system

Abstract

The system according to this embodiment aims to streamline delivery operations in large-scale apartment buildings. [Solution] The system according to the embodiment comprises an aggregation unit, a receiving unit, a delivery unit, and a linking unit. The aggregation unit aggregates packages from delivery companies. The receiving unit receives the packages aggregated by the aggregation unit. The delivery unit delivers the packages received by the receiving unit at the optimal time in conjunction with the homeowner's return information. The linking unit links with existing systems of delivery companies and condominium management companies to share and analyze data.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, delivery operations in large-scale condominiums are not efficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of delivery operations in large-scale condominiums.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an aggregation unit, a receiving unit, a delivery unit, and a coordination unit. The aggregation unit aggregates packages from delivery companies. The receiving unit receives the packages aggregated by the aggregation unit. The delivery unit delivers the packages received by the receiving unit at the optimal time, linked to the homeowner's return home information. The coordination unit coordinates with existing systems of delivery companies and apartment management companies to share and analyze data. [Effects of the Invention]

[0007] The system according to this embodiment can streamline delivery operations in large apartment buildings. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An autonomous delivery robot system according to an embodiment of the present invention is a system for streamlining delivery operations within large apartment buildings. This autonomous delivery robot system aggregates packages from delivery companies and performs last-mile delivery within the apartment building at the optimal timing in conjunction with the homeowner's return information. For example, a delivery company aggregates packages from the apartment building, an autonomous delivery robot receives the aggregated packages, and delivers them to each apartment within the building. In this process, the delivery is performed at the optimal timing in conjunction with the homeowner's return information. For example, by delivering just before the homeowner returns home, the need for redelivery is eliminated. Furthermore, by utilizing generative AI, the system seamlessly integrates with existing systems of delivery companies and apartment management companies, enhancing data sharing and analysis. This enables more advanced logistics management and service provision. In addition, generative AI is used to enhance security authentication through facial recognition and voice recognition, and an automatic translation function is provided for foreign residents. This system eliminates the need for delivery companies to visit each apartment, resulting in significant reductions in time and labor costs. Furthermore, by linking with the homeowner's return information, unnecessary redeliveries are reduced. Furthermore, apartment building managers will no longer need to deal with multiple delivery companies, saving time and effort on security checks and unlocking. This will enable the autonomous delivery robot system to efficiently collect, receive, deliver, share, and analyze packages from delivery companies.

[0029] The autonomous delivery robot system according to this embodiment comprises an aggregation unit, a receiving unit, a delivery unit, and a coordination unit. The aggregation unit aggregates packages from courier companies. The aggregation unit can aggregate packages based on, for example, the type of package or the timing of aggregation. For example, the aggregation unit can prioritize the aggregation of fragile packages based on the type of package. The aggregation unit can also filter packages based on their size to optimize space utilization. Furthermore, the aggregation unit can select an efficient aggregation method based on the weight of the packages. The receiving unit receives the packages aggregated by the aggregation unit. The receiving unit can receive packages based on, for example, the location and means of receipt. For example, the receiving unit can receive packages at a designated location. The receiving unit can also select an efficient receipt method considering the delivery schedule of the courier company. Furthermore, the receiving unit can select the optimal receipt method by analyzing the delivery history of the courier company. The delivery unit delivers the packages received by the receiving unit at the optimal time in conjunction with the homeowner's return information. The delivery unit can deliver packages at the optimal time based, for example, the homeowner's return time and traffic conditions. For example, the delivery unit can eliminate the need for redelivery by delivering packages just before the homeowner returns home. The delivery unit can also apply different delivery algorithms depending on the homeowner's lifestyle. Furthermore, the delivery unit can prioritize deliveries based on the homeowner's return time. The integration unit connects with existing systems of delivery companies and apartment management companies to share and analyze data. For example, the integration unit can seamlessly connect with existing systems of delivery companies and apartment management companies using generative AI to share and analyze data. For instance, the integration unit can use generative AI to enhance security authentication through facial and voice recognition. It can also use generative AI to provide an automatic translation function for foreign residents. Furthermore, the integration unit can analyze the social media activities of delivery companies and apartment management companies and share relevant data. As a result, the autonomous delivery robot system according to this embodiment can efficiently collect, receive, deliver, share data on, and analyze packages from delivery companies.

[0030] The consolidation unit consolidates packages from delivery companies. The unit can consolidate packages based on factors such as package type and timing. Specifically, it is equipped with sensors that detect the material and packaging condition of packages to prioritize the consolidation of fragile items. This prevents fragile items such as glass products and electronic devices from being mixed with other packages. The consolidation unit can also filter packages based on their size to optimize space utilization. For example, it can efficiently utilize limited space by using an algorithm that automatically measures package dimensions and calculates the optimal placement. Furthermore, the consolidation unit can select an efficient consolidation method based on package weight. By measuring the weight of packages using weight sensors and placing heavier packages at the bottom and lighter packages at the top, stable loading is achieved. This allows the consolidation unit to provide the optimal consolidation method according to package type, size, and weight, enabling efficient package management. Additionally, the consolidation unit has a function to monitor the status of packages in real time and immediately issue alerts if an anomaly occurs. For example, if a package is damaged or the temperature or humidity changes abnormally, the consolidation unit automatically issues a warning and prompts appropriate action. This allows the consolidation unit to achieve efficient consolidation work while ensuring the safety and quality of the packages.

