System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a request for redelivery; means for generating a user list of a surrounding area; means for selecting an agent candidate based on the user list; means for evaluating reliability of the agent candidate using a generation AI; means for transmitting a redelivery request based on a result of the evaluation; means for receiving information indicating completion of the redelivery; and means for notifying a user of redelivery completion information.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] With the increase in logistics services such as home delivery and online shopping, the problem of redelivery is becoming more serious. Redelivery increases costs for delivery companies, and in some cases, the temporary occupation of delivery boxes can cause inconvenience to other residents. Conventional systems have limited ways to reduce the wasted effort and costs associated with redelivery, so there is a need for an effective solution to these problems. [Means for solving the problem]

[0005] The present invention provides a system for improving the efficiency of redelivery. The system includes a means for accepting redelivery requests, a means for generating a list of users in the surrounding area, a means for selecting proxy candidates based on the user list, a means for evaluating the reliability of the proxy candidates using a generation AI, a means for sending a redelivery request based on the evaluation results, a means for receiving information that the redelivery has been completed, and a means for notifying the user of the redelivery completion information. This reduces the cost of redelivery and prevents the inappropriate occupation of delivery boxes. Furthermore, during the reliability evaluation process, the generation AI analyzes the proxy candidate's past behavioral history, attribute information, and ratings from other users to calculate a reliability score, thereby selecting a safe and reliable proxy. Furthermore, by collecting user feedback following the redelivery completion notification and reflecting it in the next reliability evaluation, the accuracy and reliability of the system can be continuously improved.

[0006] "Retry delivery" is the act of delivering the same item again after the first delivery attempt has failed.

[0007] The "means for accepting a redelivery request" refers to a function or device that allows the system to receive and register a redelivery request from a user.

[0008] The "means for generating a list of users in the surrounding area" is a function or device for creating a list of other users who live in the vicinity of the user who wishes to perform redelivery.

[0009] "Substitute candidates" refer to users in the surrounding area who can act as substitutes for redelivery.

[0010] The "means for selecting proxy candidates" refers to a function or device for selecting a suitable user who can act as a proxy for redelivery from a list of users in the surrounding area.

[0011] The means by which the generative AI evaluates "trustworthiness" are functions or devices that allow the generative AI to quantify the reliability of a candidate based on the candidate's past behavioral history, attribute information, and evaluations from other users.

[0012] The "means for sending a redelivery request" refers to a function or device for transmitting a redelivery request to the agent candidate who is evaluated as having the highest reliability.

[0013] The "means for receiving information on the completion of redelivery" refers to a function or device for receiving a report from the agent on the success or completion of redelivery.

[0014] The "means for notifying the user of redelivery completion information" refers to a function or device for notifying the original sender that the delivery has been safely delivered. [Brief explanation of the drawings]

[0015] [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. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention is a system for improving the efficiency of redelivery, and provides a method for users to request redelivery and entrust it to a trusted agent in their neighborhood. Below, we will explain in natural language how the program of this system processes. We will also explain with concrete examples.

[0037] 1. Accepting delivery requests and selecting agents

[0038] a. User Device:

[0039] The user launches the app and selects "Request redelivery."

[0040] Enter the necessary information (e.g., size of the delivery item, desired time period for receipt, etc.) and press the send button.

[0041] b. Server:

[0042] The delivery request information received from the user is analyzed.

[0043] Based on the content of the delivery request, a list of users in areas where redelivery is possible is generated. This list is obtained from a preset range (for example, within a radius of 500 meters).

[0044] 2. Evaluation of the reliability of the agent

[0045] a. Server:

[0046] A list of candidate agents and information about each agent (past behavioral history, evaluations, attributes, etc.) is sent to the generation AI.

[0047] b. Generation AI:

[0048] Each agent's past behavior history (e.g., number of past redeliveries, success rate), attribute information (e.g., proximity to residence, reliability), and ratings from other users are analyzed.

[0049] Calculate an overall reliability score and rate the agent. For example,

[0050] Candidate A: Reliability score 9.5

[0051] Candidate B: Reliability score 8.8

[0052] The agent with the highest reliability score is selected and the result is sent to the server.

[0053] 3. Confirmation and notification of proxy request

[0054] a. Server:

[0055] Send a redelivery request message to highly rated agents (e.g., to Agent A, "Please redeliver User B's package").

[0056] Wait for Agent A to accept the request and receive the result.

[0057] If Agent A accepts the request, a notification will be sent to User B stating that "Redelivery will be carried out by Agent A." If not accepted, the request will be sent to the next agent.

[0058] 4. Redelivery and completion report

[0059] a. Agent A (Agreeing Agent):

[0060] Upon receiving a request, we will go to the specified address and carry out the redelivery.

[0061] After completing delivery, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[0062] b. Server:

[0063] Upon receiving the delivery completion information, a completion notification is sent to User B stating that "the package has been delivered safely."

[0064] A message is sent to User B requesting them to evaluate Agent A and feedback is collected.

[0065] The feedback information is stored in a database and is reflected in the agent reliability evaluation from the next time onwards.

[0066] Specific examples

[0067] 1. Accepting delivery requests and selecting agents

[0068] a. User's device:

[0069] The message will say, "Your delivery has arrived and needs to be redelivered. Would you like to ask a neighbor to redeliver it?"

[0070] The user selects "Yes" and sends a redelivery request.

[0071] b. Server:

[0072] Collect information about users in the vicinity and create a list of multiple proxy candidates.

[0073] Information about these candidates is sent to the generating AI and it is asked to evaluate their reliability.

[0074] 2. Evaluation of the reliability of the agent

[0075] a. Generation AI:

[0076] A candidate's past behavioral history (e.g., 10 redelivery requests received in the past, all of which were successful) is analyzed and a reliability score is assigned to each candidate.

[0077] Candidate A is given a high reliability score and selected as the optimal proxy.

[0078] 3. Confirmation and notification of proxy request

[0079] a. Server:

[0080] A request message is sent to Agent A saying, "Please redeliver User B."

[0081] If Agent A accepts the request, User B will receive a notification that "Redelivery will be carried out by Agent A."

[0082] 4. Redelivery and completion report

[0083] a. Agent A:

[0084] The driver heads to the delivery destination and safely delivers User B's package.

[0085] After the delivery is completed, a "redelivery completed" report is sent to the server.

[0086] b. Server:

[0087] Upon receiving the delivery completion report, the system sends a completion notification to User B stating "The package has been delivered."

[0088] A request for evaluation of agent A is sent to user B, and feedback is collected. This feedback will be used for future agent reliability evaluations.

[0089] The processing flow will be explained below.

[0090] Step 1:

[0091] On the user's device:

[0092] The user launches the app and selects "Request redelivery."

[0093] Enter the necessary information (size of the delivery item, desired time of receipt, etc.) and press the send button.

[0094] Step 2:

[0095] server:

[0096] The delivery request information received from the user is analyzed.

[0097] Obtain the address information of the user who wishes to have the item redelivered.

[0098] Step 3:

[0099] server:

[0100] Based on the contents of the delivery request, a list of users in the surrounding area within a certain range for whom redelivery is possible is generated.

[0101] Step 4:

[0102] server:

[0103] From a list of users in the surrounding area, users who can act as substitutes for redelivery are listed as substitute candidates.

[0104] Step 5:

[0105] server:

[0106] Information about the potential agent (past behavioral history, ratings from other users, attribute information, etc.) is sent to the generation AI.

[0107] Step 6:

[0108] Generation AI:

[0109] The past behavioral history, attribute information, and evaluations from other users of the proxy candidate are analyzed.

[0110] An overall reliability score is calculated for each candidate.

[0111] Step 7:

[0112] server:

[0113] Select the most trustworthy agent based on their reliability score.

[0114] A redelivery request message is sent to the selected agent.

[0115] Step 8:

[0116] Agent's device:

[0117] The agent receives the redelivery request message and chooses whether to accept the request.

[0118] When the agent presses the accept button, this information is sent to the server.

[0119] Step 9:

[0120] server:

[0121] Verify that the agent has accepted the redelivery request.

[0122] A notification is sent to the user stating that "redelivery will be carried out by a substitute."

[0123] Step 10:

[0124] Agent:

[0125] We will go to the specified address and redeliver the parcel.

[0126] Once the redelivery is complete, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[0127] Step 11:

[0128] server:

[0129] Upon receiving the delivery completion information, a completion notification is sent to the user stating that "the package has been delivered safely."

[0130] At the same time, a message requesting evaluation of the agent is sent to the user.

[0131] Step 12:

[0132] On the user's device:

[0133] Receive a review message for the redelivery and enter your feedback in the app and submit it.

[0134] Step 13:

[0135] server:

[0136] Save the feedback information from the user in a database.

[0137] The collected feedback information will be reflected in future reliability evaluations.

[0138] Example 1

[0139] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0140] Improving the efficiency of redelivery and enhancing user convenience requires the rapid and accurate processing of redelivery requests. However, in current redelivery systems, managing redelivery requests is cumbersome, and selecting a proxy and evaluating their reliability often requires time and resources. Another issue is the lack of a means to collect feedback after redelivery is completed and reflect it in the next reliability evaluation.

[0141] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0142] In this invention, the server includes means for accepting redelivery requests, means for generating a list of users in the surrounding area, means for selecting agent candidates based on the user list, means for evaluating the reliability of the agent candidates using a generation AI, means for sending a redelivery request based on the evaluation results, means for receiving information that redelivery has been completed, means for notifying the user of redelivery completion information, means for sending information about the agent candidates to the generation AI, means for collecting detailed information about the agent candidates and calculating a reliability score, means for the agent to perform redelivery and send completion information, and means for sending a request to the next-ranked agent based on the evaluation. This enables fast and efficient processing of redelivery requests, reliable redelivery, and collection of user feedback to be reflected in the next reliability evaluation.

[0143] "Means for accepting redelivery requests" refers to the process of receiving and recording redelivery requests in a database when a user requests redelivery through the application.

[0144] "Means for generating a list of users in the surrounding area" refers to a process of extracting and listing other users within a certain range based on the user's location information.

[0145] "Means for selecting proxy candidates" refers to the process of selecting candidates who can carry out redelivery from the generated user list.

[0146] "Means for evaluating the reliability of proxy candidates using generative AI" refers to the process by which generative AI quantifies and evaluates the reliability of each proxy candidate based on data such as the proxy candidate's past behavioral history, attribute information, and evaluations from other users.

[0147] "Means for sending a redelivery request" refers to a process for sending a redelivery request message to the selected agent candidate.

[0148] "Means for receiving information that redelivery has been completed" refers to the process by which the server receives completion information when the agent has completed redelivery.

[0149] "Means for notifying the user of redelivery completion information" refers to the process of notifying the user that redelivery has been successfully completed.

[0150] "Means for sending information about candidate agents to the generation AI" refers to the process by which the server sends detailed information about candidate agents to the generation AI and requests an evaluation.

[0151] "Means for collecting detailed information on proxy candidates and calculating reliability scores" refers to the process by which the generation AI analyzes various data on proxy candidates and calculates reliability scores.

[0152] "Means for an agent to redeliver and send completion information" refers to the process in which a selected agent redelivers the parcel and sends the completion information to the server.

[0153] "Means for sending the request to the next highest ranking agent based on the rating" refers to the process of re-sending the request to the agent with the next highest reliability score if the first agent does not accept the request.

[0154] This invention is a system for improving the efficiency of redelivery, and provides a method for users to request redelivery and entrust it to a trusted agent in their neighborhood. The program processing of this system will be explained in detail below.

[0155] System Overview

[0156] Hardware or software used

[0157] User device: A mobile device such as a smartphone or tablet used to request redelivery. A dedicated application is installed on the device.

[0158] Server: Receives redelivery requests from users, selects proxy candidates, and evaluates their reliability. A cloud server or on-premise server is used.

[0159] Generative AI model: An AI model for assessing the trustworthiness of potential agents, using algorithms such as random forests or neural networks.

[0160] System Operation

[0161] User's device

[0162] When a user launches the dedicated app, a "Request Redelivery" button will appear on the home screen. The user taps this button, enters the necessary information such as the size of the delivery item and the desired time of receipt, and then presses the "Send" button. This operation sends the entered data to the server.

[0163] server

[0164] The server receives delivery request information sent from the user's device and first analyzes the information. Next, it extracts other users within a certain range (for example, within a 500-meter radius) based on the user's location information and generates a list of users in the surrounding area. This list is used to select proxy candidates.

[0165] The server sends the list of proxy candidates and their detailed information (past behavioral history, ratings, attribute information, etc.) to the generative AI model, which then calculates a reliability score based on that information. The generative AI model then uses a machine learning algorithm to calculate a reliability score for each proxy and returns it to the server. For example, candidate A might be given a reliability score of 9.5, and candidate B might be given a reliability score of 8.8.

[0166] The server sends a redelivery request message to the agent with the highest reliability score. If the agent accepts the request, the server sends a notification to the user saying "Redelivery will be performed by the agent." If the agent does not accept, the server sends the request again to the next agent.

[0167] Agent

[0168] The selected agent will receive the redelivery request, go to the specified address, and deliver the package. After completing the delivery, the agent will press the "Redelivery Complete" button on the dedicated app and send the completion information to the server.

[0169] server

[0170] The server receives delivery completion information from the agent and sends a completion notification to the user saying "The package has been delivered safely." It also sends a message to the user requesting an evaluation of the agent and collects feedback. The collected feedback information is stored in a database and used for the next reliability evaluation.

[0171] Specific examples

[0172] For example, if a user selects "Yes" to the message "Your delivery has arrived and requires redelivery. Would you like to request a neighbor to redeliver it?" and sends a redelivery request, the server collects user information from the surrounding area and creates a list of multiple proxy candidates. It then sends information about the candidates to the generation AI and requests it to evaluate their reliability.

[0173] Prompt Sentence Examples

[0174] "How can we build an AI model that receives redelivery requests, evaluates the reliability of agents, and selects the best agent?"

[0175] By inputting this prompt into a generative AI model, specific construction steps and optimization methods can be obtained.

[0176] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0177] Step 1:

[0178] The user submits a redelivery request

[0179] The user launches a dedicated application on their device and taps the "Request Redelivery" button displayed on the home screen. The user then enters details of the delivery (size, desired time of receipt, etc.) and presses the "Send" button. This operation sends the input data to the server.

[0180] Input: Shipment details entered by the user

[0181] Output: Redelivery request data sent to the server

[0182] Step 2:

[0183] The server analyzes the delivery request

[0184] The server receives the redelivery request data sent from the user's device. It analyzes the received data and extracts information such as the user's address and desired time period. It then uses a geo-distance algorithm to extract other users within a certain range (for example, within a 500-meter radius) of the user's address and generates a user list.

[0185] Input: Redelivery request data

[0186] Output: List of users in the surrounding area

[0187] Step 3:

[0188] The server selects a proxy candidate.

[0189] The server selects candidates for redelivery agents from the generated user list, while also acquiring past behavioral history and evaluation data, and sends this information to the generating AI model.

[0190] Input: Local area user list, candidate behavior history and evaluation data

[0191] Output: Candidate proxy data sent to the generative AI model

[0192] Step 4:

[0193] Generative AI evaluates potential substitutes

[0194] The generative AI model receives the data sent from the server and calculates a reliability score for each agent. It analyzes the data using a machine learning algorithm (e.g., random forest, neural network), and assigns a reliability score to each agent. For example, it assigns a score of 9.5 to candidate A and 8.8 to candidate B. It then sends the reliability evaluation results back to the server.

[0195] Input: Delegate candidate data

[0196] Output: Reliability evaluation results

[0197] Step 5:

[0198] The server sends a redelivery request message

[0199] The server receives the reliability evaluation results returned from the generative AI model and selects the agent with the highest reliability score. It then sends a redelivery request message to the selected agent, stating, "Please redeliver the user's package." At the same time, it waits until the agent accepts the request.

[0200] Input: Reliability evaluation result

[0201] Output: Redelivery request message sent to agent

[0202] Step 6:

[0203] The server notifies the user

[0204] If the agent accepts the redelivery request, the server sends a notification to the user saying, "Redelivery will be performed by the agent." If the agent does not accept the request, the server sends a request message to the next agent.

[0205] Input: Agent consent information

[0206] Output: Notification message sent to the user

[0207] Step 7:

[0208] A substitute will redeliver the item

[0209] The selected agent will receive the redelivery request, go to the specified address and deliver the item. After completing the delivery, the agent will press the "Redelivery Complete" button on the dedicated app and send the delivery completion information to the server.

[0210] Input: Redelivery request message

[0211] Output: Delivery completion information sent to the server

[0212] Step 8:

[0213] The server processes the redelivery completion information and feedback

[0214] The server receives delivery completion information from the agent and sends a completion notification to the user saying "The package has been delivered safely." At the same time, it sends a message to the user requesting an evaluation of the agent and collects feedback. The collected feedback information is stored in a database and will be used for the next agent reliability evaluation.

[0215] Input: Delivery completion information, user feedback

[0216] Output: A completion notification and a rating request message sent to the user, along with feedback information used for the next trust rating.

[0217] (Application example 1)

[0218] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0219] Improving the efficiency and reliability of redelivery is an important issue in the logistics industry. Currently, when a redelivery request is made, processing it takes time and costs money, which is a factor that reduces customer satisfaction. This problem is particularly pronounced in fields such as food delivery, where prompt redelivery is required. Therefore, this invention aims to provide a system that performs redelivery quickly and reliably, thereby improving customer satisfaction.

[0220] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0221] In this invention, the server includes means for accepting a redelivery request, means for generating a list of users in the surrounding area, means for selecting proxy candidates based on the user list, means for evaluating the reliability of the proxy candidates using a generative AI, means for sending a redelivery request based on the evaluation results, means for receiving information that redelivery has been completed, means for notifying the user of the redelivery completion information, means for selecting proxy candidates from a list of delivery partners, and means for using a generative AI model to select the most suitable proxy candidate, thereby enabling faster and more efficient redelivery.

[0222] The "means for accepting a redelivery request" is an interface that receives a redelivery request from a user and registers that information in the system.

[0223] The "means for generating a list of users in the surrounding area" is a function for collecting user information within a specified area and compiling it into a list.

[0224] The "means for selecting a proxy candidate" is an algorithm or function that selects an appropriate proxy from the list of users who can be redelivered.

[0225] "Means of evaluating trustworthiness using generative AI" refers to the process of using generative AI to calculate a trustworthiness score based on the candidate agent's past performance and evaluations.

[0226] The "means for sending a redelivery request" is a function for sending a request for redelivery to the agent selected based on the evaluation results.

[0227] "Means for receiving information that redelivery has been completed" refers to a system for receiving a report that the redelivery process has been completed.

[0228] The "means for notifying the user of redelivery completion information" is a notification function that notifies the user who requested redelivery that redelivery has been completed.

[0229] "Means for selecting a candidate surrogate from a list of delivery partners" refers to a method for selecting an appropriate surrogate from a list of delivery partners who fulfill specific roles such as food delivery.

[0230] "Means of using a generative AI model to select the most suitable proxy candidate" refers to a technology that uses a generative AI model to select the most reliable proxy candidate from multiple proxy candidates.

[0231] This invention is a system that improves the efficiency and reliability of redelivery. The system provides a way for users to request redelivery and entrust it to a trusted agent in their neighborhood. Specifically, it is realized by combining a smartphone application and a cloud server.

[0232] First, a redelivery request is made from the user's device (such as a smartphone). The app has an interface that accepts redelivery requests, and the user enters and submits the necessary information (desired redelivery time, current location, etc.).

[0233] The server analyzes the received redelivery request information and generates a list of users in the surrounding area. The user list is created by collecting user information in a specified area (for example, within a 500-meter radius).

[0234] Next, the server uses a generative AI model to select the most suitable agent from the list of agent candidates. The reliability of each agent is evaluated based on their past behavioral history, attribute information, and ratings from other users. A reliability score is calculated and the most suitable agent is selected. This process is carried out using generative AI.

[0235] After the most suitable agent is selected, the server sends a redelivery request to that agent. If the selected agent accepts the request, that information is sent to the user's terminal.

[0236] Once redelivery is complete, the agent presses the "Redelivery Complete" button on the smartphone application to send delivery completion information to the server. The server receives this information and notifies the user.

[0237] The server then collects user feedback, which is stored in a database and used for future trust assessments.

[0238] For example, if a customer misses a meal they ordered via food delivery, this system can quickly arrange for a redelivery. When the customer requests a redelivery, the generative AI model selects the most suitable delivery partner and redelivers the food. After the user has received the meal, they can provide feedback, making the redelivery process more efficient and reliable in the future.

[0239] Here are some examples of prompts:

[0240] "The customer would like to redeliver the curry. Please select a reliable delivery partner and request a redelivery."

[0241] In this way, the redelivery system can perform redelivery efficiently and quickly.

[0242] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0243] Step 1:

[0244] Redelivery request from user device

[0245] The user requests redelivery using a smartphone app. They enter the necessary information (desired redelivery time, current location, etc.) into the app interface and press the send button. The input data is sent to the server.

[0246] Step 2:

[0247] Generate a list of users in the surrounding area on the server

[0248] The server analyzes the received redelivery request information, collects user information within the specified area (for example, within a 500-meter radius), and generates a list based on past user data retrieved from a database.

[0249] Step 3:

[0250] Evaluating the reliability of proxy candidates using generative AI

[0251] The server sends the created list of users in the surrounding area to the generation AI. The generation AI analyzes each proxy candidate's past behavior history, attribute information, and ratings from other users to calculate a reliability score. Based on this, the optimal proxy candidate is selected.

[0252] Step 4:

[0253] Sending a redelivery request

[0254] The server sends a redelivery request to the most suitable proxy candidate based on the reliability score calculated by the generation AI, waits for the candidate to accept the request, and checks the result.

[0255] Step 5:

[0256] Receiving redelivery completion information

[0257] The agent redelivers the parcel and, once the redelivery is complete, presses the "Redelivery Complete" button on the app. This sends delivery completion information to the server, which then analyzes the received completion information.

[0258] Step 6:

[0259] Notification of redelivery completion to the user

[0260] The server then notifies the user device that redelivery has been completed, and the user can confirm this via the smartphone app.

[0261] Step 7:

[0262] Gathering feedback

[0263] After the redelivery is completed, the server sends a message to the user requesting feedback. When the user enters and submits feedback, the information is collected by the server. The server stores this feedback information in a database and uses it to calculate the reliability score from the next time onwards.

[0264] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0265] This invention provides a system that combines a new emotion engine that recognizes the user's emotions in order to improve the efficiency of redelivery. Below, we will explain in natural language how the program of this system works, and also provide specific examples.

[0266] 1. Accepting delivery requests and selecting agents

[0267] a. User Device:

[0268] The user launches the app and selects "Request redelivery."

