System

Abstract

To realize more efficient communication of re-delivery or time rescheduling, thereby enabling a delivery driver to focus on his / her original task of delivery.SOLUTION: A system according to an embodiment includes: a reception unit, an analysis unit, an adjustment unit and a notification unit. The reception unit receives a user's request for re-delivery or time rescheduling. The analysis unit analyzes the request received by the reception unit. The adjustment unit adjusts a delivery schedule based on the request analyzed by the analysis unit. The notification unit notifies the user of the schedule adjusted by the adjustment unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, delivery drivers are responsible for negotiating redelivery and changes to delivery time specifications, which can reduce delivery efficiency.

[0005] The system of the embodiment aims to streamline the process of redelivery and time changes, allowing delivery drivers to focus on their actual deliveries. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an adjustment unit, and a notification unit. The reception unit receives a request for redelivery or a change in the designated delivery time from a user. The analysis unit analyzes the request received by the reception unit. The adjustment unit adjusts a delivery schedule based on the request analyzed by the analysis unit. The notification unit notifies the user of the schedule adjusted by the adjustment unit. Effect of the Invention

[0007] The system according to the embodiment can improve the efficiency of redelivery and time change negotiations, allowing delivery drivers to focus on their actual deliveries. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The interactive system according to the embodiment of the present invention is a system that improves the efficiency of redelivery and time designation change exchanges in the last mile of logistics. This interactive system is a mechanism that allows delivery drivers to focus resources on their original delivery work by combining a messenger app and a generation AI. First, a user sends a request for redelivery or time designation change through a messenger app. This request is accepted and analyzed by the generation AI. The generation AI understands the content of the user's request and automatically responds appropriately. For example, when a redelivery request is made, the generation AI checks the delivery schedule and proposes the optimal redelivery date and time. Similarly, when a time designation change request is made, the generation AI adjusts the schedule and notifies the user of the new delivery time. This system eliminates the need for delivery drivers to exchange redelivery or time designation changes, and allows them to concentrate on their original delivery work. Furthermore, since the generation AI automatically processes requests, interactions with users are performed quickly and efficiently. For example, a user sends a request for redelivery or time designation change through a messenger app. The generation AI accepts and analyzes the request. The generation AI checks the delivery schedule and proposes the optimal redelivery date and time or a new delivery time. The user is notified of the proposed content. The delivery driver makes the delivery based on the schedule adjustments made by the generative AI. This system reduces the burden on the delivery driver and improves delivery efficiency. It also enables users to respond quickly and flexibly, improving their satisfaction. In this way, the dialogue system can reduce the burden on delivery drivers and improve delivery efficiency.

[0029] The interactive system according to the embodiment includes a reception unit, an analysis unit, an adjustment unit, and a notification unit. The reception unit receives a request for redelivery or a change in the designated time from a user. The request from the user includes, but is not limited to, a change in the date and time of redelivery and a change in the delivery destination, for example. The reception unit receives a request from the user through, for example, a messenger app. The messenger app includes, but is not limited to, LINE (registered trademark), WhatsApp (registered trademark), Facebook (registered trademark) Messenger, and the like. The analysis unit uses a generation AI to analyze the request received by the reception unit. The analysis is performed, for example, based on a classification method and an analysis algorithm of the request content, but is not limited to the example. For example, the generation AI analyzes the request using a natural language processing model (for example, GPT-4 (registered trademark), Gemini, etc.) and determines an appropriate response. The adjustment unit uses the generation AI to adjust the delivery schedule based on the request analyzed by the analysis unit. The adjustment is performed, for example, based on a schedule optimization method and an adjustment algorithm, but is not limited to the example. For example, the generation AI proposes an optimal redelivery date and time or a new delivery time, taking into consideration the user's desired time zone and the delivery driver's schedule. The notification unit uses the generation AI to notify the user of the schedule adjusted by the adjustment unit. The notification is performed by means of, for example, email, SMS, push notification, etc., but is not limited to such examples. For example, the generation AI promptly and accurately notifies the user of the proposed content. As a result, the interactive system according to the embodiment can efficiently accept, analyze, adjust, and notify requests from users, thereby reducing the burden on delivery drivers and improving delivery efficiency.

[0030] The reception unit receives requests from the user for redelivery or time designation change. The user's requests include, but are not limited to, for example, a change in the date and time of redelivery or a change in the delivery destination. The reception unit receives requests from the user through, for example, a messenger app. The messenger app includes, but is not limited to, LINE, WhatsApp, Facebook Messenger, etc. Specifically, the reception unit receives text messages and voice messages sent by the user through the messenger app and processes them within the system. If the user wishes to change the date and time of redelivery, the user can input the desired date and time through the interface of the messenger app. Also, if the user wishes to change the delivery destination, the user can similarly input the address of the new delivery destination. When the reception unit receives these requests, it immediately records them in the database within the system and proceeds to the next processing step. Furthermore, the reception unit can utilize pre-set templates and automatic response functions to smoothly proceed with the dialogue with the user. For example, when the user sends a request, the reception unit automatically returns a confirmation message and notifies the user that the request has been successfully accepted. This allows the user to confirm that his or her request has been reliably reflected in the system. The reception unit plays a role in improving the efficiency of the entire system by quickly and accurately receiving requests from users.

[0031] The analysis unit uses the generation AI to analyze the request received by the reception unit. The analysis is performed, for example, based on a classification method of the request content and an analysis algorithm, but is not limited to such examples. Specifically, the generation AI analyzes the request using a natural language processing model (for example, GPT-4, Gemini, etc.) and determines an appropriate response. The generation AI takes in the request message from the user as text data and analyzes its contents. For example, if the request is to change the date and time of redelivery, the generation AI extracts the date and time information in the message and converts it into an appropriate format. In addition, in the case of a request to change the delivery destination, new address information is extracted and registered in the database. Based on these analysis results, the generation AI determines the type and priority of the request and issues instructions to proceed to the next processing step. Furthermore, the analysis unit can analyze the trends and patterns of requests by utilizing past request data and user history information. This enables the system to predict user behavior and provide faster and more appropriate responses. For example, if a specific user frequently requests redelivery, a proactive response such as sending a reminder to that user in advance can be considered. The analysis unit utilizes the advanced analytical capabilities of the generative AI to efficiently process user requests and improve overall system performance.

[0032] The adjustment unit uses the generation AI to adjust the delivery schedule based on the request analyzed by the analysis unit. The adjustment is performed, for example, based on a schedule optimization method or an adjustment algorithm, but is not limited to such examples. Specifically, the generation AI proposes the optimal redelivery date and time or a new delivery time, taking into account the user's desired time zone and the delivery driver's schedule. The generation AI first analyzes the user's desired redelivery date and time and checks whether the date and time match the delivery driver's schedule. Next, based on the delivery driver's schedule and route information, calculates the optimal delivery route and proposes an efficient schedule. For example, if multiple users wish to redeliver to the same area, the generation AI consolidates these requests and calculates the most efficient route, thereby reducing the burden on the delivery driver. In addition, the adjustment unit can dynamically adjust the schedule, taking into account traffic and weather information updated in real time. For example, if traffic congestion or bad weather is predicted, the generation AI recalculates the schedule based on this information and proposes a new delivery time to the user. In addition, the adjustment unit can make more personalized suggestions, taking into account the user's past request history and behavior patterns. This enables the adjustment department to provide flexible responses according to user needs and maximize delivery efficiency.