[0031] The receiving unit receives packages that have been aggregated by the aggregation unit. The receiving unit can receive packages based on, for example, the location and method of receipt. Specifically, the receiving unit uses GPS functionality to obtain precise location information in order to receive packages at a designated location and understands in real time when the delivery company's truck or drone will arrive. The receiving unit can also select an efficient receiving method considering the delivery schedule of the delivery company. For example, by using an algorithm that analyzes the delivery schedule and calculates the most efficient receiving time slot, waiting times can be minimized. Furthermore, the receiving unit can also select the optimal receiving method by analyzing the delivery history of the delivery company. Based on past delivery data, it predicts delivery patterns for specific time slots and days of the week and proposes the optimal receiving method. This enables the receiving unit to achieve efficient and flexible receiving operations and strengthen cooperation with delivery companies. In addition, the receiving unit also has a function to automatically confirm receipt when a package is received. For example, by scanning a 2D code (e.g., QR code®) or barcode, it quickly confirms receipt and records it in a database. This allows the receiving unit to improve the efficiency and accuracy of the receiving operation.

[0032] The delivery department delivers packages received by the receiving department at the optimal time, linked to the homeowner's return information. For example, the delivery department can make deliveries at the optimal time based on the homeowner's return time and traffic conditions. Specifically, it adjusts the delivery timing based on return information obtained from the homeowner's smartphone or smart home device. For example, by making a delivery just before the homeowner returns home, the need for redelivery is eliminated. The delivery department can also apply different delivery algorithms depending on the homeowner's lifestyle. For example, if the homeowner returns home at the same time every day, an algorithm that delivers at that time will be applied. On the other hand, if the homeowner's return time is irregular, an algorithm that obtains return information in real time and calculates the optimal delivery timing will be used. Furthermore, the delivery department can determine the priority of deliveries based on when the homeowner returns home. For example, if the homeowner is away for an extended period, deliveries will be temporarily suspended and resumed after their return. In addition, the delivery department monitors traffic conditions in real time and selects the optimal route to shorten delivery times and achieve efficient deliveries. This allows the delivery department to provide flexible and efficient deliveries linked to the homeowner's return information, reducing the hassle of redeliveries. Furthermore, the delivery department also has a function to automatically confirm deliveries after completion. For example, when the homeowner receives a package, they can confirm receipt via their smartphone or smart home device and record it in the database. This allows the delivery department to improve the efficiency and accuracy of its delivery operations.

[0033] The Integration Department will integrate with existing systems of delivery companies and condominium management companies to share and analyze data. For example, the Integration Department can seamlessly integrate with existing systems of delivery companies and condominium management companies using generative AI to share and analyze data. Specifically, it can use generative AI to enhance security authentication through facial recognition and voice recognition. For example, when a delivery person from a delivery company accesses a condominium, the facial recognition system can be used to verify their identity and prevent unauthorized access. Also, by using voice recognition to receive instructions from delivery persons, manual effort can be saved. Furthermore, the Integration Department can also use generative AI to provide an automatic translation function for foreign residents. For example, when a delivery person makes a delivery to a foreign resident, the automatic translation function can be used to enable smooth communication. In addition, the Integration Department can analyze the social media activities of delivery companies and condominium management companies and share relevant data. For example, it can collect customer feedback and ratings on social media and use it to improve services. In this way, the Integration Department can strengthen collaboration with delivery companies and condominium management companies and achieve efficient data sharing and analysis. Furthermore, the Integration Department can use generative AI to analyze data and support future demand forecasting and service optimization. For example, based on past delivery data, the system predicts delivery demand in specific areas and time slots and proposes the optimal allocation of resources. This allows the collaboration department to support the efficient and effective provision of services through cooperation with delivery companies and apartment management companies.

[0034] The delivery department can make deliveries at the optimal time by linking with the homeowner's return information. For example, the delivery department can adjust the timing of deliveries based on the homeowner's expected return time. For instance, by making a delivery just before the homeowner returns home, the need for redelivery is eliminated. The delivery department can also obtain the homeowner's return information in real time and determine the optimal delivery timing. Furthermore, the delivery department can determine the priority of deliveries based on the homeowner's return information. This, by linking with the homeowner's return information, eliminates the need for redelivery.