[0269] Enter the necessary information (e.g., size of the delivery item, desired time period for receipt, etc.) and press the send button.

[0270] b. Server:

[0271] The delivery request information received from the user is analyzed.

[0272] Obtain the address information of the user who wishes to have the item redelivered.

[0273] c. Server:

[0274] Based on the content of the delivery request, a list of users in areas where redelivery is possible is generated. This list is obtained from a preset range (for example, within a radius of 500 meters).

[0275] 2. Evaluation of the reliability of the agent

[0276] a. Server:

[0277] From a list of users in the surrounding area, users who can act as substitutes for redelivery are listed as substitute candidates.

[0278] b. Server:

[0279] Information about the potential agent (past behavioral history, ratings from other users, attribute information, etc.) is sent to the generation AI.

[0280] c. Generation AI:

[0281] Each agent's past behavior history, attribute information, and evaluations from other users are analyzed.

[0282] An overall reliability score is calculated for each candidate.

[0283] 3. Emotional engine for recognizing user emotions

[0284] a. User Device:

[0285] When a user requests redelivery, they use text input or voice input.

[0286] b. Emotion engine (on the server):

[0287] It analyzes the user's text or voice input to determine their emotional state, for example, recognizing that the user is frustrated from the text "This package is delayed and I'm having trouble."

[0288] c. Server:

[0289] Based on the user's perceived emotional state, the system adaptively adjusts redelivery requests, for example by prioritizing a more reliable agent for a particularly dissatisfied user.

[0290] 4. Confirmation and notification of proxy request

[0291] a. Server:

[0292] Send a redelivery request message to highly rated agents (e.g., to Agent A, "Please redeliver User B's package").

[0293] Wait for the agent to accept the request and receive the result.

[0294] b. Server:

[0295] If the agent accepts the request, a notification is sent to the user stating "Redelivery will be carried out by the agent." If not accepted, the request is sent to the next agent.

[0296] 5. Redelivery and completion report

[0297] a. Agent (Agreement Agent):

[0298] Upon receiving a request, we will go to the specified address and carry out the redelivery.

[0299] Once the redelivery is complete, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[0300] b. Server:

[0301] Upon receiving the delivery completion information, a completion notification is sent to the user stating that "the package has been delivered safely."

[0302] At the same time, a message requesting evaluation of the agent is sent to the user to collect feedback.

[0303] Specific examples

[0304] 1. Accepting delivery requests and selecting agents

[0305] a. User's device:

[0306] The message will say, "Your delivery has arrived and needs to be redelivered. Would you like to ask a neighbor to redeliver it?"

[0307] The user selects "Yes" and sends a redelivery request.

[0308] b. Server:

[0309] Collect information about nearby users and create a list of multiple proxy candidates. Send information about these candidates to the generation AI and request a reliability evaluation.

[0310] 2. Evaluation of the reliability of the agent

[0311] a. Generation AI:

[0312] A candidate's past behavioral history (e.g., 10 redelivery requests received in the past, all of which were successful) is analyzed and a reliability score is assigned to each candidate.

[0313] Candidates are given a high reliability score and selected as the best proxy.

[0314] 3. Emotional engine for recognizing user emotions

[0315] a. User Device:

[0316] The emotion engine analyzes the text entered by the user, "My package is delayed and I'm having a lot of trouble."

[0317] b. Emotion Engine:

[0318] The emotional state of the user is determined to be distressed, and the information is transmitted to the server.

[0319] c. Server:

[0320] Based on information from the emotion engine, the system prioritizes the selection of particularly reliable agents and coordinates redelivery requests.

[0321] 4. Confirmation and notification of proxy request

[0322] a. Server:

[0323] A request message "Please redeliver the user's package" is sent to the agent.

[0324] If accepted, the user will receive a notification that "redelivery will be carried out by a proxy."

[0325] 5. Redelivery and completion report

[0326] a. Agent:

[0327] Head to the delivery destination and deliver the user's package safely.

[0328] After the delivery is completed, a "redelivery completed" report is sent to the server.

[0329] b. Server:

[0330] Upon receiving a report that delivery has been completed, the system sends a completion notification to the user stating that "your package has been delivered."

[0331] A request for evaluation of the agent is sent to the user, and feedback is collected. This feedback is used for future evaluation of the agent's reliability.

[0332] The processing flow will be explained below.

[0333] Step 1:

[0334] On the user's device:

[0335] The user launches the app and selects "Request redelivery."

[0336] Enter the necessary information (size of the delivery item, desired time of receipt, etc.) and press the send button.

[0337] Step 2:

[0338] server:

[0339] Redelivery request information sent from the user is received.

[0340] Verify the user's address and shipping details.

[0341] Step 3:

[0342] server:

[0343] Based on the user's redelivery request, a list of users in the surrounding area within the specified range is generated. For example, information on users within a 500-meter radius of the address of the user requesting redelivery is obtained.

[0344] Step 4:

[0345] server:

[0346] From a list of users in the surrounding area, users who can act as substitutes for redelivery are listed as substitute candidates.

[0347] Step 5:

[0348] server:

[0349] Information about the potential agent (past behavioral history, ratings from other users, attribute information, etc.) is sent to the generation AI.

[0350] Step 6:

[0351] Generation AI:

[0352] The past behavioral history of the agent candidate (e.g., number of past redelivery attempts and success rate), attribute information (e.g., proximity to residence), and ratings from other users are analyzed.

[0353] An overall reliability score is calculated for each candidate.

[0354] Step 7:

[0355] server:

[0356] Select the most trustworthy agent based on their reliability score.

[0357] Step 8:

[0358] On the user's device:

[0359] When a user requests redelivery, they use text input or voice input.

[0360] Step 9:

[0361] Emotion engine (on the server):

[0362] It analyzes the user's text or voice input to determine their emotional state, for example, recognizing that the user is frustrated from the text "This package is delayed and I'm having trouble."

[0363] Step 10:

[0364] server:

[0365] Based on the user's perceived emotional state, the system adaptively adjusts redelivery requests, for example by prioritizing a more reliable agent for a particularly dissatisfied user.

[0366] Step 11:

[0367] server:

[0368] Send a redelivery request message to highly rated agents (e.g., to Agent A, "Please redeliver User B's package").

[0369] Step 12:

[0370] Agent's device:

[0371] The agent receives the redelivery request message and chooses whether to accept the request.

[0372] When the agent presses the accept button, this information is sent to the server.

[0373] Step 13:

[0374] server:

[0375] Verify that the agent has accepted the redelivery request.

[0376] Send a notification to the user saying "Redelivery will be carried out by a substitute."

[0377] Step 14:

[0378] Agent:

[0379] We will go to the specified address and redeliver the parcel.

[0380] Once the redelivery is complete, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[0381] Step 15:

[0382] server:

[0383] Upon receiving the delivery completion information, a completion notification is sent to the user stating that "the package has been delivered safely."

[0384] At the same time, a message requesting evaluation of the agent is sent to the user to collect feedback.

[0385] Step 16:

[0386] On the user's device:

[0387] Receive a review message for the redelivery and enter your feedback in the app and submit it.

[0388] Step 17:

[0389] server:

[0390] Save the feedback information from the user in a database.

[0391] The collected feedback information will be reflected in future reliability evaluations.

[0392] Example 2

[0393] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0394] Conventional redelivery systems did not efficiently process users' redelivery requests, and it was difficult to respond flexibly, taking into account the user's feelings and the urgency of the redelivery. Furthermore, there was insufficient evaluation of the reliability of the agent selection process, so the quality of redelivery was often not guaranteed. This led to problems such as lower user satisfaction and a loss of redelivery efficiency.

[0395] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0396] In this invention, the server includes a means for accepting redelivery requests, a means for generating a list of users in the surrounding area, a means for selecting agent candidates based on the user list, a means for evaluating the reliability of the agent candidates using a generation AI, a means for analyzing the user's emotional state and adjusting the content of the redelivery request, a means for sending a redelivery request based on the evaluation results and the user's emotional state, a means for receiving information that the redelivery has been completed, and a means for notifying the user of the redelivery completion information. This allows redelivery requests to be processed efficiently and quickly, and enables flexible responses based on the user's emotions. Furthermore, selecting a highly reliable agent improves the quality of redelivery and user satisfaction.

[0397] "Means for accepting redelivery requests" refers to a function that allows a user to input the necessary information when requesting redelivery and send it to the system.

[0398] "Means for generating a list of users in the surrounding area" refers to a function for collecting user information within an area where redelivery is possible and compiling it in list form.

[0399] "Means for selecting candidate agents" refers to a function for extracting people who can act as agents for redelivery from a list of users in the surrounding area and listing them as candidates.

[0400] "Means for evaluating trustworthiness using generative AI" refers to a function that uses generative AI to analyze the past behavioral history, attribute information, and evaluations from other users of a potential agent and calculate a trustworthiness score.

[0401] "Means for analyzing the user's emotional state and adjusting the content of the redelivery request" refers to a function for analyzing the user's emotional state from the content entered by the user and adaptively adjusting the priority and arrangement content of the redelivery request based on that information.

[0402] "Means for sending a redelivery request based on the evaluation results and emotional state" refers to a function for sending a redelivery request message to a selected agent based on the results of the reliability evaluation and emotional analysis.

[0403] "Means for receiving information on the completion of redelivery" refers to the function for reporting and receiving information to the system when an agent completes redelivery.

[0404] "Means for notifying the user of redelivery completion information" refers to a function for notifying the user of the completion information when redelivery is completed.

[0405] This invention is directed to a system that combines a new emotion engine that recognizes user emotions to improve the efficiency of redelivery. This system is implemented using the following hardware and software:

[0406] Server: AWS (Amazon Web Services) is used. The server receives redelivery request information from users, analyzes it, and generates a list of nearby users.

[0407] Generative AI model: OpenAI GPT-4 is used. Generative AI is used to evaluate the trustworthiness of potential agents.

[0408] Emotion engine: Using IBM Watson Natural Language Understanding, the emotion engine analyzes the emotional state of the user's input and provides that information to the server.

[0409] User device: Uses an iOS or Android device. The user device provides an interface for inputting and confirming redelivery requests, inputting emotional states, etc.

[0410] Specific examples of implementation

[0411] 1. Accepting delivery requests:

[0412] User device: The user launches the app and selects the redelivery request option. The user then enters the details of the redelivery request (size of the item, desired time of receipt, etc.) and presses the send button.

[0413] 2. Analyzing delivery requests and listing potential agents:

[0414] Server: Receives the information sent by the user and records it in a database. The server generates a list of users in areas where redelivery is possible based on the user's address information.

[0415] 3. Agent Credibility Assessment:

[0416] Server: Extracts proxy candidates from the generated user list and collects their profile information. Sends the collected information to the generation AI and requests a reliability evaluation.

[0417] Generative AI: Calculates a reliability score for each agent, based on, for example, the number of successful redeliveries in the past and the overall score of user ratings.

[0418] 4. Emotion engine recognizes user emotions:

[0419] User terminal: The user uses text or voice to input a redelivery request.

[0420] Emotion engine: Analyzes input text or voice and determines the user's emotional state (e.g., dissatisfied, satisfied, confused, etc.). Provides the analysis results to the server.

[0421] 5. Submission and notification of proxy requests:

[0422] Server: Based on the results of the reliability evaluation and sentiment analysis, the server sends a redelivery request message to the selected agent. The server notifies the user that the redelivery will be carried out by the agent.

[0423] 6. Redelivery and Completion Report:

[0424] Agent: Receives the request and carries out redelivery. Once redelivery is complete, presses the "Redelivery Completed" button to report to the server.

[0425] Server: Receives delivery completion information and sends a completion notification to the user. At the same time, it sends a message to the user requesting a rating of the agent and collects feedback.

[0426] Prompt sentences in specific examples

[0427] Below are some example prompts to input to a generative AI model:

[0428] "Nearby user list:

[0429] 1. User A - Past behavior history: All 10 redelivery requests were successful

[0430] 2. User B - Past behavior history: 5 successful redelivery requests out of 8

[0431] 3. User C - Past behavior history: 14 successful redelivery requests out of 15

[0432] Based on these candidates, calculate a credibility score for each."

[0433] In this way, the system efficiently processes users' redelivery requests and uses emotion recognition technology to select the most suitable agent, improving redelivery efficiency and user satisfaction.

[0434] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0435] Step 1:

[0436] User device: The user launches the app and selects the redelivery request option. The user then enters the details of the redelivery request (size of the item, desired time of receipt, etc.) and presses the send button.

[0437] Input: User's delivery details and desired time slot for receipt.

[0438] Output: User's redelivery request information.

[0439] Step 2:

[0440] Server: Receives delivery request information sent by users and records it in a database. Based on address information, it generates a list of users in an area where redelivery is possible (for example, within a 500-meter radius).

[0441] Input: User's redelivery request information, user's address information.

[0442] Output: A list of users in the surrounding area.

[0443] Step 3:

[0444] Server: Extracts proxy candidates from the generated user list and collects each candidate's profile information (past behavioral history, user ratings, attribute information, etc.). This information is sent to the generation AI and a reliability evaluation is requested.

[0445] Input: A list of patrons in the surrounding area.

[0446] Output: Profile information of potential agents.

[0447] Step 4:

[0448] Generative AI model: Analyzes the profile information of each agent candidate and calculates a reliability score.

[0449] Input: The profile information of the potential agent.

[0450] Output: A confidence score.

[0451] Step 5:

[0452] User terminal: The user inputs a redelivery request using text or voice. This input is sent to the emotion engine.

[0453] Input: User text or voice input.

[0454] Output: User sentiment analysis information.

[0455] Step 6:

[0456] Emotion engine: Analyzes input text or voice to determine the user's emotional state and provides this information to the server.

[0457] Input: User text or voice input.

[0458] Output: User's emotional state information.

[0459] Step 7:

[0460] Server: Adjusts the redelivery request based on the reliability score and emotional state, then sends the redelivery request message to the agent with the highest rating.

[0461] Input: Confidence score, user emotional state information.

[0462] Output: Adjusted redelivery request details and redelivery request message.

[0463] Step 8:

[0464] Server: Waits for information from the agent about whether the redelivery request has been accepted or rejected, and receives the result.

[0465] Input: Redelivery request message.

[0466] Output: The agent's acceptance or rejection information.

[0467] Step 9:

[0468] Server: If the agent accepts the redelivery request, it sends a notification to the user saying "Redelivery will be carried out by the agent." If not accepted, it sends the request to the next agent.

[0469] Input: Agent acceptance or denial information.

[0470] Output: Redelivery notification, message to resubmit to next agent.

[0471] Step 10:

[0472] Agent: Receives the request and carries out redelivery. Once redelivery is complete, presses the "Redelivery Completed" button to report to the server.

[0473] Input: Redelivery request details.

[0474] Output: Redelivery completion information.

[0475] Step 11:

[0476] Server: Receives the redelivery completion information and sends a completion notification to the user stating that "the package has been delivered safely." It also sends a message to the user requesting a rating for the agent and collects feedback.

[0477] Input: Redelivery completion information.

[0478] Output: Redelivery completion notification, rating request message, feedback.

[0479] (Application example 2)

[0480] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0481] It has been reported that redelivery and delivery delays have a significant impact on user satisfaction in food delivery services. In particular, when users feel anxious or uncomfortable due to delivery delays or redelivery, their evaluation of the service declines. Therefore, there is a need for systems that can properly recognize users' emotions and respond in a way that is most appropriate for their situation. Current systems do not adequately take users' emotions into consideration, and improving the efficiency of redelivery and user satisfaction remains a challenge.

[0482] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0483] In this invention, the server includes means for accepting redelivery requests, means for generating a list of users in the surrounding area, means for selecting agent candidates based on the user list, means for evaluating the reliability of the agent candidates using a generation AI, means for sending a redelivery request based on the evaluation results, means for receiving information that redelivery has been completed, means for notifying the user of the redelivery completion information, and means for selecting a delivery partner based on the user's emotional state, which includes an emotion engine that recognizes the user's emotional state when placing an order. This makes it possible to select the most appropriate delivery partner taking the user's emotions into consideration.

[0484] The "means for accepting a request for redelivery" is a system function that accepts a request for redelivery when a user desires redelivery.

[0485] The "means for generating a list of users in the surrounding area" is a system function for creating a list of users who live in the surrounding area of ​​the requester when a redelivery request is made.

[0486] The "means for selecting proxy candidates" is a system function that selects candidates who can act as proxy redelivery agents based on the generated user list.

[0487] "Means for evaluating trustworthiness using generation AI" refers to a system function for using generation AI to evaluate the trustworthiness of selected proxy candidates.

[0488] The "means for sending a redelivery request" is a system function that sends a redelivery request to the selected agent based on the evaluation results.

[0489] "Means for receiving information that redelivery has been completed" refers to a function by which the system receives information when the agent has completed redelivery.

[0490] The "means for notifying the user of redelivery completion information" is a system function for notifying the user that redelivery has been completed.

[0491] An "emotion engine" is an engine that analyzes and recognizes the emotional state from text and voice input by the user.

[0492] "Means for selecting a delivery partner based on emotional state" is a function of the system that selects the most suitable delivery partner based on the emotional state recognized by the emotion engine.

[0493] This invention provides a system that uses an emotion engine to recognize a user's emotion and take appropriate action to improve the efficiency of redelivery. Specific embodiments of this system are described in detail below.

[0494] System Configuration

[0495] The system of the present invention is comprised of the following major components:

[0496] 1. Server:

[0497] The server accepts redelivery requests, generates a user list, selects proxy candidates, evaluates their reliability, sends redelivery requests, and receives and notifies completion information.

[0498] An emotion engine also runs on the server and analyzes the user's emotional state.

[0499] 2. User Device:

[0500] The user terminal provides an interface for inputting a redelivery request and also has a text or voice input function for inputting an emotional state.

[0501] 3. Delivery partner terminal:

[0502] It provides an interface for delivery partners to receive redelivery request messages and execute redelivery. It also has a reporting function when redelivery is completed.

[0503] Program processing overview

[0504] Below is a natural language explanation of how the system works.

[0505] 1. Redelivery request acceptance:

[0506] The user inputs a redelivery request from the terminal, and the server receives and analyzes this information.

[0507] 2. Generate a user list:

[0508] The server generates a list of users in the surrounding area who may be able to assist with redelivery requests.

[0509] 3. Selection of potential agents:

[0510] The server selects proxy candidates from the generated user list.

[0511] 4. Reliability assessment:

[0512] Using generative AI, a reliability score is calculated based on the candidate agent's past behavioral history, attribute information, and evaluations from other users.

[0513] 5. Emotion Recognition with Emotion Engine:

[0514] The emotion engine analyzes the text and voice entered by the user when requesting redelivery and recognizes the user's emotional state.

[0515] 6. Submitting a redelivery request:

[0516] Based on the perceived emotional state and trustworthiness assessment, a redelivery request is sent to the most suitable agent.

[0517] 7. Receiving and notifying completion information:

[0518] Once the redelivery is complete, the delivery partner terminal sends the completion information to the server, which notifies the user and collects further feedback.

[0519] Hardware and software used

[0520] Hardware: Smartphones, servers

[0521] Software: EmotionEngine (emotion recognition engine), DeliveryService (delivery service), generative AI model

[0522] Specific examples

[0523] For example, if a user sends a redelivery request through a smartphone app saying, "I'm frustrated because the pizza I ordered hasn't arrived yet," the emotion engine will recognize the user's emotional state as "frustrated." Based on this information, the server will select a particularly reliable agent who can quickly redeliver the delivery. After the redelivery is completed, feedback such as "I was very satisfied with the speed of the delivery and the service" is collected and reflected in the next reliability evaluation.

[0524] Prompt Sentence Examples

[0525] If someone enters "I'm frustrated because the pizza I ordered hasn't arrived yet," recognize the user's emotion as "frustrated."

[0526] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0527] Step 1:

[0528] The user inputs and submits a redelivery request.

[0529] Input: The user enters a message, including a redelivery request and sentiment, via text or voice.

[0530] Data processing: The user terminal sends the input data in digital form to the server.

[0531] Output: The server receives this data and processes it as redelivery request data.

[0532] Specific actions: The user opens the app, selects "Request redelivery," enters a message, and presses the send button.

[0533] Step 2:

[0534] The server generates a list of users in the surrounding area.

[0535] Input: The server receives the redelivery request data and the requester's address information.

[0536] Data processing: The server generates a list of other users who live within a certain range based on the requester's address.

[0537] Output: A list of users in the surrounding area is generated.

[0538] Specific behavior: Searches the address database on the server and lists users who live within 500 meters.

[0539] Step 3:

[0540] The server selects candidates for the proxy.

[0541] Input: Receives a server-generated user list.

[0542] Data processing: Select candidates who are likely to be able to act as redelivery agents from the user list.

[0543] Output: A list of potential agents is generated.

[0544] Specific operation: The system refers to the user's past delivery history and ratings and selects candidates for redelivery.

[0545] Step 4:

[0546] The server evaluates the reliability of the proxy candidate.

[0547] Input: The server receives a list of candidate agents and past behavioral history and evaluation information for each candidate.

[0548] Data processing: A reliability evaluation algorithm is run and the generating AI calculates a reliability score.

[0549] Output: The reliability score for each agent candidate is output.

[0550] Specific operation: The server has the generation AI calculate the reliability score and saves the result.

[0551] Step 5:

[0552] The server uses an emotion engine to recognize the user's emotional state.

[0553] Input: The server receives a redelivery request message from the user.

[0554] Data processing: The server uses the emotion engine to analyze the message and recognize the emotional state.

[0555] Output: Multiple emotion tags are output as the user's emotional state.

[0556] What it does: Send the text "I'm frustrated because my pizza order hasn't arrived yet" to the emotion engine, which will recognize it as "frustrated."

[0557] Step 6:

[0558] The server sends a redelivery request based on the emotional state and reliability score.

[0559] Input: Receives emotional state information and a confidence score for each agent.

[0560] Data processing: The server determines the most suitable agent based on the emotional state and reliability score, and sends a redelivery request to that agent.

[0561] Output: A redelivery request message is sent to the selected agent.

[0562] Specific operation: The server will prioritize redelivery requests from highly reliable agents for users who are feeling "irritated."

[0563] Step 7:

[0564] The agent completes the redelivery and sends the completion information to the server.

[0565] Input: After the agent completes the redelivery, he / she presses the "Redelivery Complete" button.

[0566] Data processing: The device sends the completion information to the server.

[0567] Output: Redelivery completion information is received by the server.

[0568] Specific operation: The agent will redeliver the package to the specified address and, once complete, press a button on the app to report completion.

[0569] Step 8:

[0570] The server notifies the user of the redelivery completion information and collects feedback.

[0571] Input: Receive redelivery completion information.

