route generation unit will collect data on the characteristics of the delivery item, and build a system that uses AI to analyze that data and automatically suggest the optimal packing method. For example, it will suggest appropriate packing methods for fragile items or items that require temperature control. An algorithm will be developed to suggest specialized packing methods according to the characteristics of the delivery item, and an automatic suggestion system will be built based on that algorithm. For example, it will suggest packing methods according to the size and weight of the delivery item. A system will be developed that analyzes the characteristic data of the delivery item in real time and dynamically suggest the optimal packing method based on the results. For example, if the characteristics of the delivery item change, the packing method will be changed immediately. This will make it possible to suggest the optimal packing method according to the characteristics of the delivery item.

[0060] The delivery route generation unit can suggest a delivery means based on the weight and size of the delivery item. For example, the delivery route generation unit collects weight and size data of delivery items, and builds a system in which AI analyzes the data to suggest the optimal delivery means. For example, it suggests delivery means such as bicycle, motorbike, or car. An algorithm is developed to suggest the optimal delivery means based on the weight and size of the delivery item, and an automatic suggestion system is built based on that algorithm. For example, it suggests a car for heavy items. A system is developed that analyzes weight and size data of delivery items in real time and dynamically suggests the optimal delivery means based on the results. For example, the delivery means can be changed immediately if the characteristics of the delivery item change. This makes it possible to suggest the optimal delivery means based on the weight and size of the delivery item.

[0061] The emotion estimation unit can attach a personalized message to the delivery according to the customer's emotion. The emotion estimation unit, for example, analyzes the customer's emotional state and builds a system that automatically generates a personalized message according to the emotion score. For example, a message of thanks is attached to customers with a high positive emotion score. An algorithm is developed based on the emotion estimation data to suggest a personalized message according to the customer's emotion, and an automatic suggestion system is built based on the algorithm. For example, a message according to the customer's emotional state is attached. A system is developed that monitors the customer's emotional state in real time and dynamically generates a personalized message based on the results. For example, the message is changed immediately if the customer's emotional state changes. This makes it possible to attach a personalized message according to the customer's emotion.

[0062] The delivery route generation unit can automatically propose an insurance plan according to the type of delivery item. For example, the delivery route generation unit collects data on the type of delivery item, and builds a system that uses AI to analyze that data and automatically propose the most appropriate insurance plan. For example, it proposes appropriate insurance plans for fragile or expensive items. An algorithm is developed to propose specific insurance plans according to the type of delivery item, and an automatic proposal system is built based on that algorithm. For example, it proposes an insurance plan with high compensation for expensive items. A system is developed that analyzes delivery type data in real time and dynamically proposes the most appropriate insurance plan based on the results. For example, the insurance plan can be changed immediately if the characteristics of the delivery item change. This makes it possible to propose the most appropriate insurance plan according to the type of delivery item.

[0063] The delivery route generation unit can propose delivery time slots according to the characteristics of the delivery item. For example, the delivery route generation unit will collect data on the characteristics of the delivery item, and build a system that uses AI to analyze that data and propose the optimal delivery time slot. For example, it may propose early morning for medicines and evening for daily necessities. An algorithm will be developed that proposes specific delivery time slots according to the characteristics of the delivery item, and an automatic proposal system will be built based on that algorithm. For example, it will propose cooler times for items that require temperature control. A system will be developed that analyzes the characteristic data of the delivery item in real time and dynamically proposes the optimal delivery time slot based on the results. For example, the delivery time slot can be changed immediately if the characteristics of the delivery item change. This will make it possible to propose the optimal delivery time slot according to the characteristics of the delivery item.

[0064] The emotion estimation unit can propose a packaging design for a delivery item that corresponds to the customer's emotions. The emotion estimation unit, for example, analyzes the customer's emotional state and builds a system that automatically proposes a packaging design that corresponds to the emotion score. For example, a bright-colored packaging design is proposed for customers with a high positive emotion score. An algorithm is developed that proposes a packaging design that corresponds to the customer's emotions based on the emotion estimation data, and an automatic proposal system is built based on the algorithm. For example, a design that corresponds to the customer's emotional state is proposed. A system is developed that monitors the customer's emotional state in real time and dynamically proposes a packaging design based on the results. For example, the design is changed immediately if the customer's emotional state changes. This makes it possible to propose the optimal packaging design that corresponds to the customer's emotions.