[0035] The Integration Department can seamlessly integrate with existing systems of delivery companies and condominium management companies using generative AI, enabling data sharing and analysis. For example, the Integration Department can use generative AI to share and analyze data with existing systems of delivery companies and condominium management companies. For instance, it can use generative AI to analyze delivery data from delivery companies and propose optimal delivery routes. Furthermore, the Integration Department can use generative AI to analyze security data from condominium management companies and strengthen security measures. In addition, the Integration Department can use generative AI to analyze the social media activities of delivery companies and condominium management companies and share relevant data. This improves the accuracy of data sharing and analysis through the use of generative AI.

[0036] The collaborative unit can enhance security authentication using facial recognition and voice recognition through generative AI. For example, the collaborative unit can execute a facial recognition algorithm using generative AI to strengthen security within an apartment building. For instance, the collaborative unit can recognize residents' faces using generative AI and control access to the apartment building. Furthermore, the collaborative unit can execute voice recognition technology using generative AI to recognize residents' voices and perform security authentication. In addition, the collaborative unit can perform security authentication combining facial recognition and voice recognition using generative AI. This improves the accuracy of security authentication by utilizing generative AI.

[0037] The collaborative unit can provide an automatic translation function for foreign residents using generative AI. For example, the collaborative unit can use generative AI to automatically translate the language of foreign residents, facilitating smooth communication. For instance, the collaborative unit can use generative AI to translate the native language of foreign residents into Japanese and provide guidance and notifications within the apartment building. Furthermore, the collaborative unit can use generative AI to translate Japanese into the native language of foreign residents, supporting communication with residents. In addition, the collaborative unit can provide an automatic translation function that supports multiple languages ​​using generative AI. This improves the accuracy of support for foreign residents through the use of generative AI.

[0038] The aggregation unit can analyze the delivery schedules of courier companies and select the optimal aggregation method. For example, the aggregation unit can analyze the past delivery schedules of courier companies to determine the optimal aggregation timing. For example, the aggregation unit can select an efficient aggregation method by considering the real-time delivery schedules of courier companies. The aggregation unit can also compare the delivery schedules of courier companies with the available receiving times of apartments to select the optimal aggregation method. This enables efficient aggregation by analyzing the delivery schedules of courier companies. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without using AI.

[0039] The aggregation unit can filter based on the type and size of the packages. For example, the aggregation unit can prioritize the aggregation of fragile packages based on their type. For example, the aggregation unit can filter based on the size of the packages to make optimal use of space. The aggregation unit can also select an efficient aggregation method based on the weight of the packages. This enables efficient aggregation by filtering based on the type and size of the packages. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without using AI.

[0040] The aggregation unit can prioritize the aggregation of highly relevant packages by considering the geographical location information of the delivery companies. For example, the aggregation unit can prioritize the aggregation of nearby packages based on the geographical location information of the delivery companies. For example, the aggregation unit can efficiently aggregate packages in the same area based on the geographical location information of the delivery companies. Furthermore, the aggregation unit can also prioritize the aggregation of packages that can be aggregated via the shortest route based on the geographical location information of the delivery companies. This enables efficient aggregation by considering the geographical location information of the delivery companies. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without using AI.

[0041] The aggregation unit can analyze the social media activities of delivery companies and aggregate relevant packages. For example, the aggregation unit can analyze the social media activities of delivery companies and prioritize the aggregation of packages related to specific campaigns. For example, the aggregation unit can analyze the social media activities of delivery companies and efficiently aggregate packages related to trends. The aggregation unit can also analyze the social media activities of delivery companies and aggregate packages based on customer interests. This enables efficient aggregation by analyzing the social media activities of delivery companies. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI.

[0042] The receiving unit can analyze the delivery history of the courier company to select the optimal receiving method. For example, the receiving unit can analyze the past delivery history of the courier company and propose the optimal receiving method. For example, the receiving unit can consider the real-time delivery history of the courier company to select an efficient receiving method. The receiving unit can also compare the delivery history of the courier company with the available receiving times of the apartment building to select the optimal receiving method. This enables efficient receiving by analyzing the delivery history of the courier company. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without using AI.

[0043] The receiving unit can adjust the level of detail in receiving packages based on their importance. For example, the receiving unit can provide detailed receiving procedures based on the importance of the package. For example, it can provide detailed receiving procedures for important packages. It can also provide standard receiving procedures for regular packages. Furthermore, it can provide simplified receiving procedures for low-importance packages. This allows for efficient receiving by adjusting the level of detail in receiving packages based on their importance. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI.

[0044] The receiving unit can prioritize receiving packages that are highly relevant, taking into account the geographical location information of the delivery company. For example, the receiving unit can prioritize receiving nearby packages based on the geographical location information of the delivery company. For example, the receiving unit can efficiently receive packages in the same area based on the geographical location information of the delivery company. Furthermore, the receiving unit can also prioritize receiving packages that can be received via the shortest route based on the geographical location information of the delivery company. In this way, efficient receiving becomes possible by considering the geographical location information of the delivery company. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without using AI.