[0033] The notification unit uses the generation AI to notify the user of the schedule adjusted by the adjustment unit. The notification is made by means of, for example, email, SMS, push notification, etc., but is not limited to such examples. Specifically, the generation AI promptly and accurately notifies the user of the proposed content. The generation AI generates an appropriate notification message for the user based on the new schedule information provided by the adjustment unit. For example, if the date and time of redelivery is changed, the generation AI creates a notification message including that information and sends it through the notification means desired by the user. In the case of email notification, the generation AI sends detailed schedule information to the user's email address, and in the case of SMS notification, important information is conveyed in the form of a short message. In the case of push notification, notification can be made in real time through a smartphone app. In addition, the notification unit also has a function to accept feedback from the user, and the user can request approval or changes to the proposed schedule. For example, if the user agrees with the proposed redelivery date and time, approval is completed by simply clicking the link in the notification message. In addition, if the user wants to change the date and time again, he or she can send a new request through the link in the notification message. As a result, the notification unit provides the user with quick and accurate information, improving user convenience and enhancing the efficiency of the entire system.

[0034] The reception unit can receive a request from a user through a messenger app. Examples of the messenger app include, but are not limited to, LINE, WhatsApp, and Facebook Messenger. For example, the reception unit receives a request from a user to send a redelivery request or a change in the designated delivery time through a messenger app. This allows the user to easily send a request by receiving the request through the messenger app. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input the request received through the messenger app to the generation AI and have the generation AI execute the reception of the request.

[0035] The analysis unit can analyze the request using the generation AI and determine an appropriate response. The generation AI includes, for example, a natural language processing model (for example, GPT-4, Gemini, etc.), but is not limited to such examples. The analysis unit, for example, uses the generation AI to analyze the request accepted by the acceptance unit. The analysis is performed, for example, based on a classification method of the request content and an analysis algorithm. For example, the generation AI analyzes the request content and determines an appropriate response. As a result, by using the generation AI, the accuracy of the analysis of the request is improved, and an appropriate response is possible. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input a request to the generation AI and have the generation AI execute the analysis of the request.

[0036] The adjustment unit can check the delivery schedule using the generation AI and propose a redelivery date and time or a new delivery time. The redelivery date and time or the new delivery time includes, for example, the user's desired time zone and the delivery driver's schedule, but is not limited to such examples. The adjustment unit adjusts the delivery schedule based on the request analyzed by the analysis unit, for example, using the generation AI. The adjustment is performed, for example, based on a schedule optimization method or an adjustment algorithm. For example, the generation AI considers the user's desired time zone and the delivery driver's schedule to propose an optimal redelivery date and time or a new delivery time. This makes it possible to adjust the schedule optimally by using the generation AI. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the delivery schedule to the generation AI and cause the generation AI to adjust the schedule.

[0037] The notification unit can notify the user of the proposed contents by the generation AI. The proposed contents include, for example, redelivery date and time, changes to the delivery destination, etc., but are not limited to such examples. The notification unit notifies the user of the schedule adjusted by the adjustment unit, for example, by using the generation AI. The notification is performed by means of, for example, email, SMS, push notification, etc. For example, the generation AI notifies the user of the proposed contents quickly and accurately. As a result, by using the generation AI, the notification to the user is performed quickly and accurately. A part or all of the above-mentioned processing in the notification unit may be performed, for example, by using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the proposed contents to the generation AI and have the generation AI execute the notification.

[0038] The reception unit can analyze the user's past request history and select the optimal reception method. The reception unit, for example, analyzes the user's past request history and selects the optimal reception method. The past request history includes, for example, past redelivery requests, time designation change requests, etc., but is not limited to such examples. For example, the reception unit automatically displays contents that the user frequently requested in the past as candidates. In addition, it is also possible to preferentially suggest reception methods (voice, text, etc.) that the user used in the past. In addition, it is also possible to predict and suggest request contents to be used in a specific time period from the user's past request history. In this way, by analyzing the past request history, it is possible to provide the user with the optimal reception method. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the past request history to the generation AI and cause the generation AI to select the optimal reception method.

[0039] The reception unit can perform filtering based on the user's current situation and field of interest when receiving a request. The reception unit, for example, performs filtering based on the user's current situation and field of interest when receiving a request. The current situation and field of interest include, for example, a current geographical location, a product category of interest, and the like, but are not limited to such examples. For example, the reception unit preferentially displays related request contents when the user inputs the current situation. In addition, the request content can be customized based on the user's field of interest. In addition, the optimal request content can be proposed according to the user's current situation. In this way, by filtering the request based on the user's situation and field of interest, a more appropriate request can be received. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data of the user's current situation and field of interest to the generation AI and cause the generation AI to perform filtering.

[0040] The reception unit can preferentially receive requests with high relevance in consideration of the geographical location information of the user when receiving a request. For example, the reception unit preferentially receives requests with high relevance in consideration of the geographical location information of the user when receiving a request. The geographical location information includes, for example, the user's address, current location, etc., but is not limited to such examples. For example, when the user is in a specific area, requests related to that area are preferentially received. In addition, requests related to places close to the user's current location can be preferentially received. In addition, the optimal request content can be proposed based on the geographical location information of the user. As a result, by considering the geographical location information of the user, requests with high relevance can be preferentially received. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the reception of the request.