[0065] In addition to optimizing delivery routes, the delivery route generation unit can analyze traffic conditions and weather information during deliveries in real time and dynamically change the route. For example, in addition to optimizing delivery routes, the delivery route generation unit will build a system that collects traffic and weather information in real time and uses AI to analyze that data to dynamically suggest the optimal route. For example, it will propose routes that avoid traffic congestion and bad weather. We will develop an algorithm that analyzes traffic conditions and weather information during deliveries in real time and dynamically changes delivery routes based on the results. For example, it will propose routes that avoid traffic accidents and road construction. In addition to optimizing delivery routes, we will develop a system that monitors traffic conditions and weather information in real time and dynamically adjusts delivery routes based on that data. For example, it will propose routes that can adapt to sudden changes in weather. This will make it possible to dynamically change the optimal route according to traffic conditions and weather information.

[0066] The delivery route generation unit can optimize break times and meal times for delivery personnel in addition to managing their schedules. For example, in addition to managing delivery personnel's schedules, the delivery route generation unit develops an algorithm that optimizes break times and meal times, and builds a system that automatically adjusts schedules based on that algorithm. For example, it proposes schedules that take into account the health status of delivery personnel. It analyzes delivery personnel's schedule data and develops an algorithm that optimizes break times and meal times. For example, it proposes break times to avoid long periods of continuous delivery. In addition to managing delivery personnel's schedules, it develops a system that monitors break times and meal times in real time and dynamically adjusts schedules based on that data. For example, it proposes break times based on the delivery personnel's level of fatigue. This makes it possible to optimize break times and meal times for delivery personnel.

[0067] The emotion estimation unit can adjust the delivery time according to the customer's emotions. The emotion estimation unit, for example, analyzes the customer's emotional state and builds a system that automatically adjusts the delivery time according to the emotion score. For example, it prioritizes desired delivery times for customers with high positive emotion scores. Based on the emotion estimation data, it develops an algorithm that suggests a delivery time according to the customer's emotions, and builds an automatic adjustment system based on that algorithm. For example, it suggests a delivery time according to the customer's emotional state. It develops a system that monitors the customer's emotional state in real time and dynamically adjusts the delivery time based on the results. For example, it immediately changes the delivery time if the customer's emotional state changes. This makes it possible to adjust the optimal delivery time according to the customer's emotions.

[0068] The delivery route generation unit can propose routes that minimize the energy consumption of delivery personnel in addition to optimizing delivery routes. For example, the delivery route generation unit will build a system that proposes routes that minimize the energy consumption of delivery personnel in addition to optimizing delivery routes. For example, it will propose routes that avoid slopes and long distances. It will develop an algorithm that evaluates the energy consumption of delivery personnel and propose routes that minimize energy consumption based on that algorithm. For example, it will propose routes that prioritize flat roads. In addition to optimizing delivery routes, it will develop a system that monitors the energy consumption of delivery personnel in real time and dynamically proposes routes that minimize energy consumption based on that data. For example, it will change the route if energy consumption increases. This will make it possible to propose routes that minimize the energy consumption of delivery personnel.

[0069] The delivery route generation unit can monitor the status of deliveries in real time. For example, the delivery route generation unit will build a system that not only tracks deliveries but also monitors the status of deliveries (for example, temperature and humidity) in real time. For example, it will constantly monitor the temperature of deliveries that require temperature control. A system will be developed that monitors the status of deliveries in real time and dynamically adjusts delivery routes and methods based on that data. For example, it will use cooling equipment if the temperature rises. In addition to tracking deliveries, a system will be built that analyzes the status of deliveries in real time and optimizes delivery routes and methods based on the results. For example, it will take moisture-proofing measures if the humidity is high. This will allow the status of deliveries to be monitored in real time.

[0070] The emotion estimation unit can suggest how the delivery person should respond depending on the customer's emotions. The emotion estimation unit, for example, analyzes the customer's emotional state and builds a system that automatically suggests how the delivery person should respond depending on the emotion score. For example, a polite response is suggested for customers with a high positive emotion score. An algorithm is developed based on the emotion estimation data to suggest how the delivery person should respond depending on the customer's emotions, and an automatic suggestion system is built based on that algorithm. For example, a way of greeting or speaking depending on the customer's emotional state is suggested. A system is developed that monitors the customer's emotional state in real time and dynamically suggests how the delivery person should respond based on the results. For example, the response method is immediately changed if the customer's emotional state changes. This makes it possible to suggest the optimal response method depending on the customer's emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The delivery person selection unit analyzes delivery people's past delivery histories to select the most efficient delivery people. For example, the delivery person selection unit collects delivery history data and uses AI to analyze it to evaluate delivery efficiency. The delivery history data is used to identify areas and time periods in which delivery people excel, and based on that information, the most suitable delivery person is selected. An algorithm is developed to analyze the delivery history data and evaluate delivery person performance. The delivery route generation unit generates the optimal delivery route for the delivery person selected by the delivery person selection unit. For example, AI analyzes delivery person location information, traffic conditions, and delivery item information in real time to generate the optimal delivery route. The delivery route generation unit assigns specific deliveries based on the delivery person's skills and special abilities. The delivery route generation unit utilizes the delivery person's communication skills to promote direct communication with customers. The health monitoring unit monitors the health of delivery people. For example, the health monitoring unit uses wearable devices to collect data such as heart rate, number of steps, and sleep time. Based on the health status data, an algorithm is developed to evaluate the delivery person's fatigue level and suggest delivery routes that minimize fatigue. A system is constructed that monitors health status in real time and dynamically adjusts delivery routes according to the health status. The emotion estimation unit analyzes the emotional state of the delivery person. For example, the emotion estimation unit collects facial expressions and voice data, and AI analyzes the data to calculate an emotion score. An algorithm is developed that evaluates the stress level of the delivery person based on the emotion estimation data and suggests a delivery route that minimizes stress. A system is constructed that monitors emotional status in real time and dynamically adjusts delivery routes according to the emotional status. As a result, the delivery system according to the embodiment enables efficient delivery that takes into account the health status and emotional status.