[0045] The receiving unit can analyze the social media activities of delivery companies and receive relevant packages. For example, the receiving unit can analyze the social media activities of delivery companies and prioritize receiving packages related to specific campaigns. For example, the receiving unit can analyze the social media activities of delivery companies and efficiently receive packages related to trends. The receiving unit can also analyze the social media activities of delivery companies and receive packages based on customer interests. This enables efficient receiving by analyzing the social media activities of delivery companies. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not using AI.

[0046] The delivery department can adjust the level of detail in deliveries based on the homeowner's return information. For example, the delivery department can provide detailed delivery information based on the homeowner's return information. For example, the delivery department can provide detailed delivery information just before the homeowner returns home. The delivery department can also provide simplified delivery information if the homeowner is already home. Furthermore, if the homeowner is absent, the delivery department can provide detailed information about redelivery. By adjusting the level of detail in deliveries based on the homeowner's return information, efficient deliveries become possible. Some or all of the above processing in the delivery department may be performed using AI, for example, or without AI.

[0047] The delivery department can apply different delivery algorithms depending on the homeowner's lifestyle. For example, the delivery department can select the optimal delivery time based on the homeowner's lifestyle. For example, the delivery department can make deliveries to coincide with the homeowner's regular return home times. The delivery department can also analyze the homeowner's lifestyle and apply an efficient delivery algorithm. Furthermore, the delivery department can apply different delivery algorithms based on the homeowner's lifestyle. This enables optimal deliveries tailored to the homeowner's lifestyle. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI.

[0048] The delivery department can determine delivery priorities based on when the homeowner returns home. For example, the delivery department can prioritize the delivery of important packages based on when the homeowner returns home. For example, the delivery department can prioritize the delivery of important packages just before the homeowner returns home. The delivery department can also deliver packages with normal priority if the homeowner is already home. Furthermore, the delivery department can determine the priority for redelivery if the homeowner is absent. This enables efficient delivery by determining delivery priorities based on when the homeowner returns home. Some or all of the above processes in the delivery department may be performed using AI, for example, or not.

[0049] The delivery department can adjust the delivery order based on the homeowner's relevance. For example, the delivery department can prioritize the delivery of important packages based on the homeowner's relevance. For instance, if a homeowner needs to receive an important package, the delivery department can prioritize its delivery. The delivery department can also deliver regular packages in a standard order. Furthermore, the delivery department can postpone deliveries of less important packages to homeowners. This allows for efficient delivery by adjusting the delivery order based on the homeowner's relevance. Some or all of the above processes in the delivery department may be performed using AI, for example, or not.

[0050] The integration unit can select the optimal sharing method by referring to the history of existing systems of delivery companies and condominium management companies. For example, the integration unit can analyze the past data sharing history of delivery companies and propose the optimal sharing method. For example, the integration unit can refer to the history of existing systems of condominium management companies and select an efficient data sharing method. Furthermore, the integration unit can integrate the data sharing history of delivery companies and condominium management companies and select the optimal sharing method. This enables efficient data sharing by referring to the history of existing systems of delivery companies and condominium management companies. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0051] The collaboration unit can adjust the level of detail shared based on the attribute information of delivery companies and condominium management companies. For example, the collaboration unit can prioritize the sharing of important data based on the attribute information of delivery companies. For example, the collaboration unit can share detailed data based on the attribute information of condominium management companies. The collaboration unit can also integrate the attribute information of delivery companies and condominium management companies and select the optimal data sharing method. This enables efficient data sharing by adjusting the level of detail shared based on the attribute information of delivery companies and condominium management companies. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without using AI.

[0052] The collaboration unit can prioritize the sharing of highly relevant data by considering the geographical location information of delivery companies and condominium management companies. For example, the collaboration unit can prioritize the sharing of nearby data based on the geographical location information of delivery companies. For example, the collaboration unit can efficiently share data within the same area based on the geographical location information of condominium management companies. Furthermore, the collaboration unit can integrate the geographical location information of delivery companies and condominium management companies and select the optimal data sharing method. This enables efficient data sharing by considering the geographical location information of delivery companies and condominium management companies. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without using AI.

[0053] The Collaboration Department can analyze the social media activities of delivery companies and condominium management companies and share relevant data. For example, the Collaboration Department can analyze the social media activities of delivery companies and prioritize the sharing of data related to specific campaigns. For example, the Collaboration Department can analyze the social media activities of condominium management companies and efficiently share data related to trends. The Collaboration Department can also integrate the social media activities of delivery companies and condominium management companies and select the optimal data sharing method. This enables efficient data sharing by analyzing the social media activities of delivery companies and condominium management companies. Some or all of the above processing in the Collaboration Department may be performed using AI, for example, or not using AI.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The autonomous delivery robot system can also be equipped with an environmental sensor unit. This unit can acquire environmental information such as temperature, humidity, and illuminance within the apartment building, and optimize delivery timing and routes based on this information. For example, if the temperature inside the apartment building is high, the unit can expedite deliveries to prevent package deterioration. If the humidity is high, it can prioritize the delivery of packages sensitive to moisture. Furthermore, if the illuminance is low, it can select routes that pass through well-lit areas for safety reasons. This enables efficient deliveries that take environmental information into consideration.