[0041] The reception unit can analyze the user's social media activity at the time of receiving the request and receive the related request. For example, the reception unit analyzes the user's social media activity at the time of receiving the request and receives the related request. The social media activity includes, for example, the posting content, the history of likes, and the like, but is not limited to such examples. For example, the current interests of the user are analyzed from the social media activity of the user, and a related request is proposed. In addition, the request content can be customized based on the posting content of the user on social media. In addition, the optimal request content can be proposed with reference to the social media activity of the user. In this way, the related request can be appropriately received by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the reception of the request.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the request when analyzing the request. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the request when analyzing the request. The importance of the request includes, for example, the urgency of the user, the contents of the request, and the like, but is not limited to such examples. For example, a request with high importance is analyzed in detail. In addition, a request with low importance can be analyzed briefly. In addition, the priority of the analysis can be determined according to the importance. As a result, by adjusting the level of detail of the analysis based on the importance of the request, efficient analysis is possible. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance data of the request to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the request when analyzing the request. For example, the analysis unit applies different analysis algorithms depending on the category of the request when analyzing the request. The request category includes, for example, redelivery, time designation change, etc., but is not limited to such examples. For example, a specific algorithm is applied to a redelivery request. Also, a different algorithm can be applied to a time designation change request. Also, the optimal analysis algorithm can be selected depending on the category of the request. As a result, the analysis accuracy is improved by applying the optimal analysis algorithm depending on the category of the request. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input request category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the time of submission of the request when analyzing the request. The analysis unit, for example, determines the priority of analysis based on the time of submission of the request when analyzing the request. The time of submission of the request includes, for example, the submission date and time, the submission time zone, etc., but is not limited to such examples. For example, the priority is determined based on the time zone in which the request is submitted. In addition, the level of detail of the analysis can be adjusted according to the time of submission of the request. In addition, the optimal analysis method can be selected based on the time of submission. As a result, efficient analysis is possible by determining the priority of analysis based on the time of submission of the request. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the submission time data of the request to the generation AI and cause the generation AI to execute the determination of the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the request when analyzing the request. The analysis unit, for example, adjusts the order of analysis based on the relevance of the request when analyzing the request. The relevance of the request includes, for example, the similarity of the request content, the user's past request history, and the like, but is not limited to such examples. For example, highly relevant requests are analyzed preferentially. In addition, requests with low relevance can be postponed. In addition, the order of analysis can be determined based on the relevance of the request. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of the request. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the relevance data of the request to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The adjustment unit can select an optimal adjustment method by referring to past schedule data when adjusting a schedule. The adjustment unit, for example, selects an optimal adjustment method by referring to past schedule data when adjusting a schedule. The past schedule data includes, for example, past delivery history, data on delivery time zones, etc., but is not limited to such examples. For example, an optimal redelivery date and time can be proposed based on the past schedule data. Also, an optimal time designation change can be proposed from the past schedule data. Also, the past schedule data can be analyzed to select the most efficient schedule adjustment method. As a result, optimal schedule adjustment can be made by referring to the past schedule data. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input past schedule data to the generation AI and cause the generation AI to select an adjustment method.

[0047] The adjustment unit can customize the adjustment means based on the contents of the request when adjusting the schedule. The adjustment unit customizes the adjustment means based on the contents of the request when adjusting the schedule, for example. The adjustment means include, but are not limited to, for example, changing the delivery route and adjusting the delivery time slot. For example, a specific adjustment means is applied to a redelivery request. Also, a different adjustment means can be applied to a time designation change request. Also, the optimal adjustment means can be selected according to the contents of the request. This improves the adjustment accuracy by applying the optimal adjustment means according to the contents of the request. Part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input content data of the request to the generation AI and cause the generation AI to execute customization of the adjustment means.

[0048] The adjustment unit can select an optimal adjustment method in consideration of the geographical location information of the user when adjusting the schedule. The adjustment unit, for example, selects an optimal adjustment method in consideration of the geographical location information of the user when adjusting the schedule. The geographical location information includes, for example, the user's address, current location, etc., but is not limited to such examples. For example, when the user is in a specific area, schedule adjustment related to that area is performed preferentially. In addition, schedule adjustment related to a place close to the user's current location can also be performed preferentially. In addition, an optimal schedule adjustment method can be selected based on the geographical location information of the user. This makes it possible to perform optimal schedule adjustment by considering the geographical location information of the user. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input the geographical location information of the user to the generation AI and cause the generation AI to select an adjustment method.

[0049] The adjustment unit can analyze the user's social media activity and propose a means of adjustment when adjusting a schedule. The adjustment unit, for example, analyzes the user's social media activity and proposes a means of adjustment when adjusting a schedule. Social media activity includes, for example, posting content, history of likes, and the like, but is not limited to such examples. For example, the current interests of the user are analyzed from the social media activity of the user, and related schedule adjustments are proposed. In addition, the means of schedule adjustment can be customized based on the content of the user's social media posts. In addition, the optimal schedule adjustment means can be proposed with reference to the user's social media activity. In this way, by analyzing the user's social media activity, related schedule adjustment means can be proposed. Part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the proposal of the adjustment means.

[0050] The notification unit can select the optimal notification method by referring to the user's past notification history at the time of notification. The notification unit, for example, selects the optimal notification method by referring to the user's past notification history at the time of notification. The past notification history includes, for example, past notification contents, notification means, etc., but is not limited to such examples. For example, the notification method that the user has used in the past with preference is provided. In addition, the optimal notification method for a specific time period can be proposed from the user's past notification history. In addition, the user's past notification history can be analyzed and the most effective notification method can be selected. As a result, the optimal notification method can be provided to the user by referring to the past notification history. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input past notification history data to the generation AI and cause the generation AI to select the notification method.

[0051] The notification unit can customize the notification means based on the contents of the request at the time of notification. The notification unit, for example, customizes the notification means based on the contents of the request at the time of notification. Notification means include, for example, email, SMS, push notification, etc., but are not limited to such examples. For example, a specific notification means is applied to a redelivery request. Also, a different notification means can be applied to a time designation change request. Also, the optimal notification means can be selected depending on the contents of the request. This improves the notification accuracy by applying the optimal notification means depending on the contents of the request. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input content data of the request to the generation AI and have the generation AI execute customization of the notification means.

[0052] The notification unit can select the optimal notification method in consideration of the geographical location information of the user at the time of notification. The notification unit, for example, selects the optimal notification method in consideration of the geographical location information of the user at the time of notification. The geographical location information includes, for example, the user's address, current location, etc., but is not limited to such examples. For example, when the user is in a specific area, a notification method related to that area is preferentially provided. In addition, a notification method related to a place close to the user's current location can also be preferentially provided. In addition, the optimal notification method can be selected based on the geographical location information of the user. In this way, the optimal notification method can be provided by considering the geographical location information of the user. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the notification method.

[0053] The notification unit can analyze the user's social media activity at the time of notification and suggest a means of notification. The notification unit, for example, analyzes the user's social media activity at the time of notification and suggests a means of notification. The social media activity includes, for example, the content of posts, the history of likes, and the like, but is not limited to such examples. For example, the current interests of the user are analyzed from the social media activity of the user, and a related notification method is suggested. The notification means can also be customized based on the content of the user's posts on social media. The optimal notification means can also be suggested with reference to the user's social media activity. In this way, the related notification means can be suggested by analyzing the user's social media activity. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the user's social media activity data to the generation AI and have the generation AI execute the suggestion of the notification means.

[0054] The notification unit can analyze the user's social media activity at the time of notification and suggest a means of notification. The notification unit, for example, analyzes the user's social media activity at the time of notification and suggests a means of notification. The social media activity includes, for example, the content of posts, the history of likes, and the like, but is not limited to such examples. For example, the current interests of the user are analyzed from the social media activity of the user, and a related notification method is suggested. The notification means can also be customized based on the content of the user's posts on social media. The optimal notification means can also be suggested with reference to the user's social media activity. In this way, the related notification means can be suggested by analyzing the user's social media activity. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the user's social media activity data to the generation AI and have the generation AI execute the suggestion of the notification means.

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

[0056] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the contents of requests that the user has frequently made in the past can be automatically displayed as candidates. It can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the request contents to be used during a specific time period from the user's past request history. In this way, it is possible to provide the optimal reception method for the user by analyzing the past request history.

[0057] When analyzing a request, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, requests with high importance can be analyzed in detail, and requests with low importance can be analyzed briefly. In addition, the analysis priority can be determined according to the importance. In this way, efficient analysis is possible by adjusting the level of detail of the analysis based on the importance of the request.