[0073] The delivery staff selection unit can analyze delivery history data and select the most efficient delivery staff based on indicators such as delivery time, delivery success rate, and customer satisfaction. For example, delivery history data is collected and AI analyzes the data to evaluate delivery efficiency. For example, the most efficient delivery staff is selected based on indicators such as delivery time, delivery success rate, and customer satisfaction. Delivery history data is used to identify areas and time periods in which delivery staff excel, and the most suitable delivery staff is selected based on that information. For example, delivery staff with extensive delivery experience in a specific area are selected preferentially. Delivery history data is analyzed and an algorithm developed to evaluate delivery staff performance. For example, the skills and efficiency of delivery staff are evaluated from past delivery history, and the most suitable delivery staff is selected. This maximizes delivery staff efficiency.

[0074] The health monitoring unit can collect data on the delivery person's heart rate, number of steps, and sleep time. For example, to monitor the delivery person's health condition, a wearable device can be used to collect data such as heart rate, number of steps, and sleep time. This can then be used to suggest delivery routes based on their health condition. An algorithm can be developed that evaluates the delivery person's fatigue level based on the health condition data and suggests delivery routes that minimize fatigue. For example, a route that avoids long delivery times can be suggested. A system can be built that monitors health condition in real time and dynamically adjusts delivery routes according to health condition. For example, if the delivery condition worsens, the delivery route can be shortened. This allows for detailed monitoring of the delivery person's health condition.

[0075] The emotion estimation unit can collect facial expression and voice data of delivery personnel and analyze their emotional state. For example, to analyze a delivery personnel's emotional state, facial expression and voice data can be collected, and AI can analyze the data to calculate an emotion score. This can then be used to suggest a delivery route with less stress. Based on the emotion estimation data, an algorithm can be developed to evaluate the stress level of delivery personnel and suggest a delivery route with less stress. For example, a route with less traffic congestion can be suggested. A system can be built that monitors the emotional state in real time and dynamically adjusts the delivery route according to the emotional state. For example, if the emotional state worsens, the delivery route can be changed. This allows for a detailed analysis of the delivery personnel's emotional state.

[0076] The delivery route generation unit can assign delivery of items according to the skills and special abilities of delivery personnel. For example, the skills and special abilities of delivery personnel are registered in a database, and specific delivery items are assigned based on that information. For example, assignment is made to delivery personnel who are skilled in delivering heavy packages or fragile items. A system is built to evaluate the skills and special abilities of delivery personnel, and specific delivery items are assigned based on the evaluation results. For example, specialized deliveries are assigned to delivery personnel with specific skills. A system is developed that evaluates the skills and special abilities of delivery personnel in real time, and dynamically assigns specific delivery items based on that information. For example, the optimal delivery item is assigned based on the skills of the delivery personnel. This makes it possible to make optimal deliveries based on the skills and special abilities of the delivery personnel.

[0077] The delivery route generation unit can utilize the communication skills of delivery personnel to promote direct communication with customers. For example, a system can be constructed that evaluates the communication skills of delivery personnel and promotes direct communication with customers based on the evaluation results. For example, customer service can be assigned to delivery personnel with high communication skills. A training program can be developed to improve the communication skills of delivery personnel, and direct communication with customers can be promoted through the program. An incentive system can be introduced to utilize the communication skills of delivery personnel and promote direct communication with customers. For example, rewards can be provided to delivery personnel with high customer satisfaction. This will improve communication with customers.