[0056] The autonomous delivery robot system can also be equipped with a health monitoring unit. This unit can monitor the homeowner's health and select a delivery method appropriate to their condition. For example, if the homeowner is unwell, the unit can either postpone the delivery or select a delivery method that takes their health into consideration. Furthermore, if the homeowner is elderly and requires assistance during delivery, the unit can arrange for support staff. Additionally, if the homeowner is exercising, the delivery can be temporarily suspended and resumed after the exercise is complete. This enables flexible delivery tailored to the homeowner's health condition.

[0057] Autonomous delivery robot systems can also be equipped with an energy management unit. This unit manages the robot's battery level and the location of charging stations, enabling efficient energy use. For example, if the battery level is low, the unit can move to the nearest charging station to recharge. It can also optimize delivery routes to minimize energy consumption. Furthermore, the energy management unit can comprehensively manage the battery levels of multiple robots and distribute energy efficiently, enabling energy-efficient deliveries.

[0058] The autonomous delivery robot system can also be equipped with an emergency response unit. This unit can respond quickly to emergencies within the apartment building. For example, in the event of a fire, earthquake, or other disaster, the emergency response unit can guide residents along evacuation routes and ensure their safety. It can also coordinate with medical institutions to provide a rapid response if emergency medical care is needed. Furthermore, the emergency response unit can integrate with the building's security system to detect intruders and issue alarms. This improves the safety of the apartment building and increases residents' sense of security.

[0059] Autonomous delivery robot systems can also be equipped with an entertainment unit. This unit can provide entertainment content to residents during deliveries, enhancing the delivery experience. For example, it can play music or videos to entertain residents during deliveries. The delivery robot can also interact with residents, offering quizzes and games. Furthermore, the entertainment unit can provide special content tailored to the season or events. This transforms deliveries from mere package handover into an enjoyable experience.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The consolidation unit consolidates packages from delivery companies. The consolidation unit can consolidate packages based on their type and timing. For example, it can prioritize the consolidation of fragile packages, filter packages based on their size to optimize space utilization, and select an efficient consolidation method based on their weight. Step 2: The receiving unit receives the packages aggregated by the aggregation unit. The receiving unit can receive packages based on the location and method of receipt. For example, it can receive packages at a designated location, select an efficient receiving method considering the delivery schedule of the courier company, or select the optimal receiving method by analyzing the delivery history of the courier company. Step 3: The delivery department delivers packages received by the receiving department at the optimal time, linked to the homeowner's return home information. The delivery department can make deliveries at the optimal time based on the homeowner's return time and traffic conditions. For example, by delivering just before the homeowner returns home, the need for redelivery is eliminated, and different delivery algorithms can be applied depending on the homeowner's lifestyle. Furthermore, delivery priorities can be determined based on when the homeowner returns home. Step 4: The Integration Department integrates with existing systems of delivery companies and apartment management companies to share and analyze data. The Integration Department can seamlessly integrate with existing systems of delivery companies and apartment management companies using generative AI to share and analyze data. For example, it can use generative AI to enhance security authentication through facial recognition and voice recognition, provide automatic translation functions for foreign residents, and analyze the social media activities of delivery companies and apartment management companies to share relevant data.

[0062] (Example of form 2) An autonomous delivery robot system according to an embodiment of the present invention is a system for streamlining delivery operations within large apartment buildings. This autonomous delivery robot system aggregates packages from delivery companies and performs last-mile delivery within the apartment building at the optimal timing in conjunction with the homeowner's return information. For example, a delivery company aggregates packages from the apartment building, an autonomous delivery robot receives the aggregated packages, and delivers them to each apartment within the building. In this process, the delivery is performed at the optimal timing in conjunction with the homeowner's return information. For example, by delivering just before the homeowner returns home, the need for redelivery is eliminated. Furthermore, by utilizing generative AI, the system seamlessly integrates with existing systems of delivery companies and apartment management companies, enhancing data sharing and analysis. This enables more advanced logistics management and service provision. In addition, generative AI is used to enhance security authentication through facial recognition and voice recognition, and an automatic translation function is provided for foreign residents. This system eliminates the need for delivery companies to visit each apartment, resulting in significant reductions in time and labor costs. Furthermore, by linking with the homeowner's return information, unnecessary redeliveries are reduced. Furthermore, apartment building managers will no longer need to deal with multiple delivery companies, saving time and effort on security checks and unlocking. This will enable the autonomous delivery robot system to efficiently collect, receive, deliver, share, and analyze packages from delivery companies.