[0058] When adjusting a schedule, the adjustment unit can refer to past schedule data and select the optimal adjustment method. For example, the adjustment unit can suggest the optimal redelivery date and time based on past schedule data. The adjustment unit can also suggest the optimal time designation change from past schedule data. Furthermore, the adjustment unit can analyze past schedule data and select the most efficient schedule adjustment method. This makes it possible to make optimal schedule adjustments by referring to past schedule data.

[0059] At the time of notification, the notification unit can select the optimal notification method by referring to the user's past notification history. For example, the notification method that the user has used in the past as a favorite is preferentially provided. Also, the notification unit can suggest the optimal notification method for a specific time period based on the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and select the most effective notification method. In this way, the optimal notification method can be provided to the user by referring to the past notification history.

[0060] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving a request. For example, when the user inputs the current situation, related request contents are preferentially displayed. In addition, the request contents can be customized based on the user's areas of interest. Furthermore, optimal request contents can be proposed according to the user's current situation. In this way, more appropriate requests can be received by filtering requests based on the user's situation and areas of interest.

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

[0062] Step 1: The reception unit receives a request from the user for redelivery or a change in the designated delivery time. Requests from the user include changing the date and time of redelivery or changing the delivery destination. The reception unit receives the request from the user, for example, through a messenger app (LINE, WhatsApp, Facebook Messenger, etc.). Step 2: The analysis unit uses the generation AI to analyze the request received by the reception unit. The analysis is performed based on the classification method of the request content and the analysis algorithm. For example, the generation AI analyzes the request using a natural language processing model (GPT-4, Gemini, etc.) and determines the appropriate response. Step 3: The adjustment unit uses the generation AI to adjust the delivery schedule based on the request analyzed by the analysis unit. The adjustment is made based on the schedule optimization method and adjustment algorithm. For example, the generation AI considers the user's desired time zone and the delivery driver's schedule to propose the optimal redelivery date and time or a new delivery time. Step 4: The notification unit uses the generation AI to notify the user of the schedule adjusted by the adjustment unit. The notification is performed by means of email, SMS, push notification, etc. For example, the generation AI notifies the user of the proposed content quickly and accurately.

[0063] (Example 2) The interactive system according to the embodiment of the present invention is a system that improves the efficiency of redelivery and time designation change exchanges in the last mile of logistics. This interactive system is a mechanism that allows delivery drivers to focus resources on their original delivery work by combining a messenger app and a generation AI. First, a user sends a request for redelivery or time designation change through a messenger app. This request is accepted and analyzed by the generation AI. The generation AI understands the content of the user's request and automatically responds appropriately. For example, when a redelivery request is made, the generation AI checks the delivery schedule and proposes the optimal redelivery date and time. Similarly, when a time designation change request is made, the generation AI adjusts the schedule and notifies the user of the new delivery time. This system eliminates the need for delivery drivers to exchange redelivery or time designation changes, and allows them to concentrate on their original delivery work. Furthermore, since the generation AI automatically processes requests, interactions with users are performed quickly and efficiently. For example, a user sends a request for redelivery or time designation change through a messenger app. The generation AI accepts and analyzes the request. The generation AI checks the delivery schedule and proposes the optimal redelivery date and time or a new delivery time. The user is notified of the proposed content. The delivery driver makes the delivery based on the schedule adjustments made by the generative AI. This system reduces the burden on the delivery driver and improves delivery efficiency. It also enables users to respond quickly and flexibly, improving their satisfaction. In this way, the dialogue system can reduce the burden on delivery drivers and improve delivery efficiency.

[0064] The interactive system according to the embodiment includes a reception unit, an analysis unit, an adjustment unit, and a notification unit. The reception unit receives a request for redelivery or a change in the designated time from a user. The request from the user includes, but is not limited to, a change in the date and time of redelivery and a change in the delivery destination, for example. The reception unit receives a request from the user through, for example, a messenger app. The messenger app includes, but is not limited to, LINE, WhatsApp, Facebook Messenger, and the like. The analysis unit uses a generation AI to analyze the request received by the reception unit. The analysis is performed, for example, based on a classification method and an analysis algorithm of the request content, but is not limited to the example. For example, the generation AI analyzes the request using a natural language processing model (for example, GPT-4, Gemini, etc.) and determines an appropriate response. The adjustment unit uses the generation AI to adjust the delivery schedule based on the request analyzed by the analysis unit. The adjustment is performed, for example, based on a schedule optimization method and an adjustment algorithm, but is not limited to the example. For example, the generation AI proposes an optimal redelivery date and time or a new delivery time, taking into consideration the user's desired time zone and the delivery driver's schedule. The notification unit uses the generation AI to notify the user of the schedule adjusted by the adjustment unit. The notification is performed by means of, for example, email, SMS, push notification, etc., but is not limited to such examples. For example, the generation AI promptly and accurately notifies the user of the proposed content. As a result, the interactive system according to the embodiment can efficiently accept, analyze, adjust, and notify requests from users, thereby reducing the burden on delivery drivers and improving delivery efficiency.

[0065] The reception unit receives requests from the user for redelivery or time designation change. The user's requests include, but are not limited to, for example, a change in the date and time of redelivery or a change in the delivery destination. The reception unit receives requests from the user through, for example, a messenger app. The messenger app includes, but is not limited to, LINE, WhatsApp, Facebook Messenger, etc. Specifically, the reception unit receives text messages and voice messages sent by the user through the messenger app and processes them within the system. If the user wishes to change the date and time of redelivery, the user can input the desired date and time through the interface of the messenger app. Also, if the user wishes to change the delivery destination, the user can similarly input the address of the new delivery destination. When the reception unit receives these requests, it immediately records them in the database within the system and proceeds to the next processing step. Furthermore, the reception unit can utilize pre-set templates and automatic response functions to smoothly proceed with the dialogue with the user. For example, when the user sends a request, the reception unit automatically returns a confirmation message and notifies the user that the request has been successfully accepted. This allows the user to confirm that his or her request has been reliably reflected in the system. The reception unit plays a role in improving the efficiency of the entire system by quickly and accurately receiving requests from users.

[0066] The analysis unit uses the generation AI to analyze the request received by the reception unit. The analysis is performed, for example, based on a classification method of the request content and an analysis algorithm, but is not limited to such examples. Specifically, the generation AI analyzes the request using a natural language processing model (for example, GPT-4, Gemini, etc.) and determines an appropriate response. The generation AI takes in the request message from the user as text data and analyzes its contents. For example, if the request is to change the date and time of redelivery, the generation AI extracts the date and time information in the message and converts it into an appropriate format. In addition, in the case of a request to change the delivery destination, new address information is extracted and registered in the database. Based on these analysis results, the generation AI determines the type and priority of the request and issues instructions to proceed to the next processing step. Furthermore, the analysis unit can analyze the trends and patterns of requests by utilizing past request data and user history information. This enables the system to predict user behavior and provide faster and more appropriate responses. For example, if a specific user frequently requests redelivery, a proactive response such as sending a reminder to that user in advance can be considered. The analysis unit utilizes the advanced analytical capabilities of the generative AI to efficiently process user requests and improve overall system performance.