[0572] Data processing: The server sends a completion notification to the user and provides a feedback collection interface.

[0573] Output: A completion notification and a feedback collection request is sent to the user.

[0574] Specific operation: The server sends a notification to the user that the package has been delivered safely, along with a message requesting a rating for the agent.

[0575] 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.

[0576] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0577] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0578] [Second embodiment]

[0579] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0580] 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.

[0581] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0582] 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.

[0583] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0584] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0585] 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.

[0586] 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.

[0587] 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 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.

[0588] 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.

[0589] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0590] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0591] This invention is a system for improving the efficiency of redelivery, and provides a method for users to request redelivery and entrust it to a trusted agent in their neighborhood. Below, we will explain in natural language how the program of this system processes. We will also explain with concrete examples.

[0592] 1. Accepting delivery requests and selecting agents

[0593] a. User Device:

[0594] The user launches the app and selects "Request redelivery."

[0595] Enter the necessary information (e.g., size of the delivery item, desired time period for receipt, etc.) and press the send button.

[0596] b. Server:

[0597] The delivery request information received from the user is analyzed.

[0598] Based on the content of the delivery request, a list of users in areas where redelivery is possible is generated. This list is obtained from a preset range (for example, within a radius of 500 meters).

[0599] 2. Evaluation of the reliability of the agent

[0600] a. Server:

[0601] A list of candidate agents and information about each agent (past behavioral history, evaluations, attributes, etc.) is sent to the generation AI.

[0602] b. Generation AI:

[0603] Each agent's past behavior history (e.g., number of past redeliveries, success rate), attribute information (e.g., proximity to residence, reliability), and ratings from other users are analyzed.

[0604] Calculate an overall reliability score and rate the agent. For example,

[0605] Candidate A: Reliability score 9.5

[0606] Candidate B: Reliability score 8.8

[0607] The agent with the highest reliability score is selected and the result is sent to the server.

[0608] 3. Confirmation and notification of proxy request

[0609] a. Server:

[0610] Send a redelivery request message to highly rated agents (e.g., to Agent A, "Please redeliver User B's package").

[0611] Wait for Agent A to accept the request and receive the result.

[0612] If Agent A accepts the request, a notification will be sent to User B stating that "Redelivery will be carried out by Agent A." If not accepted, the request will be sent to the next agent.

[0613] 4. Redelivery and completion report

[0614] a. Agent A (Agreeing Agent):

[0615] Upon receiving a request, we will go to the specified address and carry out the redelivery.

[0616] After completing delivery, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[0617] b. Server:

[0618] Upon receiving the delivery completion information, a completion notification is sent to User B stating that "the package has been delivered safely."

[0619] A message is sent to User B requesting them to evaluate Agent A and feedback is collected.

[0620] The feedback information is stored in a database and is reflected in the agent reliability evaluation from the next time onwards.

[0621] Specific examples

[0622] 1. Accepting delivery requests and selecting agents

[0623] a. User's device:

[0624] The message will say, "Your delivery has arrived and needs to be redelivered. Would you like to ask a neighbor to redeliver it?"

[0625] The user selects "Yes" and sends a redelivery request.

[0626] b. Server:

[0627] Collect information about users in the vicinity and create a list of multiple proxy candidates.

[0628] Information about these candidates is sent to the generating AI and it is asked to evaluate their reliability.

[0629] 2. Evaluation of the reliability of the agent

[0630] a. Generation AI:

[0631] A candidate's past behavioral history (e.g., 10 redelivery requests received in the past, all of which were successful) is analyzed and a reliability score is assigned to each candidate.

[0632] Candidate A is given a high reliability score and selected as the optimal proxy.

[0633] 3. Confirmation and notification of proxy request

[0634] a. Server:

[0635] A request message is sent to Agent A saying, "Please redeliver User B."

[0636] If Agent A accepts the request, User B will receive a notification that "Redelivery will be carried out by Agent A."

[0637] 4. Redelivery and completion report

[0638] a. Agent A:

[0639] The driver heads to the delivery destination and safely delivers User B's package.

[0640] After the delivery is completed, a "redelivery completed" report is sent to the server.

[0641] b. Server:

[0642] Upon receiving the delivery completion report, the system sends a completion notification to User B stating "The package has been delivered."

[0643] A request for evaluation of agent A is sent to user B, and feedback is collected. This feedback will be used for future agent reliability evaluations.

[0644] The processing flow will be explained below.

[0645] Step 1:

[0646] On the user's device:

[0647] The user launches the app and selects "Request redelivery."

[0648] Enter the necessary information (size of the delivery item, desired time of receipt, etc.) and press the send button.

[0649] Step 2:

[0650] server:

[0651] The delivery request information received from the user is analyzed.

[0652] Obtain the address information of the user who wishes to have the item redelivered.

[0653] Step 3:

[0654] server:

[0655] Based on the contents of the delivery request, a list of users in the surrounding area within a certain range for whom redelivery is possible is generated.

[0656] Step 4:

[0657] server:

[0658] From a list of users in the surrounding area, users who can act as substitutes for redelivery are listed as substitute candidates.

[0659] Step 5:

[0660] server:

[0661] Information about the potential agent (past behavioral history, ratings from other users, attribute information, etc.) is sent to the generation AI.

[0662] Step 6:

[0663] Generation AI:

[0664] The past behavioral history, attribute information, and evaluations from other users of the proxy candidate are analyzed.

[0665] An overall reliability score is calculated for each candidate.

[0666] Step 7:

[0667] server:

[0668] Select the most trustworthy agent based on their reliability score.

[0669] A redelivery request message is sent to the selected agent.

[0670] Step 8:

[0671] Agent's device:

[0672] The agent receives the redelivery request message and chooses whether to accept the request.

[0673] When the agent presses the accept button, this information is sent to the server.

[0674] Step 9:

[0675] server:

[0676] Verify that the agent has accepted the redelivery request.

[0677] A notification is sent to the user stating that "redelivery will be carried out by a substitute."

[0678] Step 10:

[0679] Agent:

[0680] We will go to the specified address and redeliver the parcel.

[0681] Once the redelivery is complete, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[0682] Step 11:

[0683] server:

[0684] Upon receiving the delivery completion information, a completion notification is sent to the user stating that "the package has been delivered safely."

[0685] At the same time, a message requesting evaluation of the agent is sent to the user.

[0686] Step 12:

[0687] On the user's device:

[0688] Receive a review message for the redelivery and enter your feedback in the app and submit it.

[0689] Step 13:

[0690] server:

[0691] Save the feedback information from the user in a database.

[0692] The collected feedback information will be reflected in future reliability evaluations.

[0693] Example 1

[0694] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0695] Improving the efficiency of redelivery and enhancing user convenience requires the rapid and accurate processing of redelivery requests. However, in current redelivery systems, managing redelivery requests is cumbersome, and selecting a proxy and evaluating their reliability often requires time and resources. Another issue is the lack of a means to collect feedback after redelivery is completed and reflect it in the next reliability evaluation.

[0696] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0697] In this invention, the server includes means for accepting redelivery requests, means for generating a list of users in the surrounding area, means for selecting agent candidates based on the user list, means for evaluating the reliability of the agent candidates using a generation AI, means for sending a redelivery request based on the evaluation results, means for receiving information that redelivery has been completed, means for notifying the user of redelivery completion information, means for sending information about the agent candidates to the generation AI, means for collecting detailed information about the agent candidates and calculating a reliability score, means for the agent to perform redelivery and send completion information, and means for sending a request to the next-ranked agent based on the evaluation. This enables fast and efficient processing of redelivery requests, reliable redelivery, and collection of user feedback to be reflected in the next reliability evaluation.

[0698] "Means for accepting redelivery requests" refers to the process of receiving and recording redelivery requests in a database when a user requests redelivery through the application.

[0699] "Means for generating a list of users in the surrounding area" refers to a process of extracting and listing other users within a certain range based on the user's location information.

[0700] "Means for selecting proxy candidates" refers to the process of selecting candidates who can carry out redelivery from the generated user list.

[0701] "Means for evaluating the reliability of proxy candidates using generative AI" refers to the process by which generative AI quantifies and evaluates the reliability of each proxy candidate based on data such as the proxy candidate's past behavioral history, attribute information, and evaluations from other users.

[0702] "Means for sending a redelivery request" refers to a process for sending a redelivery request message to the selected agent candidate.

[0703] "Means for receiving information that redelivery has been completed" refers to the process by which the server receives completion information when the agent has completed redelivery.

[0704] "Means for notifying the user of redelivery completion information" refers to the process of notifying the user that redelivery has been successfully completed.

[0705] "Means for sending information about candidate agents to the generation AI" refers to the process by which the server sends detailed information about candidate agents to the generation AI and requests an evaluation.

[0706] "Means for collecting detailed information on proxy candidates and calculating reliability scores" refers to the process by which the generation AI analyzes various data on proxy candidates and calculates reliability scores.

[0707] "Means for an agent to redeliver and send completion information" refers to the process in which a selected agent redelivers the parcel and sends the completion information to the server.

[0708] "Means for sending the request to the next highest ranking agent based on the rating" refers to the process of re-sending the request to the agent with the next highest reliability score if the first agent does not accept the request.

[0709] This invention is a system for improving the efficiency of redelivery, and provides a method for users to request redelivery and entrust it to a trusted agent in their neighborhood. The program processing of this system will be explained in detail below.

[0710] System Overview

[0711] Hardware or software used

[0712] User device: A mobile device such as a smartphone or tablet used to request redelivery. A dedicated application is installed on the device.

[0713] Server: Receives redelivery requests from users, selects proxy candidates, and evaluates their reliability. A cloud server or on-premise server is used.

[0714] Generative AI model: An AI model for assessing the trustworthiness of potential agents, using algorithms such as random forests or neural networks.

[0715] System Operation

[0716] User's device

[0717] When a user launches the dedicated app, a "Request Redelivery" button will appear on the home screen. The user taps this button, enters the necessary information such as the size of the delivery item and the desired time of receipt, and then presses the "Send" button. This operation sends the entered data to the server.

[0718] server

[0719] The server receives delivery request information sent from the user's device and first analyzes the information. Next, it extracts other users within a certain range (for example, within a 500-meter radius) based on the user's location information and generates a list of users in the surrounding area. This list is used to select proxy candidates.

[0720] The server sends the list of proxy candidates and their detailed information (past behavioral history, ratings, attribute information, etc.) to the generative AI model, which then calculates a reliability score based on that information. The generative AI model then uses a machine learning algorithm to calculate a reliability score for each proxy and returns it to the server. For example, candidate A might be given a reliability score of 9.5, and candidate B might be given a reliability score of 8.8.

[0721] The server sends a redelivery request message to the agent with the highest reliability score. If the agent accepts the request, the server sends a notification to the user saying "Redelivery will be performed by the agent." If the agent does not accept, the server sends the request again to the next agent.

[0722] Agent

[0723] The selected agent will receive the redelivery request, go to the specified address, and deliver the package. After completing the delivery, the agent will press the "Redelivery Complete" button on the dedicated app and send the completion information to the server.

[0724] server

[0725] The server receives delivery completion information from the agent and sends a completion notification to the user saying "The package has been delivered safely." It also sends a message to the user requesting an evaluation of the agent and collects feedback. The collected feedback information is stored in a database and used for the next reliability evaluation.

[0726] Specific examples

[0727] For example, if a user selects "Yes" to the message "Your delivery has arrived and requires redelivery. Would you like to request a neighbor to redeliver it?" and sends a redelivery request, the server collects user information from the surrounding area and creates a list of multiple proxy candidates. It then sends information about the candidates to the generation AI and requests it to evaluate their reliability.

[0728] Prompt Sentence Examples

[0729] "How can we build an AI model that receives redelivery requests, evaluates the reliability of agents, and selects the best agent?"

[0730] By inputting this prompt into a generative AI model, specific construction steps and optimization methods can be obtained.

[0731] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0732] Step 1:

[0733] The user submits a redelivery request

[0734] The user launches a dedicated application on their device and taps the "Request Redelivery" button displayed on the home screen. The user then enters details of the delivery (size, desired time of receipt, etc.) and presses the "Send" button. This operation sends the input data to the server.

[0735] Input: Shipment details entered by the user

[0736] Output: Redelivery request data sent to the server

[0737] Step 2:

[0738] The server analyzes the delivery request

[0739] The server receives the redelivery request data sent from the user's device. It analyzes the received data and extracts information such as the user's address and desired time period. It then uses a geo-distance algorithm to extract other users within a certain range (for example, within a 500-meter radius) of the user's address and generates a user list.

[0740] Input: Redelivery request data

[0741] Output: List of users in the surrounding area

[0742] Step 3:

[0743] The server selects a proxy candidate.

[0744] The server selects candidates for redelivery agents from the generated user list, while also acquiring past behavioral history and evaluation data, and sends this information to the generating AI model.

[0745] Input: Local area user list, candidate behavior history and evaluation data

[0746] Output: Candidate proxy data sent to the generative AI model

[0747] Step 4:

[0748] Generative AI evaluates potential substitutes

[0749] The generative AI model receives the data sent from the server and calculates a reliability score for each agent. It analyzes the data using a machine learning algorithm (e.g., random forest, neural network), and assigns a reliability score to each agent. For example, it assigns a score of 9.5 to candidate A and 8.8 to candidate B. It then sends the reliability evaluation results back to the server.

[0750] Input: Delegate candidate data

[0751] Output: Reliability evaluation results

[0752] Step 5:

[0753] The server sends a redelivery request message

[0754] The server receives the reliability evaluation results returned from the generative AI model and selects the agent with the highest reliability score. It then sends a redelivery request message to the selected agent, stating, "Please redeliver the user's package." At the same time, it waits until the agent accepts the request.

[0755] Input: Reliability evaluation result

[0756] Output: Redelivery request message sent to agent

[0757] Step 6:

[0758] The server notifies the user

[0759] If the agent accepts the redelivery request, the server sends a notification to the user saying, "Redelivery will be performed by the agent." If the agent does not accept the request, the server sends a request message to the next agent.

[0760] Input: Agent consent information

[0761] Output: Notification message sent to the user

[0762] Step 7:

[0763] A substitute will redeliver the item

[0764] The selected agent will receive the redelivery request, go to the specified address and deliver the item. After completing the delivery, the agent will press the "Redelivery Complete" button on the dedicated app and send the delivery completion information to the server.

[0765] Input: Redelivery request message

[0766] Output: Delivery completion information sent to the server

[0767] Step 8:

[0768] The server processes the redelivery completion information and feedback

[0769] The server receives delivery completion information from the agent and sends a completion notification to the user saying "The package has been delivered safely." At the same time, it sends a message to the user requesting an evaluation of the agent and collects feedback. The collected feedback information is stored in a database and will be used for the next agent reliability evaluation.

[0770] Input: Delivery completion information, user feedback

[0771] Output: A completion notification and a rating request message sent to the user, along with feedback information used for the next trust rating.

[0772] (Application example 1)

[0773] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0774] Improving the efficiency and reliability of redelivery is an important issue in the logistics industry. Currently, when a redelivery request is made, processing it takes time and costs money, which is a factor that reduces customer satisfaction. This problem is particularly pronounced in fields such as food delivery, where prompt redelivery is required. Therefore, this invention aims to provide a system that performs redelivery quickly and reliably, thereby improving customer satisfaction.

[0775] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0776] In this invention, the server includes means for accepting a redelivery request, means for generating a list of users in the surrounding area, means for selecting proxy candidates based on the user list, means for evaluating the reliability of the proxy candidates using a generative AI, means for sending a redelivery request based on the evaluation results, means for receiving information that redelivery has been completed, means for notifying the user of the redelivery completion information, means for selecting proxy candidates from a list of delivery partners, and means for using a generative AI model to select the most suitable proxy candidate, thereby enabling faster and more efficient redelivery.

[0777] The "means for accepting a redelivery request" is an interface that receives a redelivery request from a user and registers that information in the system.

[0778] The "means for generating a list of users in the surrounding area" is a function for collecting user information within a specified area and compiling it into a list.

[0779] The "means for selecting a proxy candidate" is an algorithm or function that selects an appropriate proxy from the list of users who can be redelivered.

[0780] "Means of evaluating trustworthiness using generative AI" refers to the process of using generative AI to calculate a trustworthiness score based on the candidate agent's past performance and evaluations.

[0781] The "means for sending a redelivery request" is a function for sending a request for redelivery to the agent selected based on the evaluation results.

[0782] "Means for receiving information that redelivery has been completed" refers to a system for receiving a report that the redelivery process has been completed.

[0783] The "means for notifying the user of redelivery completion information" is a notification function that notifies the user who requested redelivery that redelivery has been completed.

[0784] "Means for selecting a candidate surrogate from a list of delivery partners" refers to a method for selecting an appropriate surrogate from a list of delivery partners who fulfill specific roles such as food delivery.

[0785] "Means of using a generative AI model to select the most suitable proxy candidate" refers to a technology that uses a generative AI model to select the most reliable proxy candidate from multiple proxy candidates.

[0786] This invention is a system that improves the efficiency and reliability of redelivery. The system provides a way for users to request redelivery and entrust it to a trusted agent in their neighborhood. Specifically, it is realized by combining a smartphone application and a cloud server.

[0787] First, a redelivery request is made from the user's device (such as a smartphone). The app has an interface that accepts redelivery requests, and the user enters and submits the necessary information (desired redelivery time, current location, etc.).

[0788] The server analyzes the received redelivery request information and generates a list of users in the surrounding area. The user list is created by collecting user information in a specified area (for example, within a 500-meter radius).

[0789] Next, the server uses a generative AI model to select the most suitable agent from the list of agent candidates. The reliability of each agent is evaluated based on their past behavioral history, attribute information, and ratings from other users. A reliability score is calculated and the most suitable agent is selected. This process is carried out using generative AI.

[0790] After the most suitable agent is selected, the server sends a redelivery request to that agent. If the selected agent accepts the request, that information is sent to the user's terminal.

[0791] Once redelivery is complete, the agent presses the "Redelivery Complete" button on the smartphone application to send delivery completion information to the server. The server receives this information and notifies the user.

[0792] The server then collects user feedback, which is stored in a database and used for future trust assessments.

[0793] For example, if a customer misses a meal they ordered via food delivery, this system can quickly arrange for a redelivery. When the customer requests a redelivery, the generative AI model selects the most suitable delivery partner and redelivers the food. After the user has received the meal, they can provide feedback, making the redelivery process more efficient and reliable in the future.

[0794] Here are some examples of prompts:

[0795] "The customer would like to redeliver the curry. Please select a reliable delivery partner and request a redelivery."

[0796] In this way, the redelivery system can perform redelivery efficiently and quickly.

[0797] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0798] Step 1:

[0799] Redelivery request from user device

[0800] The user requests redelivery using a smartphone app. They enter the necessary information (desired redelivery time, current location, etc.) into the app interface and press the send button. The input data is sent to the server.

[0801] Step 2:

[0802] Generate a list of users in the surrounding area on the server

[0803] The server analyzes the received redelivery request information, collects user information within the specified area (for example, within a 500-meter radius), and generates a list based on past user data retrieved from a database.

[0804] Step 3:

[0805] Evaluating the reliability of proxy candidates using generative AI

[0806] The server sends the created list of users in the surrounding area to the generation AI. The generation AI analyzes each proxy candidate's past behavior history, attribute information, and ratings from other users to calculate a reliability score. Based on this, the optimal proxy candidate is selected.

[0807] Step 4:

[0808] Sending a redelivery request

[0809] The server sends a redelivery request to the most suitable proxy candidate based on the reliability score calculated by the generation AI, waits for the candidate to accept the request, and checks the result.

[0810] Step 5:

[0811] Receiving redelivery completion information

[0812] The agent redelivers the parcel and, once the redelivery is complete, presses the "Redelivery Complete" button on the app. This sends delivery completion information to the server, which then analyzes the received completion information.

[0813] Step 6:

[0814] Notification of redelivery completion to the user

[0815] The server then notifies the user device that redelivery has been completed, and the user can confirm this via the smartphone app.

[0816] Step 7:

[0817] Gathering feedback

[0818] After the redelivery is completed, the server sends a message to the user requesting feedback. When the user enters and submits feedback, the information is collected by the server. The server stores this feedback information in a database and uses it to calculate the reliability score from the next time onwards.

[0819] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0820] This invention provides a system that combines a new emotion engine that recognizes the user's emotions in order to improve the efficiency of redelivery. Below, we will explain in natural language how the program of this system works, and also provide specific examples.

[0821] 1. Accepting delivery requests and selecting agents

[0822] a. User Device:

[0823] The user launches the app and selects "Request redelivery."

[0824] Enter the necessary information (e.g., size of the delivery item, desired time period for receipt, etc.) and press the send button.

[0825] b. Server:

[0826] The delivery request information received from the user is analyzed.

[0827] Obtain the address information of the user who wishes to have the item redelivered.

[0828] c. Server:

[0829] Based on the content of the delivery request, a list of users in areas where redelivery is possible is generated. This list is obtained from a preset range (for example, within a radius of 500 meters).

[0830] 2. Evaluation of the reliability of the agent

[0831] a. Server:

[0832] From a list of users in the surrounding area, users who can act as substitutes for redelivery are listed as substitute candidates.

[0833] b. Server:

[0834] Information about the potential agent (past behavioral history, ratings from other users, attribute information, etc.) is sent to the generation AI.

[0835] c. Generation AI:

[0836] Each agent's past behavior history, attribute information, and evaluations from other users are analyzed.

[0837] An overall reliability score is calculated for each candidate.

[0838] 3. Emotional engine for recognizing user emotions

[0839] a. User Device:

[0840] When a user requests redelivery, they use text input or voice input.

[0841] b. Emotion engine (on the server):

[0842] It analyzes the user's text or voice input to determine their emotional state, for example, recognizing that the user is frustrated from the text "This package is delayed and I'm having trouble."

[0843] c. Server:

[0844] Based on the user's perceived emotional state, the system adaptively adjusts redelivery requests, for example by prioritizing a more reliable agent for a particularly dissatisfied user.

[0845] 4. Confirmation and notification of proxy request

[0846] a. Server:

[0847] Send a redelivery request message to highly rated agents (e.g., to Agent A, "Please redeliver User B's package").

[0848] Wait for the agent to accept the request and receive the result.

[0849] b. Server:

[0850] If the agent accepts the request, a notification is sent to the user stating "Redelivery will be carried out by the agent." If not accepted, the request is sent to the next agent.

[0851] 5. Redelivery and completion report

[0852] a. Agent (Agreement Agent):

[0853] Upon receiving a request, we will go to the specified address and carry out the redelivery.

[0854] Once the redelivery is complete, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[0855] b. Server:

[0856] Upon receiving the delivery completion information, a completion notification is sent to the user stating that "the package has been delivered safely."