[0063] The autonomous delivery robot system according to this embodiment comprises an aggregation unit, a receiving unit, a delivery unit, and a coordination unit. The aggregation unit aggregates packages from courier companies. The aggregation unit can aggregate packages based on, for example, the type of package or the timing of aggregation. For example, the aggregation unit can prioritize the aggregation of fragile packages based on the type of package. The aggregation unit can also filter packages based on their size to optimize space utilization. Furthermore, the aggregation unit can select an efficient aggregation method based on the weight of the packages. The receiving unit receives the packages aggregated by the aggregation unit. The receiving unit can receive packages based on, for example, the location and means of receipt. For example, the receiving unit can receive packages at a designated location. The receiving unit can also select an efficient receipt method considering the delivery schedule of the courier company. Furthermore, the receiving unit can select the optimal receipt method by analyzing the delivery history of the courier company. The delivery unit delivers the packages received by the receiving unit at the optimal time in conjunction with the homeowner's return information. The delivery unit can deliver packages at the optimal time based, for example, the homeowner's return time and traffic conditions. For example, the delivery unit can eliminate the need for redelivery by delivering packages just before the homeowner returns home. The delivery unit can also apply different delivery algorithms depending on the homeowner's lifestyle. Furthermore, the delivery unit can prioritize deliveries based on the homeowner's return time. The integration unit connects with existing systems of delivery companies and apartment management companies to share and analyze data. For example, the integration unit can seamlessly connect with existing systems of delivery companies and apartment management companies using generative AI to share and analyze data. For instance, the integration unit can use generative AI to enhance security authentication through facial and voice recognition. It can also use generative AI to provide an automatic translation function for foreign residents. Furthermore, the integration unit can analyze the social media activities of delivery companies and apartment management companies and share relevant data. As a result, the autonomous delivery robot system according to this embodiment can efficiently collect, receive, deliver, share data on, and analyze packages from delivery companies.

[0064] The consolidation unit consolidates packages from delivery companies. The unit can consolidate packages based on factors such as package type and timing. Specifically, it is equipped with sensors that detect the material and packaging condition of packages to prioritize the consolidation of fragile items. This prevents fragile items such as glass products and electronic devices from being mixed with other packages. The consolidation unit can also filter packages based on their size to optimize space utilization. For example, it can efficiently utilize limited space by using an algorithm that automatically measures package dimensions and calculates the optimal placement. Furthermore, the consolidation unit can select an efficient consolidation method based on package weight. By measuring the weight of packages using weight sensors and placing heavier packages at the bottom and lighter packages at the top, stable loading is achieved. This allows the consolidation unit to provide the optimal consolidation method according to package type, size, and weight, enabling efficient package management. Additionally, the consolidation unit has a function to monitor the status of packages in real time and immediately issue alerts if an anomaly occurs. For example, if a package is damaged or the temperature or humidity changes abnormally, the consolidation unit automatically issues a warning and prompts appropriate action. This allows the consolidation unit to achieve efficient consolidation work while ensuring the safety and quality of the packages.

[0065] The receiving unit receives packages that have been aggregated by the aggregation unit. The receiving unit can receive packages based on, for example, the location and method of receipt. Specifically, the receiving unit uses GPS functionality to obtain precise location information in order to receive packages at a designated location and understands in real time when the delivery truck or drone will arrive. The receiving unit can also select an efficient receiving method considering the delivery schedule of the delivery company. For example, by using an algorithm that analyzes the delivery schedule and calculates the most efficient receiving time slot, waiting times can be minimized. Furthermore, the receiving unit can also select the optimal receiving method by analyzing the delivery history of the delivery company. Based on past delivery data, it predicts delivery patterns for specific time slots and days of the week and proposes the optimal receiving method. This enables the receiving unit to achieve efficient and flexible receiving operations and strengthen cooperation with delivery companies. In addition, the receiving unit also has a function to automatically confirm receipt when a package is received. For example, by scanning a 2D code (e.g., QR code) or barcode, it quickly confirms receipt and records it in a database. This allows the receiving unit to improve the efficiency and accuracy of the receiving operation.

[0066] The delivery department delivers packages received by the receiving department at the optimal time, linked to the homeowner's return information. For example, the delivery department can make deliveries at the optimal time based on the homeowner's return time and traffic conditions. Specifically, it adjusts the delivery timing based on return information obtained from the homeowner's smartphone or smart home device. For example, by making a delivery just before the homeowner returns home, the need for redelivery is eliminated. The delivery department can also apply different delivery algorithms depending on the homeowner's lifestyle. For example, if the homeowner returns home at the same time every day, an algorithm that delivers at that time will be applied. On the other hand, if the homeowner's return time is irregular, an algorithm that obtains return information in real time and calculates the optimal delivery timing will be used. Furthermore, the delivery department can determine the priority of deliveries based on when the homeowner returns home. For example, if the homeowner is away for an extended period, deliveries will be temporarily suspended and resumed after their return. In addition, the delivery department monitors traffic conditions in real time and selects the optimal route to shorten delivery times and achieve efficient deliveries. This allows the delivery department to provide flexible and efficient deliveries linked to the homeowner's return information, reducing the hassle of redeliveries. Furthermore, the delivery department also has a function to automatically confirm deliveries after completion. For example, when the homeowner receives a package, they can confirm receipt via their smartphone or smart home device and record it in the database. This allows the delivery department to improve the efficiency and accuracy of its delivery operations.