[0067] The adjustment unit uses the generation AI to adjust the delivery schedule based on the request analyzed by the analysis unit. The adjustment is performed, for example, based on a schedule optimization method or an adjustment algorithm, but is not limited to such examples. Specifically, the generation AI proposes the optimal redelivery date and time or a new delivery time, taking into account the user's desired time zone and the delivery driver's schedule. The generation AI first analyzes the user's desired redelivery date and time and checks whether the date and time match the delivery driver's schedule. Next, based on the delivery driver's schedule and route information, calculates the optimal delivery route and proposes an efficient schedule. For example, if multiple users wish to redeliver to the same area, the generation AI consolidates these requests and calculates the most efficient route, thereby reducing the burden on the delivery driver. In addition, the adjustment unit can dynamically adjust the schedule, taking into account traffic and weather information updated in real time. For example, if traffic congestion or bad weather is predicted, the generation AI recalculates the schedule based on this information and proposes a new delivery time to the user. In addition, the adjustment unit can make more personalized suggestions, taking into account the user's past request history and behavior patterns. This enables the adjustment department to provide flexible responses according to user needs and maximize delivery efficiency.

[0068] The notification unit uses the generation AI to notify the user of the schedule adjusted by the adjustment unit. The notification is made by means of, for example, email, SMS, push notification, etc., but is not limited to such examples. Specifically, the generation AI promptly and accurately notifies the user of the proposed content. The generation AI generates an appropriate notification message for the user based on the new schedule information provided by the adjustment unit. For example, if the date and time of redelivery is changed, the generation AI creates a notification message including that information and sends it through the notification means desired by the user. In the case of email notification, the generation AI sends detailed schedule information to the user's email address, and in the case of SMS notification, important information is conveyed in the form of a short message. In the case of push notification, notification can be made in real time through a smartphone app. In addition, the notification unit also has a function to accept feedback from the user, and the user can request approval or changes to the proposed schedule. For example, if the user agrees with the proposed redelivery date and time, approval is completed by simply clicking the link in the notification message. In addition, if the user wants to change the date and time again, he or she can send a new request through the link in the notification message. As a result, the notification unit provides the user with quick and accurate information, improving user convenience and enhancing the efficiency of the entire system.

[0069] The reception unit can receive a request from a user through a messenger app. Examples of the messenger app include, but are not limited to, LINE, WhatsApp, and Facebook Messenger. For example, the reception unit receives a request from a user to send a redelivery request or a change in the designated delivery time through a messenger app. This allows the user to easily send a request by receiving the request through the messenger app. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input the request received through the messenger app to the generation AI and have the generation AI execute the reception of the request.

[0070] The analysis unit can analyze the request using the generation AI and determine an appropriate response. The generation AI includes, for example, a natural language processing model (for example, GPT-4, Gemini, etc.), but is not limited to such examples. The analysis unit, for example, uses the generation AI to analyze the request accepted by the acceptance unit. The analysis is performed, for example, based on a classification method of the request content and an analysis algorithm. For example, the generation AI analyzes the request content and determines an appropriate response. As a result, by using the generation AI, the accuracy of the analysis of the request is improved, and an appropriate response is possible. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input a request to the generation AI and have the generation AI execute the analysis of the request.

[0071] The adjustment unit can check the delivery schedule using the generation AI and propose a redelivery date and time or a new delivery time. The redelivery date and time or the new delivery time includes, for example, the user's desired time zone and the delivery driver's schedule, but is not limited to such examples. The adjustment unit adjusts the delivery schedule based on the request analyzed by the analysis unit, for example, using the generation AI. The adjustment is performed, for example, based on a schedule optimization method or an adjustment algorithm. For example, the generation AI considers the user's desired time zone and the delivery driver's schedule to propose an optimal redelivery date and time or a new delivery time. This makes it possible to adjust the schedule optimally by using the generation AI. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the delivery schedule to the generation AI and cause the generation AI to adjust the schedule.

[0072] The notification unit can notify the user of the proposed contents by the generation AI. The proposed contents include, for example, redelivery date and time, changes to the delivery destination, etc., but are not limited to such examples. The notification unit notifies the user of the schedule adjusted by the adjustment unit, for example, by using the generation AI. The notification is performed by means of, for example, email, SMS, push notification, etc. For example, the generation AI notifies the user of the proposed contents quickly and accurately. As a result, by using the generation AI, the notification to the user is performed quickly and accurately. A part or all of the above-mentioned processing in the notification unit may be performed, for example, by using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the proposed contents to the generation AI and have the generation AI execute the notification.

[0073] The reception unit can estimate the user's emotion and adjust the request reception method based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the request reception method based on the estimated user's emotion. For example, when the user is stressed, a simple interface is provided and input procedures are minimized. Also, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Also, when the user is in a hurry, voice input can be prioritized and requests can be quickly accepted. This improves the user's satisfaction by adjusting the request reception method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0074] The reception unit can analyze the user's past request history and select the optimal reception method. The reception unit, for example, analyzes the user's past request history and selects the optimal reception method. The past request history includes, for example, past redelivery requests, time designation change requests, etc., but is not limited to such examples. For example, the reception unit automatically displays contents that the user frequently requested in the past as candidates. In addition, it is also possible to preferentially suggest reception methods (voice, text, etc.) that the user used in the past. In addition, it is also possible to predict and suggest request contents to be used in a specific time period from the user's past request history. In this way, by analyzing the past request history, it is possible to provide the user with the optimal reception method. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the past request history to the generation AI and cause the generation AI to select the optimal reception method.

[0075] The reception unit can perform filtering based on the user's current situation and field of interest when receiving a request. The reception unit, for example, performs filtering based on the user's current situation and field of interest when receiving a request. The current situation and field of interest include, for example, a current geographical location, a product category of interest, and the like, but are not limited to such examples. For example, the reception unit preferentially displays related request contents when the user inputs the current situation. In addition, the request content can be customized based on the user's field of interest. In addition, the optimal request content can be proposed according to the user's current situation. In this way, by filtering the request based on the user's situation and field of interest, a more appropriate request can be received. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data of the user's current situation and field of interest to the generation AI and cause the generation AI to perform filtering.

[0076] The reception unit can estimate the user's emotion and determine the priority of the received request based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the received request based on the estimated user's emotion. The priority of the request includes, for example, urgency and importance, but is not limited to such examples. For example, when the user transmits an urgent request, the request is processed preferentially. In addition, when the user is relaxed, the request can be processed with normal priority. In addition, when the user is stressed, the request can be processed quickly. In this way, by determining the priority of the request according to the user's emotion, the request with high urgency can be processed preferentially. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the request.

[0077] The reception unit can preferentially receive requests with high relevance in consideration of the geographical location information of the user when receiving a request. For example, the reception unit preferentially receives requests with high relevance in consideration of the geographical location information of the user when receiving a request. The geographical location information includes, for example, the user's address, current location, etc., but is not limited to such examples. For example, when the user is in a specific area, requests related to that area are preferentially received. In addition, requests related to places close to the user's current location can be preferentially received. In addition, the optimal request content can be proposed based on the geographical location information of the user. As a result, by considering the geographical location information of the user, requests with high relevance can be preferentially received. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the reception of the request.