[0078] The emotion estimation unit can provide incentives according to the emotional state of delivery personnel, thereby improving their motivation. For example, a system can be constructed that analyzes the emotional state of delivery personnel and provides incentives according to their emotional score. For example, rewards can be provided to delivery personnel with high positive emotion scores. An incentive system can be introduced based on the emotion estimation data to improve delivery personnel's motivation. For example, special rewards can be provided to delivery personnel with high emotion scores. A system can be developed that monitors the emotional state of delivery personnel in real time and dynamically provides incentives according to their emotional state. For example, rewards can be provided immediately to delivery personnel with good emotional states. This improves the motivation of delivery personnel.

[0079] The delivery route generation unit can automatically suggest a packing method according to the characteristics of the delivery item. For example, we will build a system that collects data on the characteristics of delivery items, analyzes that data using AI, and automatically suggests the optimal packing method. For example, we will suggest appropriate packing methods for fragile items or items that require temperature control. We will develop an algorithm that suggests specialized packing methods according to the characteristics of the delivery item, and build an automatic suggestion system based on that algorithm. For example, we will suggest packing methods according to the size and weight of the delivery item. We will develop a system that analyzes the characteristic data of delivery items in real time and dynamically suggests the optimal packing method based on the results. For example, if the characteristics of the delivery item change, the packing method will be changed immediately. This will make it possible to suggest the optimal packing method according to the characteristics of the delivery item.

[0080] The delivery route generation unit can suggest delivery means based on the weight and size of the delivery item. For example, we will build a system in which data on the weight and size of delivery items is collected and AI analyzes that data to suggest the optimal delivery means. For example, we will suggest delivery means such as bicycles, motorbikes, and cars. We will develop an algorithm that suggests the optimal delivery means based on the weight and size of the delivery item, and build an automatic suggestion system based on that algorithm. For example, we will suggest cars for heavy items. We will develop a system that analyzes data on the weight and size of delivery items in real time and dynamically suggests the optimal delivery means based on the results. For example, we will instantly change the delivery means if the characteristics of the delivery item change. This will make it possible to suggest the optimal delivery means based on the weight and size of the delivery item.

[0081] The emotion estimation unit can attach a personalized message to a delivery that corresponds to the customer's emotion. For example, a system is constructed that analyzes the customer's emotional state and automatically generates a personalized message that corresponds to the emotion score. For example, a message of gratitude is attached to customers with a high positive emotion score. An algorithm is developed that suggests a personalized message that corresponds to the customer's emotion based on the emotion estimation data, and an automatic suggestion system is constructed based on that algorithm. For example, a message that corresponds to the customer's emotional state is attached. A system is developed that monitors the customer's emotional state in real time and dynamically generates a personalized message based on the results. For example, the message can be changed immediately if the customer's emotional state changes. This makes it possible to attach a personalized message that corresponds to the customer's emotion.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The delivery person selection unit analyzes the delivery history of delivery people and selects the most efficient delivery person. For example, delivery history data is collected and analyzed by AI to evaluate delivery efficiency. Using the delivery history data, the areas and time periods in which delivery people excel are identified, and the optimal delivery person is selected based on that information. Step 2: The delivery route generation unit generates the optimal delivery route for the delivery person selected by the delivery person selection unit. For example, the delivery route generation unit uses AI to analyze the delivery person's location information, traffic conditions, and delivery item information in real time to generate the optimal delivery route. The delivery route generation unit assigns specific delivery items to delivery people based on their skills and special abilities. Step 3: The health monitoring unit monitors the health of delivery personnel. For example, wearable devices can be used to collect data such as heart rate, number of steps, and sleep time. Based on the health data, an algorithm is developed to evaluate the fatigue level of delivery personnel and suggest delivery routes that minimize fatigue. Step 4: The emotion estimation unit analyzes the emotional state of the delivery person. For example, facial expressions and voice data are collected, and the AI ​​analyzes the data to calculate an emotion score. Based on the emotion estimation data, an algorithm is developed to evaluate the stress level of the delivery person and suggest a delivery route with less stress.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] 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.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0120] 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.

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

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The 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.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] 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.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 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 delivery person selection unit that analyzes the delivery history of delivery persons and selects the most efficient delivery person; a delivery route generation unit that generates an optimal delivery route for the delivery person selected by the delivery person selection unit; a health monitoring unit that monitors the health status of the delivery person; An emotion estimation unit that analyzes the emotional state of the delivery person. A system characterized by:

2. The delivery person selection unit Analyze delivery history data and select the most efficient delivery personnel based on delivery time, delivery success rate, and customer satisfaction metrics.

2. The system of claim 1.

3. The health monitoring unit: Collecting data on the delivery person's heart rate, steps, and sleep time 2. The system of claim 1.

4. The emotion estimation unit Collecting facial expression and voice data of the delivery person and analyzing the emotional state 2. The system of claim 1.

5. The delivery route generation unit Assigning deliveries according to the skills and specialties of said delivery personnel 2. The system of claim 1.

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