[0067] The Integration Department will integrate with existing systems of delivery companies and condominium management companies to share and analyze data. For example, the Integration Department can seamlessly integrate with existing systems of delivery companies and condominium management companies using generative AI to share and analyze data. Specifically, it can use generative AI to enhance security authentication through facial recognition and voice recognition. For example, when a delivery person from a delivery company accesses a condominium, the facial recognition system can be used to verify their identity and prevent unauthorized access. Also, by using voice recognition to receive instructions from delivery persons, manual effort can be saved. Furthermore, the Integration Department can also use generative AI to provide an automatic translation function for foreign residents. For example, when a delivery person makes a delivery to a foreign resident, the automatic translation function can be used to enable smooth communication. In addition, the Integration Department can analyze the social media activities of delivery companies and condominium management companies and share relevant data. For example, it can collect customer feedback and ratings on social media and use it to improve services. In this way, the Integration Department can strengthen collaboration with delivery companies and condominium management companies and achieve efficient data sharing and analysis. Furthermore, the Integration Department can use generative AI to analyze data and support future demand forecasting and service optimization. For example, based on past delivery data, the system predicts delivery demand in specific areas and time slots and proposes the optimal allocation of resources. This allows the collaboration department to support the efficient and effective provision of services through cooperation with delivery companies and apartment management companies.

[0068] The delivery department can make deliveries at the optimal time by linking with the homeowner's return information. For example, the delivery department can adjust the timing of deliveries based on the homeowner's expected return time. For instance, by making a delivery just before the homeowner returns home, the need for redelivery is eliminated. The delivery department can also obtain the homeowner's return information in real time and determine the optimal delivery timing. Furthermore, the delivery department can determine the priority of deliveries based on the homeowner's return information. This, by linking with the homeowner's return information, eliminates the need for redelivery.

[0069] The Integration Department can seamlessly integrate with existing systems of delivery companies and condominium management companies using generative AI, enabling data sharing and analysis. For example, the Integration Department can use generative AI to share and analyze data with existing systems of delivery companies and condominium management companies. For instance, it can use generative AI to analyze delivery data from delivery companies and propose optimal delivery routes. Furthermore, the Integration Department can use generative AI to analyze security data from condominium management companies and strengthen security measures. In addition, the Integration Department can use generative AI to analyze the social media activities of delivery companies and condominium management companies and share relevant data. This improves the accuracy of data sharing and analysis through the use of generative AI.

[0070] The collaborative unit can enhance security authentication using facial recognition and voice recognition through generative AI. For example, the collaborative unit can execute a facial recognition algorithm using generative AI to strengthen security within an apartment building. For instance, the collaborative unit can recognize residents' faces using generative AI and control access to the apartment building. Furthermore, the collaborative unit can execute voice recognition technology using generative AI to recognize residents' voices and perform security authentication. In addition, the collaborative unit can perform security authentication combining facial recognition and voice recognition using generative AI. This improves the accuracy of security authentication by utilizing generative AI.

[0071] The collaborative unit can provide an automatic translation function for foreign residents using generative AI. For example, the collaborative unit can use generative AI to automatically translate the language of foreign residents, facilitating smooth communication. For instance, the collaborative unit can use generative AI to translate the native language of foreign residents into Japanese and provide guidance and notifications within the apartment building. Furthermore, the collaborative unit can use generative AI to translate Japanese into the native language of foreign residents, supporting communication with residents. In addition, the collaborative unit can provide an automatic translation function that supports multiple languages ​​using generative AI. This improves the accuracy of support for foreign residents through the use of generative AI.

[0072] The aggregation unit can estimate the user's emotions and adjust the timing of package aggregation based on the estimated emotions. For example, the aggregation unit can estimate the user's emotions using an emotion analysis algorithm and adjust the timing of package aggregation. For example, if the user is feeling stressed, the aggregation unit can expedite the aggregation time to respond quickly. Conversely, if the user is relaxed, the aggregation unit can delay the aggregation time to respond flexibly. Furthermore, if the user is in a hurry, the aggregation unit can optimize the aggregation time to respond efficiently. This allows for more appropriate aggregation by adjusting the timing of package aggregation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The aggregation unit can analyze the delivery schedules of courier companies and select the optimal aggregation method. For example, the aggregation unit can analyze the past delivery schedules of courier companies to determine the optimal aggregation timing. For example, the aggregation unit can select an efficient aggregation method by considering the real-time delivery schedules of courier companies. The aggregation unit can also compare the delivery schedules of courier companies with the available receiving times of apartments to select the optimal aggregation method. This enables efficient aggregation by analyzing the delivery schedules of courier companies. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without using AI.