[0078] The reception unit can analyze the user's social media activity at the time of receiving the request and receive the related request. For example, the reception unit analyzes the user's social media activity at the time of receiving the request and receives the related request. The social media activity includes, for example, the posting content, the history of likes, and the like, but is not limited to such examples. For example, the current interests of the user are analyzed from the social media activity of the user, and a related request is proposed. In addition, the request content can be customized based on the posting content of the user on social media. In addition, the optimal request content can be proposed with reference to the social media activity of the user. In this way, the related request can be appropriately received by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the reception of the request.

[0079] The analysis unit can estimate the user's emotion and adjust the request analysis method based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the request analysis method based on the estimated user's emotion. The request analysis method includes, for example, text analysis, voice analysis, and the like, but is not limited to such examples. For example, if the user is stressed, the request is analyzed quickly. Also, if the user is relaxed, a detailed analysis can be performed. Also, if the user is in a hurry, a concise analysis can be performed. Thereby, the analysis accuracy is improved by adjusting the request analysis method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the request analysis method.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the request when analyzing the request. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the request when analyzing the request. The importance of the request includes, for example, the urgency of the user, the contents of the request, and the like, but is not limited to such examples. For example, a request with high importance is analyzed in detail. In addition, a request with low importance can be analyzed briefly. In addition, the priority of the analysis can be determined according to the importance. As a result, by adjusting the level of detail of the analysis based on the importance of the request, efficient analysis is possible. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance data of the request to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of the request when analyzing the request. For example, the analysis unit applies different analysis algorithms depending on the category of the request when analyzing the request. The request categories include, for example, redelivery, time designation change, etc., but are not limited to such examples. For example, a specific algorithm is applied to a redelivery request. Also, a different algorithm can be applied to a time designation change request. Also, the optimal analysis algorithm can be selected depending on the category of the request. As a result, the analysis accuracy is improved by applying the optimal analysis algorithm depending on the category of the request. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input request category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0082] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. The display method of the analysis result includes, for example, text display, graphical display, and the like, but is not limited to such examples. For example, when the user is nervous, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information can be provided. Also, when the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display to be easily viewed by the user by adjusting the display method of the analysis result according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis result.

[0083] The analysis unit can determine the priority of analysis based on the time of submission of the request when analyzing the request. The analysis unit, for example, determines the priority of analysis based on the time of submission of the request when analyzing the request. The time of submission of the request includes, for example, the submission date and time, the submission time zone, etc., but is not limited to such examples. For example, the priority is determined based on the time zone in which the request is submitted. In addition, the level of detail of the analysis can be adjusted according to the time of submission of the request. In addition, the optimal analysis method can be selected based on the time of submission. As a result, efficient analysis is possible by determining the priority of analysis based on the time of submission of the request. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the submission time data of the request to the generation AI and cause the generation AI to execute the determination of the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the request when analyzing the request. The analysis unit, for example, adjusts the order of analysis based on the relevance of the request when analyzing the request. The relevance of the request includes, for example, the similarity of the request content, the user's past request history, and the like, but is not limited to such examples. For example, highly relevant requests are analyzed preferentially. In addition, requests with low relevance can be postponed. In addition, the order of analysis can be determined based on the relevance of the request. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of the request. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the relevance data of the request to the generation AI and cause the generation AI to adjust the order of analysis.

[0085] The adjustment unit can estimate the user's emotion and adjust the method of schedule adjustment based on the estimated user's emotion. The adjustment unit, for example, estimates the user's emotion and adjusts the method of schedule adjustment based on the estimated user's emotion. The method of schedule adjustment includes, for example, optimizing the delivery route and adjusting the delivery time slot, but is not limited to such examples. For example, when the user is stressed, the schedule is quickly adjusted. Also, when the user is relaxed, detailed schedule adjustment can be performed. Also, when the user is in a hurry, simple schedule adjustment can be performed. This improves the satisfaction of the user by adjusting the method of schedule adjustment according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the method of schedule adjustment.

[0086] The adjustment unit can select an optimal adjustment method by referring to past schedule data when adjusting a schedule. The adjustment unit, for example, selects an optimal adjustment method by referring to past schedule data when adjusting a schedule. The past schedule data includes, for example, past delivery history, data on delivery time zones, etc., but is not limited to such examples. For example, an optimal redelivery date and time can be proposed based on the past schedule data. Also, an optimal time designation change can be proposed from the past schedule data. Also, the past schedule data can be analyzed to select the most efficient schedule adjustment method. As a result, optimal schedule adjustment can be made by referring to the past schedule data. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input past schedule data to the generation AI and cause the generation AI to select an adjustment method.

[0087] The adjustment unit can customize the adjustment means based on the contents of the request when adjusting the schedule. The adjustment unit customizes the adjustment means based on the contents of the request when adjusting the schedule, for example. The adjustment means include, but are not limited to, for example, changing the delivery route and adjusting the delivery time slot. For example, a specific adjustment means is applied to a redelivery request. Also, a different adjustment means can be applied to a time designation change request. Also, the optimal adjustment means can be selected according to the contents of the request. This improves the adjustment accuracy by applying the optimal adjustment means according to the contents of the request. Part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input content data of the request to the generation AI and cause the generation AI to execute customization of the adjustment means.

[0088] The adjustment unit can estimate the user's emotion and determine the priority of schedule adjustment based on the estimated user's emotion. The adjustment unit, for example, estimates the user's emotion and determines the priority of schedule adjustment based on the estimated user's emotion. The priority of schedule adjustment includes, for example, the urgency and importance of a request, but is not limited to such an example. For example, when a user transmits an urgent request, the schedule is adjusted with priority. In addition, when a user is relaxed, the schedule can be adjusted with normal priority. In addition, when a user is stressed, the schedule can be quickly adjusted. In this way, by determining the priority of schedule adjustment according to the user's emotion, a request with high urgency can be processed with priority. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of schedule adjustment.

[0089] The adjustment unit can select an optimal adjustment method in consideration of the geographical location information of the user when adjusting the schedule. The adjustment unit, for example, selects an optimal adjustment method in consideration of the geographical location information of the user when adjusting the schedule. The geographical location information includes, for example, the user's address, current location, etc., but is not limited to such examples. For example, when the user is in a specific area, schedule adjustment related to that area is performed preferentially. In addition, schedule adjustment related to a place close to the user's current location can also be performed preferentially. In addition, an optimal schedule adjustment method can be selected based on the geographical location information of the user. This makes it possible to perform optimal schedule adjustment by considering the geographical location information of the user. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input the geographical location information of the user to the generation AI and cause the generation AI to select an adjustment method.