[0857] At the same time, a message requesting evaluation of the agent is sent to the user to collect feedback.

[0858] Specific examples

[0859] 1. Accepting delivery requests and selecting agents

[0860] a. User's device:

[0861] The message will say, "Your delivery has arrived and needs to be redelivered. Would you like to ask a neighbor to redeliver it?"

[0862] The user selects "Yes" and sends a redelivery request.

[0863] b. Server:

[0864] Collect information about nearby users and create a list of multiple proxy candidates. Send information about these candidates to the generation AI and request a reliability evaluation.

[0865] 2. Evaluation of the reliability of the agent

[0866] a. Generation AI:

[0867] A candidate's past behavioral history (e.g., 10 redelivery requests received in the past, all of which were successful) is analyzed and a reliability score is assigned to each candidate.

[0868] Candidates are given a high reliability score and selected as the best proxy.

[0869] 3. Emotional engine for recognizing user emotions

[0870] a. User Device:

[0871] The emotion engine analyzes the text entered by the user, "My package is delayed and I'm having a lot of trouble."

[0872] b. Emotion Engine:

[0873] The emotional state of the user is determined to be distressed, and the information is transmitted to the server.

[0874] c. Server:

[0875] Based on information from the emotion engine, the system prioritizes the selection of particularly reliable agents and coordinates redelivery requests.

[0876] 4. Confirmation and notification of proxy request

[0877] a. Server:

[0878] A request message "Please redeliver the user's package" is sent to the agent.

[0879] If accepted, the user will receive a notification that "redelivery will be carried out by a proxy."

[0880] 5. Redelivery and completion report

[0881] a. Agent:

[0882] Head to the delivery destination and deliver the user's package safely.

[0883] After the delivery is completed, a "redelivery completed" report is sent to the server.

[0884] b. Server:

[0885] Upon receiving a report that delivery has been completed, the system sends a completion notification to the user stating that "your package has been delivered."

[0886] A request for evaluation of the agent is sent to the user, and feedback is collected. This feedback is used for future evaluation of the agent's reliability.

[0887] The processing flow will be explained below.

[0888] Step 1:

[0889] On the user's device:

[0890] The user launches the app and selects "Request redelivery."

[0891] Enter the necessary information (size of the delivery item, desired time of receipt, etc.) and press the send button.

[0892] Step 2:

[0893] server:

[0894] Redelivery request information sent from the user is received.

[0895] Verify the user's address and shipping details.

[0896] Step 3:

[0897] server:

[0898] Based on the user's redelivery request, a list of users in the surrounding area within the specified range is generated. For example, information on users within a 500-meter radius of the address of the user requesting redelivery is obtained.

[0899] Step 4:

[0900] server:

[0901] From a list of users in the surrounding area, users who can act as substitutes for redelivery are listed as substitute candidates.

[0902] Step 5:

[0903] server:

[0904] Information about the potential agent (past behavioral history, ratings from other users, attribute information, etc.) is sent to the generation AI.

[0905] Step 6:

[0906] Generation AI:

[0907] The past behavioral history of the agent candidate (e.g., number of past redelivery attempts and success rate), attribute information (e.g., proximity to residence), and ratings from other users are analyzed.

[0908] An overall reliability score is calculated for each candidate.

[0909] Step 7:

[0910] server:

[0911] Select the most trustworthy agent based on their reliability score.

[0912] Step 8:

[0913] On the user's device:

[0914] When a user requests redelivery, they use text input or voice input.

[0915] Step 9:

[0916] Emotion engine (on the server):

[0917] It analyzes the user's text or voice input to determine their emotional state, for example, recognizing that the user is frustrated from the text "This package is delayed and I'm having trouble."

[0918] Step 10:

[0919] server:

[0920] Based on the user's perceived emotional state, the system adaptively adjusts redelivery requests, for example by prioritizing a more reliable agent for a particularly dissatisfied user.

[0921] Step 11:

[0922] server:

[0923] Send a redelivery request message to highly rated agents (e.g., to Agent A, "Please redeliver User B's package").

[0924] Step 12:

[0925] Agent's device:

[0926] The agent receives the redelivery request message and chooses whether to accept the request.

[0927] When the agent presses the accept button, this information is sent to the server.

[0928] Step 13:

[0929] server:

[0930] Verify that the agent has accepted the redelivery request.

[0931] Send a notification to the user saying "Redelivery will be carried out by a substitute."

[0932] Step 14:

[0933] Agent:

[0934] We will go to the specified address and redeliver the parcel.

[0935] Once the redelivery is complete, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[0936] Step 15:

[0937] server:

[0938] Upon receiving the delivery completion information, a completion notification is sent to the user stating that "the package has been delivered safely."

[0939] At the same time, a message requesting evaluation of the agent is sent to the user to collect feedback.

[0940] Step 16:

[0941] On the user's device:

[0942] Receive a review message for the redelivery and enter your feedback in the app and submit it.

[0943] Step 17:

[0944] server:

[0945] Save the feedback information from the user in a database.

[0946] The collected feedback information will be reflected in future reliability evaluations.

[0947] Example 2

[0948] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0949] Conventional redelivery systems did not efficiently process users' redelivery requests, and it was difficult to respond flexibly, taking into account the user's feelings and the urgency of the redelivery. Furthermore, there was insufficient evaluation of the reliability of the agent selection process, so the quality of redelivery was often not guaranteed. This led to problems such as lower user satisfaction and a loss of redelivery efficiency.

[0950] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0951] In this invention, the server includes a means for accepting redelivery requests, a means for generating a list of users in the surrounding area, a means for selecting agent candidates based on the user list, a means for evaluating the reliability of the agent candidates using a generation AI, a means for analyzing the user's emotional state and adjusting the content of the redelivery request, a means for sending a redelivery request based on the evaluation results and the user's emotional state, a means for receiving information that the redelivery has been completed, and a means for notifying the user of the redelivery completion information. This allows redelivery requests to be processed efficiently and quickly, and enables flexible responses based on the user's emotions. Furthermore, selecting a highly reliable agent improves the quality of redelivery and user satisfaction.

[0952] "Means for accepting redelivery requests" refers to a function that allows a user to input the necessary information when requesting redelivery and send it to the system.

[0953] "Means for generating a list of users in the surrounding area" refers to a function for collecting user information within an area where redelivery is possible and compiling it in list form.

[0954] "Means for selecting candidate agents" refers to a function for extracting people who can act as agents for redelivery from a list of users in the surrounding area and listing them as candidates.

[0955] "Means for evaluating trustworthiness using generative AI" refers to a function that uses generative AI to analyze the past behavioral history, attribute information, and evaluations from other users of a potential agent and calculate a trustworthiness score.

[0956] "Means for analyzing the user's emotional state and adjusting the content of the redelivery request" refers to a function for analyzing the user's emotional state from the content entered by the user and adaptively adjusting the priority and arrangement content of the redelivery request based on that information.

[0957] "Means for sending a redelivery request based on the evaluation results and emotional state" refers to a function for sending a redelivery request message to a selected agent based on the results of the reliability evaluation and emotional analysis.

[0958] "Means for receiving information on the completion of redelivery" refers to the function for reporting and receiving information to the system when an agent completes redelivery.

[0959] "Means for notifying the user of redelivery completion information" refers to a function for notifying the user of the completion information when redelivery is completed.

[0960] This invention is directed to a system that combines a new emotion engine that recognizes user emotions to improve the efficiency of redelivery. This system is implemented using the following hardware and software:

[0961] Server: AWS (Amazon Web Services) is used. The server receives redelivery request information from users, analyzes it, and generates a list of nearby users.

[0962] Generative AI model: OpenAI GPT-4 is used. Generative AI is used to evaluate the trustworthiness of potential agents.

[0963] Emotion engine: Using IBM Watson Natural Language Understanding, the emotion engine analyzes the emotional state of the user's input and provides that information to the server.

[0964] User device: Uses an iOS or Android device. The user device provides an interface for inputting and confirming redelivery requests, inputting emotional states, etc.

[0965] Specific examples of implementation

[0966] 1. Accepting delivery requests:

[0967] User device: The user launches the app and selects the redelivery request option. The user then enters the details of the redelivery request (size of the item, desired time of receipt, etc.) and presses the send button.

[0968] 2. Analyzing delivery requests and listing potential agents:

[0969] Server: Receives the information sent by the user and records it in a database. The server generates a list of users in areas where redelivery is possible based on the user's address information.

[0970] 3. Agent Credibility Assessment:

[0971] Server: Extracts proxy candidates from the generated user list and collects their profile information. Sends the collected information to the generation AI and requests a reliability evaluation.

[0972] Generative AI: Calculates a reliability score for each agent, based on, for example, the number of successful redeliveries in the past and the overall score of user ratings.

[0973] 4. Emotion engine recognizes user emotions:

[0974] User terminal: The user uses text or voice to input a redelivery request.

[0975] Emotion engine: Analyzes input text or voice and determines the user's emotional state (e.g., dissatisfied, satisfied, confused, etc.). Provides the analysis results to the server.

[0976] 5. Submission and notification of proxy requests:

[0977] Server: Based on the results of the reliability evaluation and sentiment analysis, the server sends a redelivery request message to the selected agent. The server notifies the user that the redelivery will be carried out by the agent.

[0978] 6. Redelivery and Completion Report:

[0979] Agent: Receives the request and carries out redelivery. Once redelivery is complete, presses the "Redelivery Completed" button to report to the server.

[0980] Server: Receives delivery completion information and sends a completion notification to the user. At the same time, it sends a message to the user requesting a rating of the agent and collects feedback.

[0981] Prompt sentences in specific examples

[0982] Below are some example prompts to input to a generative AI model:

[0983] "Nearby user list:

[0984] 1. User A - Past behavior history: All 10 redelivery requests were successful

[0985] 2. User B - Past behavior history: 5 successful redelivery requests out of 8

[0986] 3. User C - Past behavior history: 14 successful redelivery requests out of 15

[0987] Based on these candidates, calculate a credibility score for each."

[0988] In this way, the system efficiently processes users' redelivery requests and uses emotion recognition technology to select the most suitable agent, improving redelivery efficiency and user satisfaction.

[0989] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0990] Step 1:

[0991] User device: The user launches the app and selects the redelivery request option. The user then enters the details of the redelivery request (size of the item, desired time of receipt, etc.) and presses the send button.

[0992] Input: User's delivery details and desired time slot for receipt.

[0993] Output: User's redelivery request information.

[0994] Step 2:

[0995] Server: Receives delivery request information sent by users and records it in a database. Based on address information, it generates a list of users in an area where redelivery is possible (for example, within a 500-meter radius).

[0996] Input: User's redelivery request information, user's address information.

[0997] Output: A list of users in the surrounding area.

[0998] Step 3:

[0999] Server: Extracts proxy candidates from the generated user list and collects each candidate's profile information (past behavioral history, user ratings, attribute information, etc.). This information is sent to the generation AI and a reliability evaluation is requested.

[1000] Input: A list of patrons in the surrounding area.

[1001] Output: Profile information of potential agents.

[1002] Step 4:

[1003] Generative AI model: Analyzes the profile information of each agent candidate and calculates a reliability score.

[1004] Input: The profile information of the potential agent.

[1005] Output: A confidence score.

[1006] Step 5:

[1007] User terminal: The user inputs a redelivery request using text or voice. This input is sent to the emotion engine.

[1008] Input: User text or voice input.

[1009] Output: User sentiment analysis information.

[1010] Step 6:

[1011] Emotion engine: Analyzes input text or voice to determine the user's emotional state and provides this information to the server.

[1012] Input: User text or voice input.

[1013] Output: User's emotional state information.

[1014] Step 7:

[1015] Server: Adjusts the redelivery request based on the reliability score and emotional state, then sends the redelivery request message to the agent with the highest rating.

[1016] Input: Confidence score, user emotional state information.

[1017] Output: Adjusted redelivery request details and redelivery request message.

[1018] Step 8:

[1019] Server: Waits for information from the agent about whether the redelivery request has been accepted or rejected, and receives the result.

[1020] Input: Redelivery request message.

[1021] Output: The agent's acceptance or rejection information.

[1022] Step 9:

[1023] Server: If the agent accepts the redelivery request, it sends a notification to the user saying "Redelivery will be carried out by the agent." If not accepted, it sends the request to the next agent.

[1024] Input: Agent acceptance or denial information.

[1025] Output: Redelivery notification, message to resubmit to next agent.

[1026] Step 10:

[1027] Agent: Receives the request and carries out redelivery. Once redelivery is complete, presses the "Redelivery Completed" button to report to the server.

[1028] Input: Redelivery request details.

[1029] Output: Redelivery completion information.

[1030] Step 11:

[1031] Server: Receives the redelivery completion information and sends a completion notification to the user stating that "the package has been delivered safely." It also sends a message to the user requesting a rating for the agent and collects feedback.

[1032] Input: Redelivery completion information.

[1033] Output: Redelivery completion notification, rating request message, feedback.

[1034] (Application example 2)

[1035] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1036] It has been reported that redelivery and delivery delays have a significant impact on user satisfaction in food delivery services. In particular, when users feel anxious or uncomfortable due to delivery delays or redelivery, their evaluation of the service declines. Therefore, there is a need for systems that can properly recognize users' emotions and respond in a way that is most appropriate for their situation. Current systems do not adequately take users' emotions into consideration, and improving the efficiency of redelivery and user satisfaction remains a challenge.

[1037] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1038] In this invention, the server includes means for accepting redelivery requests, means for generating a list of users in the surrounding area, means for selecting agent candidates based on the user list, means for evaluating the reliability of the agent candidates using a generation AI, means for sending a redelivery request based on the evaluation results, means for receiving information that redelivery has been completed, means for notifying the user of the redelivery completion information, and means for selecting a delivery partner based on the user's emotional state, which includes an emotion engine that recognizes the user's emotional state when placing an order. This makes it possible to select the most appropriate delivery partner taking the user's emotions into consideration.

[1039] The "means for accepting a request for redelivery" is a system function that accepts a request for redelivery when a user desires redelivery.

[1040] The "means for generating a list of users in the surrounding area" is a system function for creating a list of users who live in the surrounding area of ​​the requester when a redelivery request is made.

[1041] The "means for selecting proxy candidates" is a system function that selects candidates who can act as proxy redelivery agents based on the generated user list.

[1042] "Means for evaluating trustworthiness using generation AI" refers to a system function for using generation AI to evaluate the trustworthiness of selected proxy candidates.

[1043] The "means for sending a redelivery request" is a system function that sends a redelivery request to the selected agent based on the evaluation results.

[1044] "Means for receiving information that redelivery has been completed" refers to a function by which the system receives information when the agent has completed redelivery.

[1045] The "means for notifying the user of redelivery completion information" is a system function for notifying the user that redelivery has been completed.

[1046] An "emotion engine" is an engine that analyzes and recognizes the emotional state from text and voice input by the user.

[1047] "Means for selecting a delivery partner based on emotional state" is a function of the system that selects the most suitable delivery partner based on the emotional state recognized by the emotion engine.

[1048] This invention provides a system that uses an emotion engine to recognize a user's emotion and take appropriate action to improve the efficiency of redelivery. Specific embodiments of this system are described in detail below.

[1049] System Configuration

[1050] The system of the present invention is comprised of the following major components:

[1051] 1. Server:

[1052] The server accepts redelivery requests, generates a user list, selects proxy candidates, evaluates their reliability, sends redelivery requests, and receives and notifies completion information.

[1053] An emotion engine also runs on the server and analyzes the user's emotional state.

[1054] 2. User Device:

[1055] The user terminal provides an interface for inputting a redelivery request and also has a text or voice input function for inputting an emotional state.

[1056] 3. Delivery partner terminal:

[1057] It provides an interface for delivery partners to receive redelivery request messages and execute redelivery. It also has a reporting function when redelivery is completed.

[1058] Program processing overview

[1059] Below is a natural language explanation of how the system works.

[1060] 1. Redelivery request acceptance:

[1061] The user inputs a redelivery request from the terminal, and the server receives and analyzes this information.

[1062] 2. Generate a user list:

[1063] The server generates a list of users in the surrounding area who may be able to assist with redelivery requests.

[1064] 3. Selection of potential agents:

[1065] The server selects proxy candidates from the generated user list.

[1066] 4. Reliability assessment:

[1067] Using generative AI, a reliability score is calculated based on the candidate agent's past behavioral history, attribute information, and evaluations from other users.

[1068] 5. Emotion Recognition with Emotion Engine:

[1069] The emotion engine analyzes the text and voice entered by the user when requesting redelivery and recognizes the user's emotional state.

[1070] 6. Submitting a redelivery request:

[1071] Based on the perceived emotional state and trustworthiness assessment, a redelivery request is sent to the most suitable agent.

[1072] 7. Receiving and notifying completion information:

[1073] Once the redelivery is complete, the delivery partner terminal sends the completion information to the server, which notifies the user and collects further feedback.

[1074] Hardware and software used

[1075] Hardware: Smartphones, servers

[1076] Software: EmotionEngine (emotion recognition engine), DeliveryService (delivery service), generative AI model

[1077] Specific examples

[1078] For example, if a user sends a redelivery request through a smartphone app saying, "I'm frustrated because the pizza I ordered hasn't arrived yet," the emotion engine will recognize the user's emotional state as "frustrated." Based on this information, the server will select a particularly reliable agent who can quickly redeliver the delivery. After the redelivery is completed, feedback such as "I was very satisfied with the speed of the delivery and the service" is collected and reflected in the next reliability evaluation.

[1079] Prompt Sentence Examples

[1080] If someone enters "I'm frustrated because the pizza I ordered hasn't arrived yet," recognize the user's emotion as "frustrated."

[1081] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1082] Step 1:

[1083] The user inputs and submits a redelivery request.

[1084] Input: The user enters a message, including a redelivery request and sentiment, via text or voice.

[1085] Data processing: The user terminal sends the input data in digital form to the server.

[1086] Output: The server receives this data and processes it as redelivery request data.

[1087] Specific actions: The user opens the app, selects "Request redelivery," enters a message, and presses the send button.

[1088] Step 2:

[1089] The server generates a list of users in the surrounding area.

[1090] Input: The server receives the redelivery request data and the requester's address information.

[1091] Data processing: The server generates a list of other users who live within a certain range based on the requester's address.

[1092] Output: A list of users in the surrounding area is generated.

[1093] Specific behavior: Searches the address database on the server and lists users who live within 500 meters.

[1094] Step 3:

[1095] The server selects candidates for the proxy.

[1096] Input: Receives a server-generated user list.

[1097] Data processing: Select candidates who are likely to be able to act as redelivery agents from the user list.

[1098] Output: A list of potential agents is generated.

[1099] Specific operation: The system refers to the user's past delivery history and ratings and selects candidates for redelivery.

[1100] Step 4:

[1101] The server evaluates the reliability of the proxy candidate.

[1102] Input: The server receives a list of candidate agents and past behavioral history and evaluation information for each candidate.

[1103] Data processing: A reliability evaluation algorithm is run and the generating AI calculates a reliability score.

[1104] Output: The reliability score for each agent candidate is output.

[1105] Specific operation: The server has the generation AI calculate the reliability score and saves the result.

[1106] Step 5:

[1107] The server uses an emotion engine to recognize the user's emotional state.

[1108] Input: The server receives a redelivery request message from the user.

[1109] Data processing: The server uses the emotion engine to analyze the message and recognize the emotional state.

[1110] Output: Multiple emotion tags are output as the user's emotional state.

[1111] What it does: Send the text "I'm frustrated because my pizza order hasn't arrived yet" to the emotion engine, which will recognize it as "frustrated."

[1112] Step 6:

[1113] The server sends a redelivery request based on the emotional state and reliability score.

[1114] Input: Receives emotional state information and a confidence score for each agent.

[1115] Data processing: The server determines the most suitable agent based on the emotional state and reliability score, and sends a redelivery request to that agent.

[1116] Output: A redelivery request message is sent to the selected agent.

[1117] Specific operation: The server will prioritize redelivery requests from highly reliable agents for users who are feeling "irritated."

[1118] Step 7:

[1119] The agent completes the redelivery and sends the completion information to the server.

[1120] Input: After the agent completes the redelivery, he / she presses the "Redelivery Complete" button.

[1121] Data processing: The device sends the completion information to the server.

[1122] Output: Redelivery completion information is received by the server.

[1123] Specific operation: The agent will redeliver the package to the specified address and, once complete, press a button on the app to report completion.

[1124] Step 8:

[1125] The server notifies the user of the redelivery completion information and collects feedback.

[1126] Input: Receive redelivery completion information.

[1127] Data processing: The server sends a completion notification to the user and provides a feedback collection interface.

[1128] Output: A completion notification and a feedback collection request is sent to the user.

[1129] Specific operation: The server sends a notification to the user that the package has been delivered safely, along with a message requesting a rating for the agent.

[1130] 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.

[1131] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[1132] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1133] [Third embodiment]

[1134] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1135] 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.

[1136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1137] 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.

[1138] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1139] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1140] 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.

[1141] 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.

[1142] 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 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.

[1143] 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.

[1144] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1145] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1146] This invention is a system for improving the efficiency of redelivery, and provides a method for users to request redelivery and entrust it to a trusted agent in their neighborhood. Below, we will explain in natural language how the program of this system processes. We will also explain with concrete examples.

[1147] 1. Accepting delivery requests and selecting agents

[1148] a. User Device:

[1149] The user launches the app and selects "Request redelivery."

[1150] Enter the necessary information (e.g., size of the delivery item, desired time period for receipt, etc.) and press the send button.

[1151] b. Server:

[1152] The delivery request information received from the user is analyzed.

[1153] Based on the content of the delivery request, a list of users in areas where redelivery is possible is generated. This list is obtained from a preset range (for example, within a radius of 500 meters).

[1154] 2. Evaluation of the reliability of the agent

[1155] a. Server:

[1156] A list of candidate agents and information about each agent (past behavioral history, evaluations, attributes, etc.) is sent to the generation AI.

[1157] b. Generation AI:

[1158] Each agent's past behavior history (e.g., number of past redeliveries, success rate), attribute information (e.g., proximity to residence, reliability), and ratings from other users are analyzed.

[1159] Calculate an overall reliability score and rate the agent. For example,

[1160] Candidate A: Reliability score 9.5

[1161] Candidate B: Reliability score 8.8

[1162] The agent with the highest reliability score is selected and the result is sent to the server.

[1163] 3. Confirmation and notification of proxy request

[1164] a. Server:

[1165] Send a redelivery request message to highly rated agents (e.g., to Agent A, "Please redeliver User B's package").

[1166] Wait for Agent A to accept the request and receive the result.

[1167] If Agent A accepts the request, a notification will be sent to User B stating that "Redelivery will be carried out by Agent A." If not accepted, the request will be sent to the next agent.

[1168] 4. Redelivery and completion report

[1169] a. Agent A (Agreeing Agent):

[1170] Upon receiving a request, we will go to the specified address and carry out the redelivery.

[1171] After completing delivery, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[1172] b. Server:

[1173] Upon receiving the delivery completion information, a completion notification is sent to User B stating that "the package has been delivered safely."

[1174] A message is sent to User B requesting them to evaluate Agent A and feedback is collected.

[1175] The feedback information is stored in a database and is reflected in the agent reliability evaluation from the next time onwards.

[1176] Specific examples

[1177] 1. Accepting delivery requests and selecting agents

[1178] a. User's device:

[1179] The message will say, "Your delivery has arrived and needs to be redelivered. Would you like to ask a neighbor to redeliver it?"

[1180] The user selects "Yes" and sends a redelivery request.

[1181] b. Server:

[1182] Collect information about users in the vicinity and create a list of multiple proxy candidates.

[1183] Information about these candidates is sent to the generating AI and it is asked to evaluate their reliability.

[1184] 2. Evaluation of the reliability of the agent

[1185] a. Generation AI:

[1186] A candidate's past behavioral history (e.g., 10 redelivery requests received in the past, all of which were successful) is analyzed and a reliability score is assigned to each candidate.

[1187] Candidate A is given a high reliability score and selected as the optimal proxy.

[1188] 3. Confirmation and notification of proxy request

[1189] a. Server:

[1190] A request message is sent to Agent A saying, "Please redeliver User B."

[1191] If Agent A accepts the request, User B will receive a notification that "Redelivery will be carried out by Agent A."

[1192] 4. Redelivery and completion report

[1193] a. Agent A:

[1194] The driver heads to the delivery destination and safely delivers User B's package.

[1195] After the delivery is completed, a "redelivery completed" report is sent to the server.

[1196] b. Server:

[1197] Upon receiving the delivery completion report, the system sends a completion notification to User B stating "The package has been delivered."

[1198] A request for evaluation of agent A is sent to user B, and feedback is collected. This feedback will be used for future agent reliability evaluations.

[1199] The processing flow will be explained below.

[1200] Step 1:

[1201] On the user's device:

[1202] The user launches the app and selects "Request redelivery."

[1203] Enter the necessary information (size of the delivery item, desired time of receipt, etc.) and press the send button.

[1204] Step 2:

[1205] server:

[1206] The delivery request information received from the user is analyzed.

[1207] Obtain the address information of the user who wishes to have the item redelivered.

[1208] Step 3:

[1209] server:

[1210] Based on the contents of the delivery request, a list of users in the surrounding area within a certain range for whom redelivery is possible is generated.

[1211] Step 4:

[1212] server:

[1213] From a list of users in the surrounding area, users who can act as substitutes for redelivery are listed as substitute candidates.

[1214] Step 5:

[1215] server:

[1216] Information about the potential agent (past behavioral history, ratings from other users, attribute information, etc.) is sent to the generation AI.

[1217] Step 6:

[1218] Generation AI:

[1219] The past behavioral history, attribute information, and evaluations from other users of the proxy candidate are analyzed.

[1220] An overall reliability score is calculated for each candidate.

[1221] Step 7:

[1222] server:

[1223] Select the most trustworthy agent based on their reliability score.

[1224] A redelivery request message is sent to the selected agent.

[1225] Step 8:

[1226] Agent's device:

[1227] The agent receives the redelivery request message and chooses whether to accept the request.

[1228] When the agent presses the accept button, this information is sent to the server.

[1229] Step 9:

[1230] server:

[1231] Verify that the agent has accepted the redelivery request.

[1232] A notification is sent to the user stating that "redelivery will be carried out by a substitute."

[1233] Step 10:

[1234] Agent:

[1235] We will go to the specified address and redeliver the parcel.

[1236] Once the redelivery is complete, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[1237] Step 11:

[1238] server:

[1239] Upon receiving the delivery completion information, a completion notification is sent to the user stating that "the package has been delivered safely."

[1240] At the same time, a message requesting evaluation of the agent is sent to the user.

[1241] Step 12:

[1242] On the user's device:

[1243] Receive a review message for the redelivery and enter your feedback in the app and submit it.

[1244] Step 13:

[1245] server:

[1246] Save the feedback information from the user in a database.

[1247] The collected feedback information will be reflected in future reliability evaluations.

[1248] Example 1

[1249] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1250] Improving the efficiency of redelivery and enhancing user convenience requires the rapid and accurate processing of redelivery requests. However, in current redelivery systems, managing redelivery requests is cumbersome, and selecting a proxy and evaluating their reliability often requires time and resources. Another issue is the lack of a means to collect feedback after redelivery is completed and reflect it in the next reliability evaluation.

[1251] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1252] In this invention, the server includes means for accepting redelivery requests, means for generating a list of users in the surrounding area, means for selecting agent candidates based on the user list, means for evaluating the reliability of the agent candidates using a generation AI, means for sending a redelivery request based on the evaluation results, means for receiving information that redelivery has been completed, means for notifying the user of redelivery completion information, means for sending information about the agent candidates to the generation AI, means for collecting detailed information about the agent candidates and calculating a reliability score, means for the agent to perform redelivery and send completion information, and means for sending a request to the next-ranked agent based on the evaluation. This enables fast and efficient processing of redelivery requests, reliable redelivery, and collection of user feedback to be reflected in the next reliability evaluation.

[1253] "Means for accepting redelivery requests" refers to the process of receiving and recording redelivery requests in a database when a user requests redelivery through the application.

[1254] "Means for generating a list of users in the surrounding area" refers to a process of extracting and listing other users within a certain range based on the user's location information.

[1255] "Means for selecting proxy candidates" refers to the process of selecting candidates who can carry out redelivery from the generated user list.

[1256] "Means for evaluating the reliability of proxy candidates using generative AI" refers to the process by which generative AI quantifies and evaluates the reliability of each proxy candidate based on data such as the proxy candidate's past behavioral history, attribute information, and evaluations from other users.

[1257] "Means for sending a redelivery request" refers to a process for sending a redelivery request message to the selected agent candidate.

[1258] "Means for receiving information that redelivery has been completed" refers to the process by which the server receives completion information when the agent has completed redelivery.

[1259] "Means for notifying the user of redelivery completion information" refers to the process of notifying the user that redelivery has been successfully completed.

[1260] "Means for sending information about candidate agents to the generation AI" refers to the process by which the server sends detailed information about candidate agents to the generation AI and requests an evaluation.

[1261] "Means for collecting detailed information on proxy candidates and calculating reliability scores" refers to the process by which the generation AI analyzes various data on proxy candidates and calculates reliability scores.

[1262] "Means for an agent to redeliver and send completion information" refers to the process in which a selected agent redelivers the parcel and sends the completion information to the server.

[1263] "Means for sending the request to the next highest ranking agent based on the rating" refers to the process of re-sending the request to the agent with the next highest reliability score if the first agent does not accept the request.

[1264] This invention is a system for improving the efficiency of redelivery, and provides a method for users to request redelivery and entrust it to a trusted agent in their neighborhood. The program processing of this system will be explained in detail below.

[1265] System Overview

[1266] Hardware or software used

[1267] User device: A mobile device such as a smartphone or tablet used to request redelivery. A dedicated application is installed on the device.

[1268] Server: Receives redelivery requests from users, selects proxy candidates, and evaluates their reliability. A cloud server or on-premise server is used.

[1269] Generative AI model: An AI model for assessing the trustworthiness of potential agents, using algorithms such as random forests or neural networks.

[1270] System Operation

[1271] User's device

[1272] When a user launches the dedicated app, a "Request Redelivery" button will appear on the home screen. The user taps this button, enters the necessary information such as the size of the delivery item and the desired time of receipt, and then presses the "Send" button. This operation sends the entered data to the server.

[1273] server

[1274] The server receives delivery request information sent from the user's device and first analyzes the information. Next, it extracts other users within a certain range (for example, within a 500-meter radius) based on the user's location information and generates a list of users in the surrounding area. This list is used to select proxy candidates.

[1275] The server sends the list of proxy candidates and their detailed information (past behavioral history, ratings, attribute information, etc.) to the generative AI model, which then calculates a reliability score based on that information. The generative AI model then uses a machine learning algorithm to calculate a reliability score for each proxy and returns it to the server. For example, candidate A might be given a reliability score of 9.5, and candidate B might be given a reliability score of 8.8.

[1276] The server sends a redelivery request message to the agent with the highest reliability score. If the agent accepts the request, the server sends a notification to the user saying "Redelivery will be performed by the agent." If the agent does not accept, the server sends the request again to the next agent.

[1277] Agent

[1278] The selected agent will receive the redelivery request, go to the specified address, and deliver the package. After completing the delivery, the agent will press the "Redelivery Complete" button on the dedicated app and send the completion information to the server.

[1279] server

[1280] The server receives delivery completion information from the agent and sends a completion notification to the user saying "The package has been delivered safely." It also sends a message to the user requesting an evaluation of the agent and collects feedback. The collected feedback information is stored in a database and used for the next reliability evaluation.

[1281] Specific examples

[1282] For example, if a user selects "Yes" to the message "Your delivery has arrived and requires redelivery. Would you like to request a neighbor to redeliver it?" and sends a redelivery request, the server collects user information from the surrounding area and creates a list of multiple proxy candidates. It then sends information about the candidates to the generation AI and requests it to evaluate their reliability.

[1283] Prompt Sentence Examples

[1284] "How can we build an AI model that receives redelivery requests, evaluates the reliability of agents, and selects the best agent?"

[1285] By inputting this prompt into a generative AI model, specific construction steps and optimization methods can be obtained.

[1286] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1287] Step 1:

[1288] The user submits a redelivery request

[1289] The user launches a dedicated application on their device and taps the "Request Redelivery" button displayed on the home screen. The user then enters details of the delivery (size, desired time of receipt, etc.) and presses the "Send" button. This operation sends the input data to the server.

[1290] Input: Shipment details entered by the user

[1291] Output: Redelivery request data sent to the server

[1292] Step 2:

[1293] The server analyzes the delivery request

[1294] The server receives the redelivery request data sent from the user's device. It analyzes the received data and extracts information such as the user's address and desired time period. It then uses a geo-distance algorithm to extract other users within a certain range (for example, within a 500-meter radius) of the user's address and generates a user list.

[1295] Input: Redelivery request data

[1296] Output: List of users in the surrounding area

[1297] Step 3:

[1298] The server selects a proxy candidate.

[1299] The server selects candidates for redelivery agents from the generated user list, while also acquiring past behavioral history and evaluation data, and sends this information to the generating AI model.

[1300] Input: Local area user list, candidate behavior history and evaluation data

[1301] Output: Candidate proxy data sent to the generative AI model

[1302] Step 4:

[1303] Generative AI evaluates potential substitutes

[1304] The generative AI model receives the data sent from the server and calculates a reliability score for each agent. It analyzes the data using a machine learning algorithm (e.g., random forest, neural network), and assigns a reliability score to each agent. For example, it assigns a score of 9.5 to candidate A and 8.8 to candidate B. It then sends the reliability evaluation results back to the server.

[1305] Input: Delegate candidate data

[1306] Output: Reliability evaluation results

[1307] Step 5:

[1308] The server sends a redelivery request message

[1309] The server receives the reliability evaluation results returned from the generative AI model and selects the agent with the highest reliability score. It then sends a redelivery request message to the selected agent, stating, "Please redeliver the user's package." At the same time, it waits until the agent accepts the request.

[1310] Input: Reliability evaluation result

[1311] Output: Redelivery request message sent to agent

[1312] Step 6:

[1313] The server notifies the user

[1314] If the agent accepts the redelivery request, the server sends a notification to the user saying, "Redelivery will be performed by the agent." If the agent does not accept the request, the server sends a request message to the next agent.

[1315] Input: Agent consent information

[1316] Output: Notification message sent to the user

[1317] Step 7:

[1318] A substitute will redeliver the item

[1319] The selected agent will receive the redelivery request, go to the specified address and deliver the item. After completing the delivery, the agent will press the "Redelivery Complete" button on the dedicated app and send the delivery completion information to the server.

[1320] Input: Redelivery request message

[1321] Output: Delivery completion information sent to the server

[1322] Step 8:

[1323] The server processes the redelivery completion information and feedback

[1324] The server receives delivery completion information from the agent and sends a completion notification to the user saying "The package has been delivered safely." At the same time, it sends a message to the user requesting an evaluation of the agent and collects feedback. The collected feedback information is stored in a database and will be used for the next agent reliability evaluation.

[1325] Input: Delivery completion information, user feedback

[1326] Output: A completion notification and a rating request message sent to the user, along with feedback information used for the next trust rating.

[1327] (Application example 1)

[1328] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1329] Improving the efficiency and reliability of redelivery is an important issue in the logistics industry. Currently, when a redelivery request is made, processing it takes time and costs money, which is a factor that reduces customer satisfaction. This problem is particularly pronounced in fields such as food delivery, where prompt redelivery is required. Therefore, this invention aims to provide a system that performs redelivery quickly and reliably, thereby improving customer satisfaction.

[1330] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1331] In this invention, the server includes means for accepting a redelivery request, means for generating a list of users in the surrounding area, means for selecting proxy candidates based on the user list, means for evaluating the reliability of the proxy candidates using a generative AI, means for sending a redelivery request based on the evaluation results, means for receiving information that redelivery has been completed, means for notifying the user of the redelivery completion information, means for selecting proxy candidates from a list of delivery partners, and means for using a generative AI model to select the most suitable proxy candidate, thereby enabling faster and more efficient redelivery.

[1332] The "means for accepting a redelivery request" is an interface that receives a redelivery request from a user and registers that information in the system.

[1333] The "means for generating a list of users in the surrounding area" is a function for collecting user information within a specified area and compiling it into a list.

[1334] The "means for selecting a proxy candidate" is an algorithm or function that selects an appropriate proxy from the list of users who can be redelivered.

[1335] "Means of evaluating trustworthiness using generative AI" refers to the process of using generative AI to calculate a trustworthiness score based on the candidate agent's past performance and evaluations.

[1336] The "means for sending a redelivery request" is a function for sending a request for redelivery to the agent selected based on the evaluation results.

[1337] "Means for receiving information that redelivery has been completed" refers to a system for receiving a report that the redelivery process has been completed.

[1338] The "means for notifying the user of redelivery completion information" is a notification function that notifies the user who requested redelivery that redelivery has been completed.

[1339] "Means for selecting a candidate surrogate from a list of delivery partners" refers to a method for selecting an appropriate surrogate from a list of delivery partners who fulfill specific roles such as food delivery.

[1340] "Means of using a generative AI model to select the most suitable proxy candidate" refers to a technology that uses a generative AI model to select the most reliable proxy candidate from multiple proxy candidates.

[1341] This invention is a system that improves the efficiency and reliability of redelivery. The system provides a way for users to request redelivery and entrust it to a trusted agent in their neighborhood. Specifically, it is realized by combining a smartphone application and a cloud server.

[1342] First, a redelivery request is made from the user's device (such as a smartphone). The app has an interface that accepts redelivery requests, and the user enters and submits the necessary information (desired redelivery time, current location, etc.).

[1343] The server analyzes the received redelivery request information and generates a list of users in the surrounding area. The user list is created by collecting user information in a specified area (for example, within a 500-meter radius).

[1344] Next, the server uses a generative AI model to select the most suitable agent from the list of agent candidates. The reliability of each agent is evaluated based on their past behavioral history, attribute information, and ratings from other users. A reliability score is calculated and the most suitable agent is selected. This process is carried out using generative AI.

[1345] After the most suitable agent is selected, the server sends a redelivery request to that agent. If the selected agent accepts the request, that information is sent to the user's terminal.

[1346] Once redelivery is complete, the agent presses the "Redelivery Complete" button on the smartphone application to send delivery completion information to the server. The server receives this information and notifies the user.

[1347] The server then collects user feedback, which is stored in a database and used for future trust assessments.

[1348] For example, if a customer misses a meal they ordered via food delivery, this system can quickly arrange for a redelivery. When the customer requests a redelivery, the generative AI model selects the most suitable delivery partner and redelivers the food. After the user has received the meal, they can provide feedback, making the redelivery process more efficient and reliable in the future.

[1349] Here are some examples of prompts:

[1350] "The customer would like to redeliver the curry. Please select a reliable delivery partner and request a redelivery."

[1351] In this way, the redelivery system can perform redelivery efficiently and quickly.

[1352] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1353] Step 1:

[1354] Redelivery request from user device

[1355] The user requests redelivery using a smartphone app. They enter the necessary information (desired redelivery time, current location, etc.) into the app interface and press the send button. The input data is sent to the server.

[1356] Step 2:

[1357] Generate a list of users in the surrounding area on the server

[1358] The server analyzes the received redelivery request information, collects user information within the specified area (for example, within a 500-meter radius), and generates a list based on past user data retrieved from a database.

[1359] Step 3:

[1360] Evaluating the reliability of proxy candidates using generative AI

[1361] The server sends the created list of users in the surrounding area to the generation AI. The generation AI analyzes each proxy candidate's past behavior history, attribute information, and ratings from other users to calculate a reliability score. Based on this, the optimal proxy candidate is selected.

[1362] Step 4:

[1363] Sending a redelivery request

[1364] The server sends a redelivery request to the most suitable proxy candidate based on the reliability score calculated by the generation AI, waits for the candidate to accept the request, and checks the result.

[1365] Step 5:

[1366] Receiving redelivery completion information

[1367] The agent redelivers the parcel and, once the redelivery is complete, presses the "Redelivery Complete" button on the app. This sends delivery completion information to the server, which then analyzes the received completion information.

[1368] Step 6:

[1369] Notification of redelivery completion to the user

[1370] The server then notifies the user device that redelivery has been completed, and the user can confirm this via the smartphone app.

[1371] Step 7:

[1372] Gathering feedback

[1373] After the redelivery is completed, the server sends a message to the user requesting feedback. When the user enters and submits feedback, the information is collected by the server. The server stores this feedback information in a database and uses it to calculate the reliability score from the next time onwards.

[1374] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1375] This invention provides a system that combines a new emotion engine that recognizes the user's emotions in order to improve the efficiency of redelivery. Below, we will explain in natural language how the program of this system works, and also provide specific examples.

[1376] 1. Accepting delivery requests and selecting agents

[1377] a. User Device:

[1378] The user launches the app and selects "Request redelivery."

[1379] Enter the necessary information (e.g., size of the delivery item, desired time period for receipt, etc.) and press the send button.

[1380] b. Server:

[1381] The delivery request information received from the user is analyzed.

[1382] Obtain the address information of the user who wishes to have the item redelivered.

[1383] c. Server:

[1384] Based on the content of the delivery request, a list of users in areas where redelivery is possible is generated. This list is obtained from a preset range (for example, within a radius of 500 meters).

[1385] 2. Evaluation of the reliability of the agent

[1386] a. Server:

[1387] From a list of users in the surrounding area, users who can act as substitutes for redelivery are listed as substitute candidates.

[1388] b. Server:

[1389] Information about the potential agent (past behavioral history, ratings from other users, attribute information, etc.) is sent to the generation AI.

[1390] c. Generation AI:

[1391] Each agent's past behavior history, attribute information, and evaluations from other users are analyzed.

[1392] An overall reliability score is calculated for each candidate.

[1393] 3. Emotional engine for recognizing user emotions

[1394] a. User Device:

[1395] When a user requests redelivery, they use text input or voice input.

[1396] b. Emotion engine (on the server):

[1397] It analyzes the user's text or voice input to determine their emotional state, for example, recognizing that the user is frustrated from the text "This package is delayed and I'm having trouble."

[1398] c. Server:

[1399] Based on the user's perceived emotional state, the system adaptively adjusts redelivery requests, for example by prioritizing a more reliable agent for a particularly dissatisfied user.

[1400] 4. Confirmation and notification of proxy request

[1401] a. Server:

[1402] Send a redelivery request message to highly rated agents (e.g., to Agent A, "Please redeliver User B's package").

[1403] Wait for the agent to accept the request and receive the result.

[1404] b. Server:

[1405] If the agent accepts the request, a notification is sent to the user stating "Redelivery will be carried out by the agent." If not accepted, the request is sent to the next agent.

[1406] 5. Redelivery and completion report

[1407] a. Agent (Agreement Agent):

[1408] Upon receiving a request, we will go to the specified address and carry out the redelivery.

[1409] Once the redelivery is complete, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[1410] b. Server:

[1411] Upon receiving the delivery completion information, a completion notification is sent to the user stating that "the package has been delivered safely."

[1412] At the same time, a message requesting evaluation of the agent is sent to the user to collect feedback.

[1413] Specific examples

[1414] 1. Accepting delivery requests and selecting agents

[1415] a. User's device:

[1416] The message will say, "Your delivery has arrived and needs to be redelivered. Would you like to ask a neighbor to redeliver it?"

[1417] The user selects "Yes" and sends a redelivery request.

[1418] b. Server:

[1419] Collect information about nearby users and create a list of multiple proxy candidates. Send information about these candidates to the generation AI and request a reliability evaluation.

[1420] 2. Evaluation of the reliability of the agent

[1421] a. Generation AI:

[1422] A candidate's past behavioral history (e.g., 10 redelivery requests received in the past, all of which were successful) is analyzed and a reliability score is assigned to each candidate.

[1423] Candidates are given a high reliability score and selected as the best proxy.

[1424] 3. Emotional engine for recognizing user emotions

[1425] a. User Device:

[1426] The emotion engine analyzes the text entered by the user, "My package is delayed and I'm having a lot of trouble."

[1427] b. Emotion Engine:

[1428] The emotional state of the user is determined to be distressed, and the information is transmitted to the server.

[1429] c. Server:

[1430] Based on information from the emotion engine, the system prioritizes the selection of particularly reliable agents and coordinates redelivery requests.

[1431] 4. Confirmation and notification of proxy request

[1432] a. Server:

[1433] A request message "Please redeliver the user's package" is sent to the agent.

[1434] If accepted, the user will receive a notification that "redelivery will be carried out by a proxy."

[1435] 5. Redelivery and completion report

[1436] a. Agent:

[1437] Head to the delivery destination and deliver the user's package safely.

[1438] After the delivery is completed, a "redelivery completed" report is sent to the server.

[1439] b. Server:

[1440] Upon receiving a report that delivery has been completed, the system sends a completion notification to the user stating that "your package has been delivered."

[1441] A request for evaluation of the agent is sent to the user, and feedback is collected. This feedback is used for future evaluation of the agent's reliability.

[1442] The processing flow will be explained below.

[1443] Step 1:

[1444] On the user's device:

[1445] The user launches the app and selects "Request redelivery."