[0090] The adjustment unit can analyze the user's social media activity and propose a means of adjustment when adjusting a schedule. The adjustment unit, for example, analyzes the user's social media activity and proposes a means of adjustment when adjusting a schedule. Social media activity includes, for example, posting content, history of likes, and the like, but is not limited to such examples. For example, the current interests of the user are analyzed from the social media activity of the user, and related schedule adjustments are proposed. In addition, the means of schedule adjustment can be customized based on the content of the user's social media posts. In addition, the optimal schedule adjustment means can be proposed with reference to the user's social media activity. In this way, by analyzing the user's social media activity, related schedule adjustment means can be proposed. Part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the proposal of the adjustment means.

[0091] The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. The notification unit, for example, estimates the user's emotion and adjusts the notification method based on the estimated user's emotion. The notification method includes, for example, email, SMS, push notification, etc., but is not limited to such examples. For example, when the user is stressed, a simple notification method is provided. Also, when the user is relaxed, a detailed notification method can be provided. Also, when the user is in a hurry, a quick notification method can be provided. This allows the optimal notification for the user by adjusting the notification method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the notification method.

[0092] The notification unit can select the optimal notification method by referring to the user's past notification history at the time of notification. The notification unit, for example, selects the optimal notification method by referring to the user's past notification history at the time of notification. The past notification history includes, for example, past notification contents, notification means, etc., but is not limited to such examples. For example, the notification method that the user has used in the past with preference is provided. In addition, the optimal notification method for a specific time period can be proposed from the user's past notification history. In addition, the user's past notification history can be analyzed and the most effective notification method can be selected. As a result, the optimal notification method can be provided to the user by referring to the past notification history. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input past notification history data to the generation AI and cause the generation AI to select the notification method.

[0093] The notification unit can customize the notification means based on the contents of the request at the time of notification. The notification unit, for example, customizes the notification means based on the contents of the request at the time of notification. Notification means include, for example, email, SMS, push notification, etc., but are not limited to such examples. For example, a specific notification means is applied to a redelivery request. Also, a different notification means can be applied to a time designation change request. Also, the optimal notification means can be selected depending on the contents of the request. This improves the notification accuracy by applying the optimal notification means depending on the contents of the request. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input content data of the request to the generation AI and have the generation AI execute customization of the notification means.

[0094] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit, for example, estimates the user's emotion and determines the priority of notifications based on the estimated user's emotion. The priority of notifications includes, for example, the urgency and importance of notifications, but is not limited to such examples. For example, when a user transmits an urgent request, the notification is given priority. Also, when the user is relaxed, the notification can be given with normal priority. Also, when the user is stressed, the notification can be given quickly. In this way, by determining the priority of notifications according to the user's emotion, notifications with high urgency can be given priority. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of notifications.

[0095] The notification unit can select the optimal notification method in consideration of the geographical location information of the user at the time of notification. The notification unit, for example, selects the optimal notification method in consideration of the geographical location information of the user at the time of notification. The geographical location information includes, for example, the user's address, current location, etc., but is not limited to such examples. For example, when the user is in a specific area, a notification method related to that area is preferentially provided. In addition, a notification method related to a place close to the user's current location can also be preferentially provided. In addition, the optimal notification method can be selected based on the geographical location information of the user. In this way, the optimal notification method can be provided by considering the geographical location information of the user. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the notification method.

[0096] The notification unit can analyze the user's social media activity at the time of notification and suggest a means of notification. The notification unit, for example, analyzes the user's social media activity at the time of notification and suggests a means of notification. The social media activity includes, for example, the content of posts, the history of likes, and the like, but is not limited to such examples. For example, the current interests of the user are analyzed from the social media activity of the user, and a related notification method is suggested. The notification means can also be customized based on the content of the user's posts on social media. The optimal notification means can also be suggested with reference to the user's social media activity. In this way, the related notification means can be suggested by analyzing the user's social media activity. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the user's social media activity data to the generation AI and have the generation AI execute the suggestion of the notification means.

[0097] The notification unit can analyze the user's social media activity at the time of notification and suggest a means of notification. The notification unit, for example, analyzes the user's social media activity at the time of notification and suggests a means of notification. The social media activity includes, for example, the content of posts, the history of likes, and the like, but is not limited to such examples. For example, the current interests of the user are analyzed from the social media activity of the user, and a related notification method is suggested. The notification means can also be customized based on the content of the user's posts on social media. The optimal notification means can also be suggested with reference to the user's social media activity. In this way, the related notification means can be suggested by analyzing the user's social media activity. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input the user's social media activity data to the generation AI and have the generation AI execute the suggestion of the notification means.

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

[0099] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the contents of requests that the user has frequently made in the past can be automatically displayed as candidates. It can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the request contents to be used during a specific time period from the user's past request history. In this way, it is possible to provide the optimal reception method for the user by analyzing the past request history.

[0100] When analyzing a request, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, requests with high importance can be analyzed in detail, and requests with low importance can be analyzed briefly. In addition, the analysis priority can be determined according to the importance. In this way, efficient analysis is possible by adjusting the level of detail of the analysis based on the importance of the request.

[0101] When adjusting a schedule, the adjustment unit can refer to past schedule data and select the optimal adjustment method. For example, the adjustment unit can suggest the optimal redelivery date and time based on past schedule data. The adjustment unit can also suggest the optimal time designation change from past schedule data. Furthermore, the adjustment unit can analyze past schedule data and select the most efficient schedule adjustment method. This makes it possible to make optimal schedule adjustments by referring to past schedule data.

[0102] At the time of notification, the notification unit can select the optimal notification method by referring to the user's past notification history. For example, the notification method that the user has used in the past as a favorite is preferentially provided. Also, the notification unit can suggest the optimal notification method for a specific time period based on the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and select the most effective notification method. In this way, the optimal notification method can be provided to the user by referring to the past notification history.

[0103] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving a request. For example, when the user inputs the current situation, related request contents are preferentially displayed. In addition, the request contents can be customized based on the user's areas of interest. Furthermore, optimal request contents can be proposed according to the user's current situation. In this way, more appropriate requests can be received by filtering requests based on the user's situation and areas of interest.

[0104] The reception unit can estimate the user's emotion and adjust the method of receiving the request based on the estimated user's emotion. For example, when the user is stressed, a simple interface can be provided to minimize the input procedure. Also, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized to allow the request to be received quickly. In this way, the method of receiving the request can be adjusted according to the user's emotion, thereby improving the satisfaction of the user.

[0105] The analysis unit can estimate the user's emotions and adjust the request analysis method based on the estimated user's emotions. For example, if the user is stressed, the request is analyzed quickly. If the user is relaxed, a detailed analysis can be performed. Furthermore, if the user is in a hurry, a brief analysis can be performed. In this way, the analysis accuracy can be improved by adjusting the request analysis method according to the user's emotions.

[0106] The adjustment unit can estimate the user's emotions and adjust the method of schedule adjustment based on the estimated user's emotions. For example, if the user is feeling stressed, the schedule can be adjusted quickly. Also, if the user is relaxed, detailed schedule adjustment can be performed. Furthermore, if the user is in a hurry, simple schedule adjustment can be performed. In this way, the method of schedule adjustment can be adjusted according to the user's emotions, thereby improving the user's satisfaction.