[1446] Enter the necessary information (size of the delivery item, desired time of receipt, etc.) and press the send button.

[1447] Step 2:

[1448] server:

[1449] Redelivery request information sent from the user is received.

[1450] Verify the user's address and shipping details.

[1451] Step 3:

[1452] server:

[1453] Based on the user's redelivery request, a list of users in the surrounding area within the specified range is generated. For example, information on users within a 500-meter radius of the address of the user requesting redelivery is obtained.

[1454] Step 4:

[1455] server:

[1456] From a list of users in the surrounding area, users who can act as substitutes for redelivery are listed as substitute candidates.

[1457] Step 5:

[1458] server:

[1459] Information about the potential agent (past behavioral history, ratings from other users, attribute information, etc.) is sent to the generation AI.

[1460] Step 6:

[1461] Generation AI:

[1462] The past behavioral history of the agent candidate (e.g., number of past redelivery attempts and success rate), attribute information (e.g., proximity to residence), and ratings from other users are analyzed.

[1463] An overall reliability score is calculated for each candidate.

[1464] Step 7:

[1465] server:

[1466] Select the most trustworthy agent based on their reliability score.

[1467] Step 8:

[1468] On the user's device:

[1469] When a user requests redelivery, they use text input or voice input.

[1470] Step 9:

[1471] Emotion engine (on the server):

[1472] It analyzes the user's text or voice input to determine their emotional state, for example, recognizing that the user is frustrated from the text "This package is delayed and I'm having trouble."

[1473] Step 10:

[1474] server:

[1475] Based on the user's perceived emotional state, the system adaptively adjusts redelivery requests, for example by prioritizing a more reliable agent for a particularly dissatisfied user.

[1476] Step 11:

[1477] server:

[1478] Send a redelivery request message to highly rated agents (e.g., to Agent A, "Please redeliver User B's package").

[1479] Step 12:

[1480] Agent's device:

[1481] The agent receives the redelivery request message and chooses whether to accept the request.

[1482] When the agent presses the accept button, this information is sent to the server.

[1483] Step 13:

[1484] server:

[1485] Verify that the agent has accepted the redelivery request.

[1486] Send a notification to the user saying "Redelivery will be carried out by a substitute."

[1487] Step 14:

[1488] Agent:

[1489] We will go to the specified address and redeliver the parcel.

[1490] Once the redelivery is complete, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[1491] Step 15:

[1492] server:

[1493] Upon receiving the delivery completion information, a completion notification is sent to the user stating that "the package has been delivered safely."

[1494] At the same time, a message requesting evaluation of the agent is sent to the user to collect feedback.

[1495] Step 16:

[1496] On the user's device:

[1497] Receive a review message for the redelivery and enter your feedback in the app and submit it.

[1498] Step 17:

[1499] server:

[1500] Save the feedback information from the user in a database.

[1501] The collected feedback information will be reflected in future reliability evaluations.

[1502] Example 2

[1503] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1504] Conventional redelivery systems did not efficiently process users' redelivery requests, and it was difficult to respond flexibly, taking into account the user's feelings and the urgency of the redelivery. Furthermore, there was insufficient evaluation of the reliability of the agent selection process, so the quality of redelivery was often not guaranteed. This led to problems such as lower user satisfaction and a loss of redelivery efficiency.

[1505] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1506] In this invention, the server includes a means for accepting redelivery requests, a means for generating a list of users in the surrounding area, a means for selecting agent candidates based on the user list, a means for evaluating the reliability of the agent candidates using a generation AI, a means for analyzing the user's emotional state and adjusting the content of the redelivery request, a means for sending a redelivery request based on the evaluation results and the user's emotional state, a means for receiving information that the redelivery has been completed, and a means for notifying the user of the redelivery completion information. This allows redelivery requests to be processed efficiently and quickly, and enables flexible responses based on the user's emotions. Furthermore, selecting a highly reliable agent improves the quality of redelivery and user satisfaction.

[1507] "Means for accepting redelivery requests" refers to a function that allows a user to input the necessary information when requesting redelivery and send it to the system.

[1508] "Means for generating a list of users in the surrounding area" refers to a function for collecting user information within an area where redelivery is possible and compiling it in list form.

[1509] "Means for selecting candidate agents" refers to a function for extracting people who can act as agents for redelivery from a list of users in the surrounding area and listing them as candidates.

[1510] "Means for evaluating trustworthiness using generative AI" refers to a function that uses generative AI to analyze the past behavioral history, attribute information, and evaluations from other users of a potential agent and calculate a trustworthiness score.

[1511] "Means for analyzing the user's emotional state and adjusting the content of the redelivery request" refers to a function for analyzing the user's emotional state from the content entered by the user and adaptively adjusting the priority and arrangement content of the redelivery request based on that information.

[1512] "Means for sending a redelivery request based on the evaluation results and emotional state" refers to a function for sending a redelivery request message to a selected agent based on the results of the reliability evaluation and emotional analysis.

[1513] "Means for receiving information on the completion of redelivery" refers to the function for reporting and receiving information to the system when an agent completes redelivery.

[1514] "Means for notifying the user of redelivery completion information" refers to a function for notifying the user of the completion information when redelivery is completed.

[1515] This invention is directed to a system that combines a new emotion engine that recognizes user emotions to improve the efficiency of redelivery. This system is implemented using the following hardware and software:

[1516] Server: AWS (Amazon Web Services) is used. The server receives redelivery request information from users, analyzes it, and generates a list of nearby users.

[1517] Generative AI model: OpenAI GPT-4 is used. Generative AI is used to evaluate the trustworthiness of potential agents.

[1518] Emotion engine: Using IBM Watson Natural Language Understanding, the emotion engine analyzes the emotional state of the user's input and provides that information to the server.

[1519] User device: Uses an iOS or Android device. The user device provides an interface for inputting and confirming redelivery requests, inputting emotional states, etc.

[1520] Specific examples of implementation

[1521] 1. Accepting delivery requests:

[1522] User device: The user launches the app and selects the redelivery request option. The user then enters the details of the redelivery request (size of the item, desired time of receipt, etc.) and presses the send button.

[1523] 2. Analyzing delivery requests and listing potential agents:

[1524] Server: Receives the information sent by the user and records it in a database. The server generates a list of users in areas where redelivery is possible based on the user's address information.

[1525] 3. Agent Credibility Assessment:

[1526] Server: Extracts proxy candidates from the generated user list and collects their profile information. Sends the collected information to the generation AI and requests a reliability evaluation.

[1527] Generative AI: Calculates a reliability score for each agent, based on, for example, the number of successful redeliveries in the past and the overall score of user ratings.

[1528] 4. Emotion engine recognizes user emotions:

[1529] User terminal: The user uses text or voice to input a redelivery request.

[1530] Emotion engine: Analyzes input text or voice and determines the user's emotional state (e.g., dissatisfied, satisfied, confused, etc.). Provides the analysis results to the server.

[1531] 5. Submission and notification of proxy requests:

[1532] Server: Based on the results of the reliability evaluation and sentiment analysis, the server sends a redelivery request message to the selected agent. The server notifies the user that the redelivery will be carried out by the agent.

[1533] 6. Redelivery and Completion Report:

[1534] Agent: Receives the request and carries out redelivery. Once redelivery is complete, presses the "Redelivery Completed" button to report to the server.

[1535] Server: Receives delivery completion information and sends a completion notification to the user. At the same time, it sends a message to the user requesting a rating of the agent and collects feedback.

[1536] Prompt sentences in specific examples

[1537] Below are some example prompts to input to a generative AI model:

[1538] "Nearby user list:

[1539] 1. User A - Past behavior history: All 10 redelivery requests were successful

[1540] 2. User B - Past behavior history: 5 successful redelivery requests out of 8

[1541] 3. User C - Past behavior history: 14 successful redelivery requests out of 15

[1542] Based on these candidates, calculate a credibility score for each."

[1543] In this way, the system efficiently processes users' redelivery requests and uses emotion recognition technology to select the most suitable agent, improving redelivery efficiency and user satisfaction.

[1544] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1545] Step 1:

[1546] User device: The user launches the app and selects the redelivery request option. The user then enters the details of the redelivery request (size of the item, desired time of receipt, etc.) and presses the send button.

[1547] Input: User's delivery details and desired time slot for receipt.

[1548] Output: User's redelivery request information.

[1549] Step 2:

[1550] Server: Receives delivery request information sent by users and records it in a database. Based on address information, it generates a list of users in an area where redelivery is possible (for example, within a 500-meter radius).

[1551] Input: User's redelivery request information, user's address information.

[1552] Output: A list of users in the surrounding area.

[1553] Step 3:

[1554] Server: Extracts proxy candidates from the generated user list and collects each candidate's profile information (past behavioral history, user ratings, attribute information, etc.). This information is sent to the generation AI and a reliability evaluation is requested.

[1555] Input: A list of patrons in the surrounding area.

[1556] Output: Profile information of potential agents.

[1557] Step 4:

[1558] Generative AI model: Analyzes the profile information of each agent candidate and calculates a reliability score.

[1559] Input: The profile information of the potential agent.

[1560] Output: A confidence score.

[1561] Step 5:

[1562] User terminal: The user inputs a redelivery request using text or voice. This input is sent to the emotion engine.

[1563] Input: User text or voice input.

[1564] Output: User sentiment analysis information.

[1565] Step 6:

[1566] Emotion engine: Analyzes input text or voice to determine the user's emotional state and provides this information to the server.

[1567] Input: User text or voice input.

[1568] Output: User's emotional state information.

[1569] Step 7:

[1570] Server: Adjusts the redelivery request based on the reliability score and emotional state, then sends the redelivery request message to the agent with the highest rating.

[1571] Input: Confidence score, user emotional state information.

[1572] Output: Adjusted redelivery request details and redelivery request message.

[1573] Step 8:

[1574] Server: Waits for information from the agent about whether the redelivery request has been accepted or rejected, and receives the result.

[1575] Input: Redelivery request message.

[1576] Output: The agent's acceptance or rejection information.

[1577] Step 9:

[1578] Server: If the agent accepts the redelivery request, it sends a notification to the user saying "Redelivery will be carried out by the agent." If not accepted, it sends the request to the next agent.

[1579] Input: Agent acceptance or denial information.

[1580] Output: Redelivery notification, message to resubmit to next agent.

[1581] Step 10:

[1582] Agent: Receives the request and carries out redelivery. Once redelivery is complete, presses the "Redelivery Completed" button to report to the server.

[1583] Input: Redelivery request details.

[1584] Output: Redelivery completion information.

[1585] Step 11:

[1586] Server: Receives the redelivery completion information and sends a completion notification to the user stating that "the package has been delivered safely." It also sends a message to the user requesting a rating for the agent and collects feedback.

[1587] Input: Redelivery completion information.

[1588] Output: Redelivery completion notification, rating request message, feedback.

[1589] (Application example 2)

[1590] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1591] It has been reported that redelivery and delivery delays have a significant impact on user satisfaction in food delivery services. In particular, when users feel anxious or uncomfortable due to delivery delays or redelivery, their evaluation of the service declines. Therefore, there is a need for systems that can properly recognize users' emotions and respond in a way that is most appropriate for their situation. Current systems do not adequately take users' emotions into consideration, and improving the efficiency of redelivery and user satisfaction remains a challenge.

[1592] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1593] In this invention, the server includes means for accepting redelivery requests, means for generating a list of users in the surrounding area, means for selecting agent candidates based on the user list, means for evaluating the reliability of the agent candidates using a generation AI, means for sending a redelivery request based on the evaluation results, means for receiving information that redelivery has been completed, means for notifying the user of the redelivery completion information, and means for selecting a delivery partner based on the user's emotional state, which includes an emotion engine that recognizes the user's emotional state when placing an order. This makes it possible to select the most appropriate delivery partner taking the user's emotions into consideration.

[1594] The "means for accepting a request for redelivery" is a system function that accepts a request for redelivery when a user desires redelivery.

[1595] The "means for generating a list of users in the surrounding area" is a system function for creating a list of users who live in the surrounding area of ​​the requester when a redelivery request is made.

[1596] The "means for selecting proxy candidates" is a system function that selects candidates who can act as proxy redelivery agents based on the generated user list.

[1597] "Means for evaluating trustworthiness using generation AI" refers to a system function for using generation AI to evaluate the trustworthiness of selected proxy candidates.

[1598] The "means for sending a redelivery request" is a system function that sends a redelivery request to the selected agent based on the evaluation results.

[1599] "Means for receiving information that redelivery has been completed" refers to a function by which the system receives information when the agent has completed redelivery.

[1600] The "means for notifying the user of redelivery completion information" is a system function for notifying the user that redelivery has been completed.

[1601] An "emotion engine" is an engine that analyzes and recognizes the emotional state from text and voice input by the user.

[1602] "Means for selecting a delivery partner based on emotional state" is a function of the system that selects the most suitable delivery partner based on the emotional state recognized by the emotion engine.

[1603] This invention provides a system that uses an emotion engine to recognize a user's emotion and take appropriate action to improve the efficiency of redelivery. Specific embodiments of this system are described in detail below.

[1604] System Configuration

[1605] The system of the present invention is comprised of the following major components:

[1606] 1. Server:

[1607] The server accepts redelivery requests, generates a user list, selects proxy candidates, evaluates their reliability, sends redelivery requests, and receives and notifies completion information.

[1608] An emotion engine also runs on the server and analyzes the user's emotional state.

[1609] 2. User Device:

[1610] The user terminal provides an interface for inputting a redelivery request and also has a text or voice input function for inputting an emotional state.

[1611] 3. Delivery partner terminal:

[1612] It provides an interface for delivery partners to receive redelivery request messages and execute redelivery. It also has a reporting function when redelivery is completed.

[1613] Program processing overview

[1614] Below is a natural language explanation of how the system works.

[1615] 1. Redelivery request acceptance:

[1616] The user inputs a redelivery request from the terminal, and the server receives and analyzes this information.

[1617] 2. Generate a user list:

[1618] The server generates a list of users in the surrounding area who may be able to assist with redelivery requests.

[1619] 3. Selection of potential agents:

[1620] The server selects proxy candidates from the generated user list.

[1621] 4. Reliability assessment:

[1622] Using generative AI, a reliability score is calculated based on the candidate agent's past behavioral history, attribute information, and evaluations from other users.

[1623] 5. Emotion Recognition with Emotion Engine:

[1624] The emotion engine analyzes the text and voice entered by the user when requesting redelivery and recognizes the user's emotional state.

[1625] 6. Submitting a redelivery request:

[1626] Based on the perceived emotional state and trustworthiness assessment, a redelivery request is sent to the most suitable agent.

[1627] 7. Receiving and notifying completion information:

[1628] Once the redelivery is complete, the delivery partner terminal sends the completion information to the server, which notifies the user and collects further feedback.

[1629] Hardware and software used

[1630] Hardware: Smartphones, servers

[1631] Software: EmotionEngine (emotion recognition engine), DeliveryService (delivery service), generative AI model

[1632] Specific examples

[1633] For example, if a user sends a redelivery request through a smartphone app saying, "I'm frustrated because the pizza I ordered hasn't arrived yet," the emotion engine will recognize the user's emotional state as "frustrated." Based on this information, the server will select a particularly reliable agent who can quickly redeliver the delivery. After the redelivery is completed, feedback such as "I was very satisfied with the speed of the delivery and the service" is collected and reflected in the next reliability evaluation.

[1634] Prompt Sentence Examples

[1635] If someone enters "I'm frustrated because the pizza I ordered hasn't arrived yet," recognize the user's emotion as "frustrated."

[1636] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1637] Step 1:

[1638] The user inputs and submits a redelivery request.

[1639] Input: The user enters a message, including a redelivery request and sentiment, via text or voice.

[1640] Data processing: The user terminal sends the input data in digital form to the server.

[1641] Output: The server receives this data and processes it as redelivery request data.

[1642] Specific actions: The user opens the app, selects "Request redelivery," enters a message, and presses the send button.

[1643] Step 2:

[1644] The server generates a list of users in the surrounding area.

[1645] Input: The server receives the redelivery request data and the requester's address information.

[1646] Data processing: The server generates a list of other users who live within a certain range based on the requester's address.

[1647] Output: A list of users in the surrounding area is generated.

[1648] Specific behavior: Searches the address database on the server and lists users who live within 500 meters.

[1649] Step 3:

[1650] The server selects candidates for the proxy.

[1651] Input: Receives a server-generated user list.

[1652] Data processing: Select candidates who are likely to be able to act as redelivery agents from the user list.

[1653] Output: A list of potential agents is generated.

[1654] Specific operation: The system refers to the user's past delivery history and ratings and selects candidates for redelivery.

[1655] Step 4:

[1656] The server evaluates the reliability of the proxy candidate.

[1657] Input: The server receives a list of candidate agents and past behavioral history and evaluation information for each candidate.

[1658] Data processing: A reliability evaluation algorithm is run and the generating AI calculates a reliability score.

[1659] Output: The reliability score for each agent candidate is output.

[1660] Specific operation: The server has the generation AI calculate the reliability score and saves the result.

[1661] Step 5:

[1662] The server uses an emotion engine to recognize the user's emotional state.

[1663] Input: The server receives a redelivery request message from the user.

[1664] Data processing: The server uses the emotion engine to analyze the message and recognize the emotional state.

[1665] Output: Multiple emotion tags are output as the user's emotional state.

[1666] What it does: Send the text "I'm frustrated because my pizza order hasn't arrived yet" to the emotion engine, which will recognize it as "frustrated."

[1667] Step 6:

[1668] The server sends a redelivery request based on the emotional state and reliability score.

[1669] Input: Receives emotional state information and a confidence score for each agent.

[1670] Data processing: The server determines the most suitable agent based on the emotional state and reliability score, and sends a redelivery request to that agent.

[1671] Output: A redelivery request message is sent to the selected agent.

[1672] Specific operation: The server will prioritize redelivery requests from highly reliable agents for users who are feeling "irritated."

[1673] Step 7:

[1674] The agent completes the redelivery and sends the completion information to the server.

[1675] Input: After the agent completes the redelivery, he / she presses the "Redelivery Complete" button.

[1676] Data processing: The device sends the completion information to the server.

[1677] Output: Redelivery completion information is received by the server.

[1678] Specific operation: The agent will redeliver the package to the specified address and, once complete, press a button on the app to report completion.

[1679] Step 8:

[1680] The server notifies the user of the redelivery completion information and collects feedback.

[1681] Input: Receive redelivery completion information.

[1682] Data processing: The server sends a completion notification to the user and provides a feedback collection interface.

[1683] Output: A completion notification and a feedback collection request is sent to the user.

[1684] Specific operation: The server sends a notification to the user that the package has been delivered safely, along with a message requesting a rating for the agent.

[1685] 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.

[1686] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[1687] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1688] [Fourth embodiment]

[1689] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1690] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1691] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1692] 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.

[1693] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1694] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1695] 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.

[1696] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1697] 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.

[1698] 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 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.

[1699] 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.

[1700] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1701] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1702] This invention is a system for improving the efficiency of redelivery, and provides a method for users to request redelivery and entrust it to a trusted agent in their neighborhood. Below, we will explain in natural language how the program of this system processes. We will also explain with concrete examples.

[1703] 1. Accepting delivery requests and selecting agents

[1704] a. User Device:

[1705] The user launches the app and selects "Request redelivery."

[1706] Enter the necessary information (e.g., size of the delivery item, desired time period for receipt, etc.) and press the send button.

[1707] b. Server:

[1708] The delivery request information received from the user is analyzed.

[1709] Based on the content of the delivery request, a list of users in areas where redelivery is possible is generated. This list is obtained from a preset range (for example, within a radius of 500 meters).

[1710] 2. Evaluation of the reliability of the agent

[1711] a. Server:

[1712] A list of candidate agents and information about each agent (past behavioral history, evaluations, attributes, etc.) is sent to the generation AI.

[1713] b. Generation AI:

[1714] Each agent's past behavior history (e.g., number of past redeliveries, success rate), attribute information (e.g., proximity to residence, reliability), and ratings from other users are analyzed.

[1715] Calculate an overall reliability score and rate the agent. For example,

[1716] Candidate A: Reliability score 9.5

[1717] Candidate B: Reliability score 8.8

[1718] The agent with the highest reliability score is selected and the result is sent to the server.

[1719] 3. Confirmation and notification of proxy request

[1720] a. Server:

[1721] Send a redelivery request message to highly rated agents (e.g., to Agent A, "Please redeliver User B's package").

[1722] Wait for Agent A to accept the request and receive the result.

[1723] If Agent A accepts the request, a notification will be sent to User B stating that "Redelivery will be carried out by Agent A." If not accepted, the request will be sent to the next agent.

[1724] 4. Redelivery and completion report

[1725] a. Agent A (Agreeing Agent):

[1726] Upon receiving a request, we will go to the specified address and carry out the redelivery.

[1727] After completing delivery, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[1728] b. Server:

[1729] Upon receiving the delivery completion information, a completion notification is sent to User B stating that "the package has been delivered safely."

[1730] A message is sent to User B requesting them to evaluate Agent A and feedback is collected.

[1731] The feedback information is stored in a database and is reflected in the agent reliability evaluation from the next time onwards.

[1732] Specific examples

[1733] 1. Accepting delivery requests and selecting agents

[1734] a. User's device:

[1735] The message will say, "Your delivery has arrived and needs to be redelivered. Would you like to ask a neighbor to redeliver it?"

[1736] The user selects "Yes" and sends a redelivery request.

[1737] b. Server:

[1738] Collect information about users in the vicinity and create a list of multiple proxy candidates.

[1739] Information about these candidates is sent to the generating AI and it is asked to evaluate their reliability.

[1740] 2. Evaluation of the reliability of the agent

[1741] a. Generation AI:

[1742] A candidate's past behavioral history (e.g., 10 redelivery requests received in the past, all of which were successful) is analyzed and a reliability score is assigned to each candidate.

[1743] Candidate A is given a high reliability score and selected as the optimal proxy.

[1744] 3. Confirmation and notification of proxy request

[1745] a. Server:

[1746] A request message is sent to Agent A saying, "Please redeliver User B."

[1747] If Agent A accepts the request, User B will receive a notification that "Redelivery will be carried out by Agent A."

[1748] 4. Redelivery and completion report

[1749] a. Agent A:

[1750] The driver heads to the delivery destination and safely delivers User B's package.

[1751] After the delivery is completed, a "redelivery completed" report is sent to the server.

[1752] b. Server:

[1753] Upon receiving the delivery completion report, the system sends a completion notification to User B stating "The package has been delivered."

[1754] A request for evaluation of agent A is sent to user B, and feedback is collected. This feedback will be used for future agent reliability evaluations.

[1755] The processing flow will be explained below.

[1756] Step 1:

[1757] On the user's device:

[1758] The user launches the app and selects "Request redelivery."

[1759] Enter the necessary information (size of the delivery item, desired time of receipt, etc.) and press the send button.

[1760] Step 2:

[1761] server:

[1762] The delivery request information received from the user is analyzed.

[1763] Obtain the address information of the user who wishes to have the item redelivered.

[1764] Step 3:

[1765] server:

[1766] Based on the contents of the delivery request, a list of users in the surrounding area within a certain range for whom redelivery is possible is generated.

[1767] Step 4:

[1768] server:

[1769] From a list of users in the surrounding area, users who can act as substitutes for redelivery are listed as substitute candidates.

[1770] Step 5:

[1771] server:

[1772] Information about the potential agent (past behavioral history, ratings from other users, attribute information, etc.) is sent to the generation AI.

[1773] Step 6:

[1774] Generation AI:

[1775] The past behavioral history, attribute information, and evaluations from other users of the proxy candidate are analyzed.

[1776] An overall reliability score is calculated for each candidate.

[1777] Step 7:

[1778] server:

[1779] Select the most trustworthy agent based on their reliability score.

[1780] A redelivery request message is sent to the selected agent.

[1781] Step 8:

[1782] Agent's device:

[1783] The agent receives the redelivery request message and chooses whether to accept the request.

[1784] When the agent presses the accept button, this information is sent to the server.

[1785] Step 9:

[1786] server:

[1787] Verify that the agent has accepted the redelivery request.

[1788] A notification is sent to the user stating that "redelivery will be carried out by a substitute."

[1789] Step 10:

[1790] Agent:

[1791] We will go to the specified address and redeliver the parcel.

[1792] Once the redelivery is complete, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[1793] Step 11:

[1794] server:

[1795] Upon receiving the delivery completion information, a completion notification is sent to the user stating that "the package has been delivered safely."

[1796] At the same time, a message requesting evaluation of the agent is sent to the user.

[1797] Step 12:

[1798] On the user's device:

[1799] Receive a review message for the redelivery and enter your feedback in the app and submit it.

[1800] Step 13:

[1801] server:

[1802] Save the feedback information from the user in a database.

[1803] The collected feedback information will be reflected in future reliability evaluations.

[1804] Example 1

[1805] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1806] Improving the efficiency of redelivery and enhancing user convenience requires the rapid and accurate processing of redelivery requests. However, in current redelivery systems, managing redelivery requests is cumbersome, and selecting a proxy and evaluating their reliability often requires time and resources. Another issue is the lack of a means to collect feedback after redelivery is completed and reflect it in the next reliability evaluation.

[1807] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1808] In this invention, the server includes means for accepting redelivery requests, means for generating a list of users in the surrounding area, means for selecting agent candidates based on the user list, means for evaluating the reliability of the agent candidates using a generation AI, means for sending a redelivery request based on the evaluation results, means for receiving information that redelivery has been completed, means for notifying the user of redelivery completion information, means for sending information about the agent candidates to the generation AI, means for collecting detailed information about the agent candidates and calculating a reliability score, means for the agent to perform redelivery and send completion information, and means for sending a request to the next-ranked agent based on the evaluation. This enables fast and efficient processing of redelivery requests, reliable redelivery, and collection of user feedback to be reflected in the next reliability evaluation.

[1809] "Means for accepting redelivery requests" refers to the process of receiving and recording redelivery requests in a database when a user requests redelivery through the application.

[1810] "Means for generating a list of users in the surrounding area" refers to a process of extracting and listing other users within a certain range based on the user's location information.

[1811] "Means for selecting proxy candidates" refers to the process of selecting candidates who can carry out redelivery from the generated user list.

[1812] "Means for evaluating the reliability of proxy candidates using generative AI" refers to the process by which generative AI quantifies and evaluates the reliability of each proxy candidate based on data such as the proxy candidate's past behavioral history, attribute information, and evaluations from other users.

[1813] "Means for sending a redelivery request" refers to a process for sending a redelivery request message to the selected agent candidate.

[1814] "Means for receiving information that redelivery has been completed" refers to the process by which the server receives completion information when the agent has completed redelivery.

[1815] "Means for notifying the user of redelivery completion information" refers to the process of notifying the user that redelivery has been successfully completed.

[1816] "Means for sending information about candidate agents to the generation AI" refers to the process by which the server sends detailed information about candidate agents to the generation AI and requests an evaluation.

[1817] "Means for collecting detailed information on proxy candidates and calculating reliability scores" refers to the process by which the generation AI analyzes various data on proxy candidates and calculates reliability scores.

[1818] "Means for an agent to redeliver and send completion information" refers to the process in which a selected agent redelivers the parcel and sends the completion information to the server.

[1819] "Means for sending the request to the next highest ranking agent based on the rating" refers to the process of re-sending the request to the agent with the next highest reliability score if the first agent does not accept the request.

[1820] This invention is a system for improving the efficiency of redelivery, and provides a method for users to request redelivery and entrust it to a trusted agent in their neighborhood. The program processing of this system will be explained in detail below.

[1821] System Overview

[1822] Hardware or software used

[1823] User device: A mobile device such as a smartphone or tablet used to request redelivery. A dedicated application is installed on the device.

[1824] Server: Receives redelivery requests from users, selects proxy candidates, and evaluates their reliability. A cloud server or on-premise server is used.

[1825] Generative AI model: An AI model for assessing the trustworthiness of potential agents, using algorithms such as random forests or neural networks.

[1826] System Operation

[1827] User's device

[1828] When a user launches the dedicated app, a "Request Redelivery" button will appear on the home screen. The user taps this button, enters the necessary information such as the size of the delivery item and the desired time of receipt, and then presses the "Send" button. This operation sends the entered data to the server.

[1829] server

[1830] The server receives delivery request information sent from the user's device and first analyzes the information. Next, it extracts other users within a certain range (for example, within a 500-meter radius) based on the user's location information and generates a list of users in the surrounding area. This list is used to select proxy candidates.

[1831] The server sends the list of proxy candidates and their detailed information (past behavioral history, ratings, attribute information, etc.) to the generative AI model, which then calculates a reliability score based on that information. The generative AI model then uses a machine learning algorithm to calculate a reliability score for each proxy and returns it to the server. For example, candidate A might be given a reliability score of 9.5, and candidate B might be given a reliability score of 8.8.

[1832] The server sends a redelivery request message to the agent with the highest reliability score. If the agent accepts the request, the server sends a notification to the user saying "Redelivery will be performed by the agent." If the agent does not accept, the server sends the request again to the next agent.

[1833] Agent

[1834] The selected agent will receive the redelivery request, go to the specified address, and deliver the package. After completing the delivery, the agent will press the "Redelivery Complete" button on the dedicated app and send the completion information to the server.

[1835] server

[1836] The server receives delivery completion information from the agent and sends a completion notification to the user saying "The package has been delivered safely." It also sends a message to the user requesting an evaluation of the agent and collects feedback. The collected feedback information is stored in a database and used for the next reliability evaluation.

[1837] Specific examples

[1838] For example, if a user selects "Yes" to the message "Your delivery has arrived and requires redelivery. Would you like to request a neighbor to redeliver it?" and sends a redelivery request, the server collects user information from the surrounding area and creates a list of multiple proxy candidates. It then sends information about the candidates to the generation AI and requests it to evaluate their reliability.

[1839] Prompt Sentence Examples

[1840] "How can we build an AI model that receives redelivery requests, evaluates the reliability of agents, and selects the best agent?"

[1841] By inputting this prompt into a generative AI model, specific construction steps and optimization methods can be obtained.

[1842] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1843] Step 1:

[1844] The user submits a redelivery request

[1845] The user launches a dedicated application on their device and taps the "Request Redelivery" button displayed on the home screen. The user then enters details of the delivery (size, desired time of receipt, etc.) and presses the "Send" button. This operation sends the input data to the server.

[1846] Input: Shipment details entered by the user

[1847] Output: Redelivery request data sent to the server

[1848] Step 2:

[1849] The server analyzes the delivery request

[1850] The server receives the redelivery request data sent from the user's device. It analyzes the received data and extracts information such as the user's address and desired time period. It then uses a geo-distance algorithm to extract other users within a certain range (for example, within a 500-meter radius) of the user's address and generates a user list.

[1851] Input: Redelivery request data

[1852] Output: List of users in the surrounding area

[1853] Step 3:

[1854] The server selects a proxy candidate.

[1855] The server selects candidates for redelivery agents from the generated user list, while also acquiring past behavioral history and evaluation data, and sends this information to the generating AI model.

[1856] Input: Local area user list, candidate behavior history and evaluation data

[1857] Output: Candidate proxy data sent to the generative AI model

[1858] Step 4:

[1859] Generative AI evaluates potential substitutes

[1860] The generative AI model receives the data sent from the server and calculates a reliability score for each agent. It analyzes the data using a machine learning algorithm (e.g., random forest, neural network), and assigns a reliability score to each agent. For example, it assigns a score of 9.5 to candidate A and 8.8 to candidate B. It then sends the reliability evaluation results back to the server.

[1861] Input: Delegate candidate data

[1862] Output: Reliability evaluation results

[1863] Step 5:

[1864] The server sends a redelivery request message

[1865] The server receives the reliability evaluation results returned from the generative AI model and selects the agent with the highest reliability score. It then sends a redelivery request message to the selected agent, stating, "Please redeliver the user's package." At the same time, it waits until the agent accepts the request.

[1866] Input: Reliability evaluation result

[1867] Output: Redelivery request message sent to agent

[1868] Step 6:

[1869] The server notifies the user

[1870] If the agent accepts the redelivery request, the server sends a notification to the user saying, "Redelivery will be performed by the agent." If the agent does not accept the request, the server sends a request message to the next agent.

[1871] Input: Agent consent information

[1872] Output: Notification message sent to the user

[1873] Step 7:

[1874] A substitute will redeliver the item

[1875] The selected agent will receive the redelivery request, go to the specified address and deliver the item. After completing the delivery, the agent will press the "Redelivery Complete" button on the dedicated app and send the delivery completion information to the server.

[1876] Input: Redelivery request message

[1877] Output: Delivery completion information sent to the server

[1878] Step 8:

[1879] The server processes the redelivery completion information and feedback

[1880] The server receives delivery completion information from the agent and sends a completion notification to the user saying "The package has been delivered safely." At the same time, it sends a message to the user requesting an evaluation of the agent and collects feedback. The collected feedback information is stored in a database and will be used for the next agent reliability evaluation.

[1881] Input: Delivery completion information, user feedback

[1882] Output: A completion notification and a rating request message sent to the user, along with feedback information used for the next trust rating.

[1883] (Application example 1)

[1884] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1885] Improving the efficiency and reliability of redelivery is an important issue in the logistics industry. Currently, when a redelivery request is made, processing it takes time and costs money, which is a factor that reduces customer satisfaction. This problem is particularly pronounced in fields such as food delivery, where prompt redelivery is required. Therefore, this invention aims to provide a system that performs redelivery quickly and reliably, thereby improving customer satisfaction.

[1886] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1887] In this invention, the server includes means for accepting a redelivery request, means for generating a list of users in the surrounding area, means for selecting proxy candidates based on the user list, means for evaluating the reliability of the proxy candidates using a generative AI, means for sending a redelivery request based on the evaluation results, means for receiving information that redelivery has been completed, means for notifying the user of the redelivery completion information, means for selecting proxy candidates from a list of delivery partners, and means for using a generative AI model to select the most suitable proxy candidate, thereby enabling faster and more efficient redelivery.

[1888] The "means for accepting a redelivery request" is an interface that receives a redelivery request from a user and registers that information in the system.

[1889] The "means for generating a list of users in the surrounding area" is a function for collecting user information within a specified area and compiling it into a list.

[1890] The "means for selecting a proxy candidate" is an algorithm or function that selects an appropriate proxy from the list of users who can be redelivered.

[1891] "Means of evaluating trustworthiness using generative AI" refers to the process of using generative AI to calculate a trustworthiness score based on the candidate agent's past performance and evaluations.

[1892] The "means for sending a redelivery request" is a function for sending a request for redelivery to the agent selected based on the evaluation results.

[1893] "Means for receiving information that redelivery has been completed" refers to a system for receiving a report that the redelivery process has been completed.

[1894] The "means for notifying the user of redelivery completion information" is a notification function that notifies the user who requested redelivery that redelivery has been completed.

[1895] "Means for selecting a candidate surrogate from a list of delivery partners" refers to a method for selecting an appropriate surrogate from a list of delivery partners who fulfill specific roles such as food delivery.

[1896] "Means of using a generative AI model to select the most suitable proxy candidate" refers to a technology that uses a generative AI model to select the most reliable proxy candidate from multiple proxy candidates.

[1897] This invention is a system that improves the efficiency and reliability of redelivery. The system provides a way for users to request redelivery and entrust it to a trusted agent in their neighborhood. Specifically, it is realized by combining a smartphone application and a cloud server.

[1898] First, a redelivery request is made from the user's device (such as a smartphone). The app has an interface that accepts redelivery requests, and the user enters and submits the necessary information (desired redelivery time, current location, etc.).

[1899] The server analyzes the received redelivery request information and generates a list of users in the surrounding area. The user list is created by collecting user information in a specified area (for example, within a 500-meter radius).

[1900] Next, the server uses a generative AI model to select the most suitable agent from the list of agent candidates. The reliability of each agent is evaluated based on their past behavioral history, attribute information, and ratings from other users. A reliability score is calculated and the most suitable agent is selected. This process is carried out using generative AI.

[1901] After the most suitable agent is selected, the server sends a redelivery request to that agent. If the selected agent accepts the request, that information is sent to the user's terminal.

[1902] Once redelivery is complete, the agent presses the "Redelivery Complete" button on the smartphone application to send delivery completion information to the server. The server receives this information and notifies the user.

[1903] The server then collects user feedback, which is stored in a database and used for future trust assessments.

[1904] For example, if a customer misses a meal they ordered via food delivery, this system can quickly arrange for a redelivery. When the customer requests a redelivery, the generative AI model selects the most suitable delivery partner and redelivers the food. After the user has received the meal, they can provide feedback, making the redelivery process more efficient and reliable in the future.

[1905] Here are some examples of prompts:

[1906] "The customer would like to redeliver the curry. Please select a reliable delivery partner and request a redelivery."

[1907] In this way, the redelivery system can perform redelivery efficiently and quickly.

[1908] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1909] Step 1:

[1910] Redelivery request from user device

[1911] The user requests redelivery using a smartphone app. They enter the necessary information (desired redelivery time, current location, etc.) into the app interface and press the send button. The input data is sent to the server.

[1912] Step 2:

[1913] Generate a list of users in the surrounding area on the server

[1914] The server analyzes the received redelivery request information, collects user information within the specified area (for example, within a 500-meter radius), and generates a list based on past user data retrieved from a database.

[1915] Step 3:

[1916] Evaluating the reliability of proxy candidates using generative AI

[1917] The server sends the created list of users in the surrounding area to the generation AI. The generation AI analyzes each proxy candidate's past behavior history, attribute information, and ratings from other users to calculate a reliability score. Based on this, the optimal proxy candidate is selected.

[1918] Step 4:

[1919] Sending a redelivery request

[1920] The server sends a redelivery request to the most suitable proxy candidate based on the reliability score calculated by the generation AI, waits for the candidate to accept the request, and checks the result.

[1921] Step 5:

[1922] Receiving redelivery completion information

[1923] The agent redelivers the parcel and, once the redelivery is complete, presses the "Redelivery Complete" button on the app. This sends delivery completion information to the server, which then analyzes the received completion information.

[1924] Step 6:

[1925] Notification of redelivery completion to the user

[1926] The server then notifies the user device that redelivery has been completed, and the user can confirm this via the smartphone app.

[1927] Step 7:

[1928] Gathering feedback

[1929] After the redelivery is completed, the server sends a message to the user requesting feedback. When the user enters and submits feedback, the information is collected by the server. The server stores this feedback information in a database and uses it to calculate the reliability score from the next time onwards.

[1930] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1931] This invention provides a system that combines a new emotion engine that recognizes the user's emotions in order to improve the efficiency of redelivery. Below, we will explain in natural language how the program of this system works, and also provide specific examples.

[1932] 1. Accepting delivery requests and selecting agents

[1933] a. User Device:

[1934] The user launches the app and selects "Request redelivery."

[1935] Enter the necessary information (e.g., size of the delivery item, desired time period for receipt, etc.) and press the send button.

[1936] b. Server:

[1937] The delivery request information received from the user is analyzed.

[1938] Obtain the address information of the user who wishes to have the item redelivered.

[1939] c. Server:

[1940] Based on the content of the delivery request, a list of users in areas where redelivery is possible is generated. This list is obtained from a preset range (for example, within a radius of 500 meters).

[1941] 2. Evaluation of the reliability of the agent

[1942] a. Server:

[1943] From a list of users in the surrounding area, users who can act as substitutes for redelivery are listed as substitute candidates.

[1944] b. Server:

[1945] Information about the potential agent (past behavioral history, ratings from other users, attribute information, etc.) is sent to the generation AI.

[1946] c. Generation AI:

[1947] Each agent's past behavior history, attribute information, and evaluations from other users are analyzed.

[1948] An overall reliability score is calculated for each candidate.

[1949] 3. Emotional engine for recognizing user emotions

[1950] a. User Device:

[1951] When a user requests redelivery, they use text input or voice input.

[1952] b. Emotion engine (on the server):

[1953] It analyzes the user's text or voice input to determine their emotional state, for example, recognizing that the user is frustrated from the text "This package is delayed and I'm having trouble."

[1954] c. Server:

[1955] Based on the user's perceived emotional state, the system adaptively adjusts redelivery requests, for example by prioritizing a more reliable agent for a particularly dissatisfied user.

[1956] 4. Confirmation and notification of proxy request

[1957] a. Server:

[1958] Send a redelivery request message to highly rated agents (e.g., to Agent A, "Please redeliver User B's package").

[1959] Wait for the agent to accept the request and receive the result.

[1960] b. Server:

[1961] If the agent accepts the request, a notification is sent to the user stating "Redelivery will be carried out by the agent." If not accepted, the request is sent to the next agent.

[1962] 5. Redelivery and completion report

[1963] a. Agent (Agreement Agent):

[1964] Upon receiving a request, we will go to the specified address and carry out the redelivery.

[1965] Once the redelivery is complete, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[1966] b. Server:

[1967] Upon receiving the delivery completion information, a completion notification is sent to the user stating that "the package has been delivered safely."

[1968] At the same time, a message requesting evaluation of the agent is sent to the user to collect feedback.

[1969] Specific examples

[1970] 1. Accepting delivery requests and selecting agents

[1971] a. User's device:

[1972] The message will say, "Your delivery has arrived and needs to be redelivered. Would you like to ask a neighbor to redeliver it?"

[1973] The user selects "Yes" and sends a redelivery request.

[1974] b. Server:

[1975] Collect information about nearby users and create a list of multiple proxy candidates. Send information about these candidates to the generation AI and request a reliability evaluation.

[1976] 2. Evaluation of the reliability of the agent

[1977] a. Generation AI:

[1978] A candidate's past behavioral history (e.g., 10 redelivery requests received in the past, all of which were successful) is analyzed and a reliability score is assigned to each candidate.

[1979] Candidates are given a high reliability score and selected as the best proxy.

[1980] 3. Emotional engine for recognizing user emotions

[1981] a. User Device:

[1982] The emotion engine analyzes the text entered by the user, "My package is delayed and I'm having a lot of trouble."

[1983] b. Emotion Engine:

[1984] The emotional state of the user is determined to be distressed, and the information is transmitted to the server.

[1985] c. Server:

[1986] Based on information from the emotion engine, the system prioritizes the selection of particularly reliable agents and coordinates redelivery requests.

[1987] 4. Confirmation and notification of proxy request

[1988] a. Server:

[1989] A request message "Please redeliver the user's package" is sent to the agent.

[1990] If accepted, the user will receive a notification that "redelivery will be carried out by a proxy."

[1991] 5. Redelivery and completion report

[1992] a. Agent:

[1993] Head to the delivery destination and deliver the user's package safely.

[1994] After the delivery is completed, a "redelivery completed" report is sent to the server.

[1995] b. Server:

[1996] Upon receiving a report that delivery has been completed, the system sends a completion notification to the user stating that "your package has been delivered."

[1997] A request for evaluation of the agent is sent to the user, and feedback is collected. This feedback is used for future evaluation of the agent's reliability.

[1998] The processing flow will be explained below.

[1999] Step 1:

[2000] On the user's device:

[2001] The user launches the app and selects "Request redelivery."

[2002] Enter the necessary information (size of the delivery item, desired time of receipt, etc.) and press the send button.

[2003] Step 2:

[2004] server:

[2005] Redelivery request information sent from the user is received.

[2006] Verify the user's address and shipping details.

[2007] Step 3:

[2008] server:

[2009] Based on the user's redelivery request, a list of users in the surrounding area within the specified range is generated. For example, information on users within a 500-meter radius of the address of the user requesting redelivery is obtained.

[2010] Step 4:

[2011] server:

[2012] From a list of users in the surrounding area, users who can act as substitutes for redelivery are listed as substitute candidates.

[2013] Step 5:

[2014] server:

[2015] Information about the potential agent (past behavioral history, ratings from other users, attribute information, etc.) is sent to the generation AI.

[2016] Step 6:

[2017] Generation AI:

[2018] The past behavioral history of the agent candidate (e.g., number of past redelivery attempts and success rate), attribute information (e.g., proximity to residence), and ratings from other users are analyzed.

[2019] An overall reliability score is calculated for each candidate.

[2020] Step 7:

[2021] server:

[2022] Select the most trustworthy agent based on their reliability score.

[2023] Step 8:

[2024] On the user's device:

[2025] When a user requests redelivery, they use text input or voice input.

[2026] Step 9:

[2027] Emotion engine (on the server):

[2028] It analyzes the user's text or voice input to determine their emotional state, for example, recognizing that the user is frustrated from the text "This package is delayed and I'm having trouble."

[2029] Step 10:

[2030] server:

[2031] Based on the user's perceived emotional state, the system adaptively adjusts redelivery requests, for example by prioritizing a more reliable agent for a particularly dissatisfied user.

[2032] Step 11:

[2033] server:

[2034] Send a redelivery request message to highly rated agents (e.g., to Agent A, "Please redeliver User B's package").

[2035] Step 12:

[2036] Agent's device:

[2037] The agent receives the redelivery request message and chooses whether to accept the request.

[2038] When the agent presses the accept button, this information is sent to the server.

[2039] Step 13:

[2040] server:

[2041] Verify that the agent has accepted the redelivery request.

[2042] Send a notification to the user saying "Redelivery will be carried out by a substitute."