[0107] The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. For example, if the user is stressed, a simple notification method can be provided. If the user is relaxed, a detailed notification method can be provided. Furthermore, if the user is in a hurry, a quick notification method can be provided. In this way, by adjusting the notification method according to the user's emotion, it is possible to provide the most suitable notification for the user.

[0108] The reception unit can estimate the user's emotion and determine the priority of the received requests based on the estimated user's emotion. For example, if the user transmits an urgent request, the request is processed with priority. If the user is relaxed, the request can be processed with normal priority. Furthermore, if the user is stressed, the request can be processed quickly. In this way, by determining the priority of the requests according to the user's emotion, it is possible to process requests with high urgency with priority.

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

[0110] Step 1: The reception unit receives a request from the user for redelivery or a change in the designated delivery time. Requests from the user include changing the date and time of redelivery or changing the delivery destination. The reception unit receives the request from the user, for example, through a messenger app (LINE, WhatsApp, Facebook Messenger, etc.). Step 2: The analysis unit uses the generation AI to analyze the request received by the reception unit. The analysis is performed based on the classification method of the request content and the analysis algorithm. For example, the generation AI analyzes the request using a natural language processing model (GPT-4, Gemini, etc.) and determines the appropriate response. Step 3: The adjustment unit uses the generation AI to adjust the delivery schedule based on the request analyzed by the analysis unit. The adjustment is made based on the schedule optimization method and adjustment algorithm. For example, the generation AI considers the user's desired time zone and the delivery driver's schedule to propose the optimal redelivery date and time or a new delivery time. Step 4: The notification unit uses the generation AI to notify the user of the schedule adjusted by the adjustment unit. The notification is performed by means of email, SMS, push notification, etc. For example, the generation AI notifies the user of the proposed content quickly and accurately.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned reception unit, analysis unit, adjustment unit, and notification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a request from a user through a messenger app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the request using a generation AI. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts a delivery schedule based on the analyzed request. The notification unit is realized, for example, by the control unit 46A of the smart device 14 and notifies the user of the adjusted schedule. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned reception unit, analysis unit, adjustment unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a request from a user through a messenger app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the request using a generation AI. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts a delivery schedule based on the analyzed request. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the user of the adjusted schedule. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned reception unit, analysis unit, adjustment unit, and notification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives a request from a user through a messenger app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the request using a generation AI. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts a delivery schedule based on the analyzed request. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314 and notifies the user of the adjusted schedule. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

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

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] Each of the multiple elements including the above-mentioned reception unit, analysis unit, adjustment unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives a request from a user through a messenger app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the request using a generation AI. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts a delivery schedule based on the analyzed request. The notification unit is realized, for example, by the control unit 46A of the robot 414 and notifies the user of the adjusted schedule. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

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

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

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

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

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

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

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

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

[0182] (Appendix 1) a reception unit that receives a request for redelivery or a change in the designated delivery time from a user; an analysis unit that analyzes the request accepted by the acceptance unit; an adjustment unit that adjusts a delivery schedule based on the request analyzed by the analysis unit; A notification unit that notifies a user of the schedule adjusted by the adjustment unit. A system characterized by: (Appendix 2) The reception unit is Accept requests from users via messenger apps 2. The system of claim 1, (Appendix 3) The analysis unit is Generative AI analyzes requests and determines appropriate responses 2. The system of claim 1, (Appendix 4) The adjustment unit is Generative AI checks delivery schedules and suggests redelivery dates and times or new delivery times 2. The system of claim 1, (Appendix 5) The notification unit is Generative AI informs users of suggestions 2. The system of claim 1, (Appendix 6) The adjustment unit is Generative AI provides delivery drivers with information to adjust their schedules 2. The system of claim 1, (Appendix 7) The reception unit is Infer user emotions and adjust how requests are accepted based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past request history and select the optimal reception method 2. The system of claim 1, (Appendix 9) The reception unit is When requests are received, they are filtered based on the user's current situation and interests. 2. The system of claim 1, (Appendix 10) The reception unit is Estimate user emotions and prioritize requests based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When accepting requests, the system takes into account the user's geographic location information and prioritizes requests that are more relevant to the user. 2. The system of claim 1, (Appendix 12) The reception unit is When receiving a request, analyze your social media activity to receive related requests 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user sentiment and adjust how requests are parsed based on the inferred sentiment 2. The system of claim 1, (Appendix 14) The analysis unit is When analyzing requests, adjust the level of analysis detail based on the importance of the request. 2. The system of claim 1, (Appendix 15) The analysis unit is When parsing a request, apply different parsing algorithms depending on the category of the request. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is When parsing requests, prioritize parsing based on when the request was submitted 2. The system of claim 1, (Appendix 18) The analysis unit is When parsing requests, adjust the order of parsing based on the relevance of the requests 2. The system of claim 1, (Appendix 19) The adjustment unit is To estimate a user's emotion and adjust a schedule adjustment method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The adjustment unit is When adjusting schedules, refer to past schedule data to select the optimal adjustment method 2. The system of claim 1, (Appendix 21) The adjustment unit is Customize scheduling based on the request 2. The system of claim 1, (Appendix 22) The adjustment unit is To estimate a user's emotion and determine a priority of schedule adjustment based on the estimated user's emotion. 2. The system of claim 1, (Appendix 23) The adjustment unit is When adjusting schedules, select the optimal adjustment method taking into account the user's geographic location information 2. The system of claim 1, (Appendix 24) The adjustment unit is Analyze users' social media activity to suggest adjustments when adjusting schedules 2. The system of claim 1, (Appendix 25) The notification unit is Infer user emotions and adjust notification methods based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The notification unit is When notifying users, the system selects the optimal notification method by referring to the user's past notification history. 2. The system of claim 1, (Appendix 27) The notification unit is Upon notification, customize the notification method based on the content of the request 2. The system of claim 1, (Appendix 28) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 2. The system of claim 1, (Appendix 29) The notification unit is When notifying users, the optimal notification method is selected based on the user's geographic location. 2. The system of claim 1, (Appendix 30) The notification unit is When notifying users, the app analyzes their social media activity to suggest a method of notification. 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a request for redelivery or a change in the designated delivery time from a user; an analysis unit that analyzes the request accepted by the acceptance unit; an adjustment unit that adjusts a delivery schedule based on the request analyzed by the analysis unit; A notification unit that notifies a user of the schedule adjusted by the adjustment unit. A system characterized by:

2. The reception unit is Accept requests from users via messenger apps 2. The system of claim 1 .

3. The analysis unit is Generative AI analyzes the request and determines the appropriate response 2. The system of claim 1 .

4. The adjustment unit is Check delivery schedules using generative AI and suggest redelivery dates and times or new delivery times 2. The system of claim 1 .

5. The notification unit is Generative AI notifies users of suggestions 2. The system of claim 1 .

6. The adjustment unit is Generative AI provides delivery drivers with information to adjust their schedules 2. The system of claim 1 .

7. The reception unit is Estimate the user's emotions and adjust the way requests are accepted based on the estimated user emotions.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past request history and select the optimal reception method 2. The system of claim 1 .

Images