[2043] Step 14:

[2044] Agent:

[2045] We will go to the specified address and redeliver the parcel.

[2046] Once the redelivery is complete, press the "Redelivery Complete" button on the app to send delivery completion information to the server.

[2047] Step 15:

[2048] server:

[2049] Upon receiving the delivery completion information, a completion notification is sent to the user stating that "the package has been delivered safely."

[2050] At the same time, a message requesting evaluation of the agent is sent to the user to collect feedback.

[2051] Step 16:

[2052] On the user's device:

[2053] Receive a review message for the redelivery and enter your feedback in the app and submit it.

[2054] Step 17:

[2055] server:

[2056] Save the feedback information from the user in a database.

[2057] The collected feedback information will be reflected in future reliability evaluations.

[2058] Example 2

[2059] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2060] Conventional redelivery systems did not efficiently process users' redelivery requests, and it was difficult to respond flexibly, taking into account the user's feelings and the urgency of the redelivery. Furthermore, there was insufficient evaluation of the reliability of the agent selection process, so the quality of redelivery was often not guaranteed. This led to problems such as lower user satisfaction and a loss of redelivery efficiency.

[2061] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2062] In this invention, the server includes a means for accepting redelivery requests, a means for generating a list of users in the surrounding area, a means for selecting agent candidates based on the user list, a means for evaluating the reliability of the agent candidates using a generation AI, a means for analyzing the user's emotional state and adjusting the content of the redelivery request, a means for sending a redelivery request based on the evaluation results and the user's emotional state, a means for receiving information that the redelivery has been completed, and a means for notifying the user of the redelivery completion information. This allows redelivery requests to be processed efficiently and quickly, and enables flexible responses based on the user's emotions. Furthermore, selecting a highly reliable agent improves the quality of redelivery and user satisfaction.

[2063] "Means for accepting redelivery requests" refers to a function that allows a user to input the necessary information when requesting redelivery and send it to the system.

[2064] "Means for generating a list of users in the surrounding area" refers to a function for collecting user information within an area where redelivery is possible and compiling it in list form.

[2065] "Means for selecting candidate agents" refers to a function for extracting people who can act as agents for redelivery from a list of users in the surrounding area and listing them as candidates.

[2066] "Means for evaluating trustworthiness using generative AI" refers to a function that uses generative AI to analyze the past behavioral history, attribute information, and evaluations from other users of a potential agent and calculate a trustworthiness score.

[2067] "Means for analyzing the user's emotional state and adjusting the content of the redelivery request" refers to a function for analyzing the user's emotional state from the content entered by the user and adaptively adjusting the priority and arrangement content of the redelivery request based on that information.

[2068] "Means for sending a redelivery request based on the evaluation results and emotional state" refers to a function for sending a redelivery request message to a selected agent based on the results of the reliability evaluation and emotional analysis.

[2069] "Means for receiving information on the completion of redelivery" refers to the function for reporting and receiving information to the system when an agent completes redelivery.

[2070] "Means for notifying the user of redelivery completion information" refers to a function for notifying the user of the completion information when redelivery is completed.

[2071] This invention is directed to a system that combines a new emotion engine that recognizes user emotions to improve the efficiency of redelivery. This system is implemented using the following hardware and software:

[2072] Server: AWS (Amazon Web Services) is used. The server receives redelivery request information from users, analyzes it, and generates a list of nearby users.

[2073] Generative AI model: OpenAI GPT-4 is used. Generative AI is used to evaluate the trustworthiness of potential agents.

[2074] Emotion engine: Using IBM Watson Natural Language Understanding, the emotion engine analyzes the emotional state of the user's input and provides that information to the server.

[2075] User device: Uses an iOS or Android device. The user device provides an interface for inputting and confirming redelivery requests, inputting emotional states, etc.

[2076] Specific examples of implementation

[2077] 1. Accepting delivery requests:

[2078] User device: The user launches the app and selects the redelivery request option. The user then enters the details of the redelivery request (size of the item, desired time of receipt, etc.) and presses the send button.

[2079] 2. Analyzing delivery requests and listing potential agents:

[2080] Server: Receives the information sent by the user and records it in a database. The server generates a list of users in areas where redelivery is possible based on the user's address information.

[2081] 3. Agent Credibility Assessment:

[2082] Server: Extracts proxy candidates from the generated user list and collects their profile information. Sends the collected information to the generation AI and requests a reliability evaluation.

[2083] Generative AI: Calculates a reliability score for each agent, based on, for example, the number of successful redeliveries in the past and the overall score of user ratings.

[2084] 4. Emotion engine recognizes user emotions:

[2085] User terminal: The user uses text or voice to input a redelivery request.

[2086] Emotion engine: Analyzes input text or voice and determines the user's emotional state (e.g., dissatisfied, satisfied, confused, etc.). Provides the analysis results to the server.

[2087] 5. Submission and notification of proxy requests:

[2088] Server: Based on the results of the reliability evaluation and sentiment analysis, the server sends a redelivery request message to the selected agent. The server notifies the user that the redelivery will be carried out by the agent.

[2089] 6. Redelivery and Completion Report:

[2090] Agent: Receives the request and carries out redelivery. Once redelivery is complete, presses the "Redelivery Completed" button to report to the server.

[2091] Server: Receives delivery completion information and sends a completion notification to the user. At the same time, it sends a message to the user requesting a rating of the agent and collects feedback.

[2092] Prompt sentences in specific examples

[2093] Below are some example prompts to input to a generative AI model:

[2094] "Nearby user list:

[2095] 1. User A - Past behavior history: All 10 redelivery requests were successful

[2096] 2. User B - Past behavior history: 5 successful redelivery requests out of 8

[2097] 3. User C - Past behavior history: 14 successful redelivery requests out of 15

[2098] Based on these candidates, calculate a credibility score for each."

[2099] In this way, the system efficiently processes users' redelivery requests and uses emotion recognition technology to select the most suitable agent, improving redelivery efficiency and user satisfaction.

[2100] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2101] Step 1:

[2102] User device: The user launches the app and selects the redelivery request option. The user then enters the details of the redelivery request (size of the item, desired time of receipt, etc.) and presses the send button.

[2103] Input: User's delivery details and desired time slot for receipt.

[2104] Output: User's redelivery request information.

[2105] Step 2:

[2106] Server: Receives delivery request information sent by users and records it in a database. Based on address information, it generates a list of users in an area where redelivery is possible (for example, within a 500-meter radius).

[2107] Input: User's redelivery request information, user's address information.

[2108] Output: A list of users in the surrounding area.

[2109] Step 3:

[2110] Server: Extracts proxy candidates from the generated user list and collects each candidate's profile information (past behavioral history, user ratings, attribute information, etc.). This information is sent to the generation AI and a reliability evaluation is requested.

[2111] Input: A list of patrons in the surrounding area.

[2112] Output: Profile information of potential agents.

[2113] Step 4:

[2114] Generative AI model: Analyzes the profile information of each agent candidate and calculates a reliability score.

[2115] Input: The profile information of the potential agent.

[2116] Output: A confidence score.

[2117] Step 5:

[2118] User terminal: The user inputs a redelivery request using text or voice. This input is sent to the emotion engine.

[2119] Input: User text or voice input.

[2120] Output: User sentiment analysis information.

[2121] Step 6:

[2122] Emotion engine: Analyzes input text or voice to determine the user's emotional state and provides this information to the server.

[2123] Input: User text or voice input.

[2124] Output: User's emotional state information.

[2125] Step 7:

[2126] Server: Adjusts the redelivery request based on the reliability score and emotional state, then sends the redelivery request message to the agent with the highest rating.

[2127] Input: Confidence score, user emotional state information.

[2128] Output: Adjusted redelivery request details and redelivery request message.

[2129] Step 8:

[2130] Server: Waits for information from the agent about whether the redelivery request has been accepted or rejected, and receives the result.

[2131] Input: Redelivery request message.

[2132] Output: The agent's acceptance or rejection information.

[2133] Step 9:

[2134] Server: If the agent accepts the redelivery request, it sends a notification to the user saying "Redelivery will be carried out by the agent." If not accepted, it sends the request to the next agent.

[2135] Input: Agent acceptance or denial information.

[2136] Output: Redelivery notification, message to resubmit to next agent.

[2137] Step 10:

[2138] Agent: Receives the request and carries out redelivery. Once redelivery is complete, presses the "Redelivery Completed" button to report to the server.

[2139] Input: Redelivery request details.

[2140] Output: Redelivery completion information.

[2141] Step 11:

[2142] Server: Receives the redelivery completion information and sends a completion notification to the user stating that "the package has been delivered safely." It also sends a message to the user requesting a rating for the agent and collects feedback.

[2143] Input: Redelivery completion information.

[2144] Output: Redelivery completion notification, rating request message, feedback.

[2145] (Application example 2)

[2146] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2147] It has been reported that redelivery and delivery delays have a significant impact on user satisfaction in food delivery services. In particular, when users feel anxious or uncomfortable due to delivery delays or redelivery, their evaluation of the service declines. Therefore, there is a need for systems that can properly recognize users' emotions and respond in a way that is most appropriate for their situation. Current systems do not adequately take users' emotions into consideration, and improving the efficiency of redelivery and user satisfaction remains a challenge.

[2148] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2149] In this invention, the server includes means for accepting redelivery requests, means for generating a list of users in the surrounding area, means for selecting agent candidates based on the user list, means for evaluating the reliability of the agent candidates using a generation AI, means for sending a redelivery request based on the evaluation results, means for receiving information that redelivery has been completed, means for notifying the user of the redelivery completion information, and means for selecting a delivery partner based on the user's emotional state, which includes an emotion engine that recognizes the user's emotional state when placing an order. This makes it possible to select the most appropriate delivery partner taking the user's emotions into consideration.

[2150] The "means for accepting a request for redelivery" is a system function that accepts a request for redelivery when a user desires redelivery.

[2151] The "means for generating a list of users in the surrounding area" is a system function for creating a list of users who live in the surrounding area of ​​the requester when a redelivery request is made.

[2152] The "means for selecting proxy candidates" is a system function that selects candidates who can act as proxy redelivery agents based on the generated user list.

[2153] "Means for evaluating trustworthiness using generation AI" refers to a system function for using generation AI to evaluate the trustworthiness of selected proxy candidates.

[2154] The "means for sending a redelivery request" is a system function that sends a redelivery request to the selected agent based on the evaluation results.

[2155] "Means for receiving information that redelivery has been completed" refers to a function by which the system receives information when the agent has completed redelivery.

[2156] The "means for notifying the user of redelivery completion information" is a system function for notifying the user that redelivery has been completed.

[2157] An "emotion engine" is an engine that analyzes and recognizes the emotional state from text and voice input by the user.

[2158] "Means for selecting a delivery partner based on emotional state" is a function of the system that selects the most suitable delivery partner based on the emotional state recognized by the emotion engine.

[2159] This invention provides a system that uses an emotion engine to recognize a user's emotion and take appropriate action to improve the efficiency of redelivery. Specific embodiments of this system are described in detail below.

[2160] System Configuration

[2161] The system of the present invention is comprised of the following major components:

[2162] 1. Server:

[2163] The server accepts redelivery requests, generates a user list, selects proxy candidates, evaluates their reliability, sends redelivery requests, and receives and notifies completion information.

[2164] An emotion engine also runs on the server and analyzes the user's emotional state.

[2165] 2. User Device:

[2166] The user terminal provides an interface for inputting a redelivery request and also has a text or voice input function for inputting an emotional state.

[2167] 3. Delivery partner terminal:

[2168] It provides an interface for delivery partners to receive redelivery request messages and execute redelivery. It also has a reporting function when redelivery is completed.

[2169] Program processing overview

[2170] Below is a natural language explanation of how the system works.

[2171] 1. Redelivery request acceptance:

[2172] The user inputs a redelivery request from the terminal, and the server receives and analyzes this information.

[2173] 2. Generate a user list:

[2174] The server generates a list of users in the surrounding area who may be able to assist with redelivery requests.

[2175] 3. Selection of potential agents:

[2176] The server selects proxy candidates from the generated user list.

[2177] 4. Reliability assessment:

[2178] Using generative AI, a reliability score is calculated based on the candidate agent's past behavioral history, attribute information, and evaluations from other users.

[2179] 5. Emotion Recognition with Emotion Engine:

[2180] The emotion engine analyzes the text and voice entered by the user when requesting redelivery and recognizes the user's emotional state.

[2181] 6. Submitting a redelivery request:

[2182] Based on the perceived emotional state and trustworthiness assessment, a redelivery request is sent to the most suitable agent.

[2183] 7. Receiving and notifying completion information:

[2184] Once the redelivery is complete, the delivery partner terminal sends the completion information to the server, which notifies the user and collects further feedback.

[2185] Hardware and software used

[2186] Hardware: Smartphones, servers

[2187] Software: EmotionEngine (emotion recognition engine), DeliveryService (delivery service), generative AI model

[2188] Specific examples

[2189] For example, if a user sends a redelivery request through a smartphone app saying, "I'm frustrated because the pizza I ordered hasn't arrived yet," the emotion engine will recognize the user's emotional state as "frustrated." Based on this information, the server will select a particularly reliable agent who can quickly redeliver the delivery. After the redelivery is completed, feedback such as "I was very satisfied with the speed of the delivery and the service" is collected and reflected in the next reliability evaluation.

[2190] Prompt Sentence Examples

[2191] If someone enters "I'm frustrated because the pizza I ordered hasn't arrived yet," recognize the user's emotion as "frustrated."

[2192] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2193] Step 1:

[2194] The user inputs and submits a redelivery request.

[2195] Input: The user enters a message, including a redelivery request and sentiment, via text or voice.

[2196] Data processing: The user terminal sends the input data in digital form to the server.

[2197] Output: The server receives this data and processes it as redelivery request data.

[2198] Specific actions: The user opens the app, selects "Request redelivery," enters a message, and presses the send button.

[2199] Step 2:

[2200] The server generates a list of users in the surrounding area.

[2201] Input: The server receives the redelivery request data and the requester's address information.

[2202] Data processing: The server generates a list of other users who live within a certain range based on the requester's address.

[2203] Output: A list of users in the surrounding area is generated.

[2204] Specific behavior: Searches the address database on the server and lists users who live within 500 meters.

[2205] Step 3:

[2206] The server selects candidates for the proxy.

[2207] Input: Receives a server-generated user list.

[2208] Data processing: Select candidates who are likely to be able to act as redelivery agents from the user list.

[2209] Output: A list of potential agents is generated.

[2210] Specific operation: The system refers to the user's past delivery history and ratings and selects candidates for redelivery.

[2211] Step 4:

[2212] The server evaluates the reliability of the proxy candidate.

[2213] Input: The server receives a list of candidate agents and past behavioral history and evaluation information for each candidate.

[2214] Data processing: A reliability evaluation algorithm is run and the generating AI calculates a reliability score.

[2215] Output: The reliability score for each agent candidate is output.

[2216] Specific operation: The server has the generation AI calculate the reliability score and saves the result.

[2217] Step 5:

[2218] The server uses an emotion engine to recognize the user's emotional state.

[2219] Input: The server receives a redelivery request message from the user.

[2220] Data processing: The server uses the emotion engine to analyze the message and recognize the emotional state.

[2221] Output: Multiple emotion tags are output as the user's emotional state.

[2222] What it does: Send the text "I'm frustrated because my pizza order hasn't arrived yet" to the emotion engine, which will recognize it as "frustrated."

[2223] Step 6:

[2224] The server sends a redelivery request based on the emotional state and reliability score.

[2225] Input: Receives emotional state information and a confidence score for each agent.

[2226] Data processing: The server determines the most suitable agent based on the emotional state and reliability score, and sends a redelivery request to that agent.

[2227] Output: A redelivery request message is sent to the selected agent.

[2228] Specific operation: The server will prioritize redelivery requests from highly reliable agents for users who are feeling "irritated."

[2229] Step 7:

[2230] The agent completes the redelivery and sends the completion information to the server.

[2231] Input: After the agent completes the redelivery, he / she presses the "Redelivery Complete" button.

[2232] Data processing: The device sends the completion information to the server.

[2233] Output: Redelivery completion information is received by the server.

[2234] Specific operation: The agent will redeliver the package to the specified address and, once complete, press a button on the app to report completion.

[2235] Step 8:

[2236] The server notifies the user of the redelivery completion information and collects feedback.

[2237] Input: Receive redelivery completion information.

[2238] Data processing: The server sends a completion notification to the user and provides a feedback collection interface.

[2239] Output: A completion notification and a feedback collection request is sent to the user.

[2240] Specific operation: The server sends a notification to the user that the package has been delivered safely, along with a message requesting a rating for the agent.

[2241] 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.

[2242] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[2243] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2244] 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.

[2245] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[2246] 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.

[2247] 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).

[2248] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2249] 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."

[2250] 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.

[2251] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2252] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2253] 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.

[2254] 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.

[2255] 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.

[2256] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[2257] The hardware resource that executes the specific processing 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 processing may be a single processor.

[2258] 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.

[2259] 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.

[2260] 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.

[2261] 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.

[2262] The following is further disclosed regarding the above embodiment.

[2263] (Claim 1)

[2264] A means for accepting redelivery requests;

[2265] means for generating a list of users in a surrounding area;

[2266] means for selecting a proxy candidate based on the user list;

[2267] A means for evaluating the reliability of the agent candidate by a generation AI;

[2268] means for sending a redelivery request based on the evaluation result;

[2269] A means of receiving information that redelivery has been completed;

[2270] means for notifying a user of redelivery completion information;

[2271] A system including:

[2272] (Claim 2)

[2273] 2. The system according to claim 1, wherein the reliability evaluation means calculates a reliability score based on the past behavior history, attribute information, and evaluations from other users of the agent candidate.

[2274] (Claim 3)

[2275] 2. The system according to claim 1, further comprising means for collecting feedback from the user following the redelivery completion notification, and reflecting the collected feedback in the next reliability evaluation.

[2276] "Example 1"

[2277] (Claim 1)

[2278] A means for accepting redelivery requests;

[2279] means for generating a list of users in the surrounding area;

[2280] means for selecting a proxy candidate based on the user list;

[2281] A means for evaluating the reliability of the agent candidate by a generation AI;

[2282] means for sending a redelivery request based on the evaluation result;

[2283] A means of receiving information that redelivery has been completed;

[2284] A means for notifying the user of redelivery completion information;

[2285] A means for transmitting information of the agent candidate to a generation AI;

[2286] a means for collecting details of potential agents and calculating a reliability score;

[2287] A means for the agent to redeliver and transmit completion information;

[2288] a means for sending the request to the next highest ranking agent based on the evaluation;

[2289] A system including:

[2290] (Claim 2)

[2291] 2. The system according to claim 1, wherein the reliability evaluation means calculates a reliability score based on the past behavior history, attribute information, and evaluations from other users of the agent candidate.

[2292] (Claim 3)

[2293] 2. The system according to claim 1, further comprising means for collecting feedback from the user following the redelivery completion notification, and reflecting the collected feedback in the next reliability evaluation.

[2294] "Application Example 1"

[2295] (Claim 1)

[2296] A means for accepting redelivery requests;

[2297] means for generating a list of users in a surrounding area;

[2298] means for selecting a proxy candidate based on the user list;

[2299] A means for evaluating the reliability of the agent candidate by a generation AI;

[2300] means for sending a redelivery request based on the evaluation result;

[2301] A means of receiving information that redelivery has been completed;

[2302] means for notifying a user of redelivery completion information;

[2303] A means for selecting a candidate agent from a list of delivery partners;

[2304] a means for using a generative AI model to select optimal proxy candidates;

[2305] A system including:

[2306] (Claim 2)

[2307] 2. The system according to claim 1, wherein the reliability evaluation means calculates a reliability score based on the past behavior history, attribute information, and evaluations from other users of the agent candidate.

[2308] (Claim 3)

[2309] 2. The system according to claim 1, further comprising means for collecting feedback from the user following the redelivery completion notification, and reflecting the collected feedback in the next reliability evaluation.

[2310] (Claim 4)

[2311] 10. The system of claim 1, further comprising means for generating prompt sentences for trustworthiness assessment using a generative AI model including optimal proxy candidates.

[2312] "Example 2: Combining Emotion Engines"

[2313] (Claim 1)

[2314] A means for accepting redelivery requests;

[2315] means for generating a list of users in the surrounding area;

[2316] means for selecting a proxy candidate based on the user list;

[2317] A means for evaluating the reliability of the agent candidate by a generation AI;

[2318] A means for analyzing the emotional state of the user and adjusting the content of the redelivery request;

[2319] means for sending a redelivery request based on the evaluation result and the emotional state;

[2320] A means of receiving information that redelivery has been completed;

[2321] A means for notifying the user of redelivery completion information;

[2322] A system including:

[2323] (Claim 2)

[2324] 2. The system according to claim 1, wherein the reliability evaluation means calculates a reliability score based on the past behavior history, attribute information, and evaluations from other users of the agent candidate.

[2325] (Claim 3)

[2326] 2. The system according to claim 1, further comprising means for collecting feedback from the user following the redelivery completion notification, and reflecting the collected feedback in the next reliability evaluation.

[2327] "Application example 2 when combining emotion engines"

[2328] (Claim 1)

[2329] A means for accepting redelivery requests;

[2330] means for generating a list of users in a surrounding area;

[2331] means for selecting a proxy candidate based on the user list;

[2332] A means for evaluating the reliability of the agent candidate by a generation AI;

[2333] means for sending a redelivery request based on the evaluation result;

[2334] A means of receiving information that redelivery has been completed;

[2335] means for notifying a user of redelivery completion information;

[2336] an emotion engine that recognizes the user's emotional state when placing an order, and means for selecting a delivery partner based on the user's emotional state;

[2337] A system including:

[2338] (Claim 2)

[2339] 2. The system according to claim 1, wherein the reliability evaluation means calculates a reliability score based on the past behavior history, attribute information, and evaluations from other users of the agent candidate.

[2340] (Claim 3)

[2341] 2. The system according to claim 1, further comprising means for collecting feedback from the user following the redelivery completion notification, and reflecting the collected feedback in the next reliability evaluation. [Explanation of symbols]

[2342] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for accepting redelivery requests; means for generating a list of users in a surrounding area; means for selecting a proxy candidate based on the user list; A means for evaluating the reliability of the agent candidate by a generation AI; means for sending a redelivery request based on the evaluation result; A means of receiving information that redelivery has been completed; means for notifying a user of redelivery completion information; A system including:

2. 2. The system according to claim 1, wherein the reliability evaluation means calculates a reliability score based on the past behavior history, attribute information, and evaluations from other users of the agent candidate.

3. 2. The system according to claim 1, further comprising means for collecting feedback from the user following the redelivery completion notification, and the collected feedback is reflected in the next reliability evaluation.

Images