[0074] The aggregation unit can filter based on the type and size of the packages. For example, the aggregation unit can prioritize the aggregation of fragile packages based on their type. For example, the aggregation unit can filter based on the size of the packages to make optimal use of space. The aggregation unit can also select an efficient aggregation method based on the weight of the packages. This enables efficient aggregation by filtering based on the type and size of the packages. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without using AI.

[0075] The aggregation unit can estimate the user's emotions and determine the priority of items to aggregate based on the estimated emotions. For example, the aggregation unit can use an emotion analysis algorithm to estimate the user's emotions and determine the priority of items. For instance, if the user is in a hurry, the aggregation unit can prioritize the aggregation of important items. Conversely, if the user is relaxed, the aggregation unit can aggregate items with normal priority. Furthermore, if the user is stressed, the aggregation unit can prioritize the aggregation of specific items to alleviate stress. This allows for more appropriate aggregation by determining the priority of items according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The aggregation unit can prioritize the aggregation of highly relevant packages by considering the geographical location information of the delivery companies. For example, the aggregation unit can prioritize the aggregation of nearby packages based on the geographical location information of the delivery companies. For example, the aggregation unit can efficiently aggregate packages in the same area based on the geographical location information of the delivery companies. Furthermore, the aggregation unit can also prioritize the aggregation of packages that can be aggregated via the shortest route based on the geographical location information of the delivery companies. This enables efficient aggregation by considering the geographical location information of the delivery companies. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without using AI.

[0077] The aggregation unit can analyze the social media activities of delivery companies and aggregate relevant packages. For example, the aggregation unit can analyze the social media activities of delivery companies and prioritize the aggregation of packages related to specific campaigns. For example, the aggregation unit can analyze the social media activities of delivery companies and efficiently aggregate packages related to trends. The aggregation unit can also analyze the social media activities of delivery companies and aggregate packages based on customer interests. This enables efficient aggregation by analyzing the social media activities of delivery companies. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI.

[0078] The receiving unit can estimate the user's emotions and adjust the package delivery method based on the estimated emotions. For example, the receiving unit can use an emotion analysis algorithm to estimate the user's emotions and adjust the package delivery method accordingly. For instance, if the user is stressed, the receiving unit can offer a simple delivery method. If the user is relaxed, it can offer more detailed delivery options. Furthermore, if the user is in a hurry, it can offer a quick delivery method. This allows for more appropriate delivery by adjusting the package delivery method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The receiving unit can analyze the delivery history of the courier company to select the optimal receiving method. For example, the receiving unit can analyze the past delivery history of the courier company and propose the optimal receiving method. For example, the receiving unit can consider the real-time delivery history of the courier company to select an efficient receiving method. The receiving unit can also compare the delivery history of the courier company with the available receiving times of the apartment building to select the optimal receiving method. This enables efficient receiving by analyzing the delivery history of the courier company. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without using AI.

[0080] The receiving unit can adjust the level of detail in receiving packages based on their importance. For example, the receiving unit can provide detailed receiving procedures based on the importance of the package. For example, it can provide detailed receiving procedures for important packages. It can also provide standard receiving procedures for regular packages. Furthermore, it can provide simplified receiving procedures for low-importance packages. This allows for efficient receiving by adjusting the level of detail in receiving packages based on their importance. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI.

[0081] The receiving unit can estimate the user's emotions and determine the priority of packages to be received based on the estimated emotions. For example, the receiving unit can estimate the user's emotions and determine package priorities using an emotion analysis algorithm. For example, if the user is in a hurry, the receiving unit can prioritize receiving important packages. Conversely, if the user is relaxed, the receiving unit can receive packages with normal priority. Furthermore, if the user is stressed, the receiving unit can prioritize receiving specific packages to alleviate that stress. This allows for more appropriate package delivery by determining package priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The receiving unit can prioritize receiving packages that are highly relevant, taking into account the geographical location information of the delivery company. For example, the receiving unit can prioritize receiving nearby packages based on the geographical location information of the delivery company. For example, the receiving unit can efficiently receive packages in the same area based on the geographical location information of the delivery company. Furthermore, the receiving unit can also prioritize receiving packages that can be received via the shortest route based on the geographical location information of the delivery company. In this way, efficient receiving becomes possible by considering the geographical location information of the delivery company. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without using AI.

[0083] The receiving unit can analyze the social media activities of delivery companies and receive relevant packages. For example, the receiving unit can analyze the social media activities of delivery companies and prioritize receiving packages related to specific campaigns. For example, the receiving unit can analyze the social media activities of delivery companies and efficiently receive packages related to trends. The receiving unit can also analyze the social media activities of delivery companies and receive packages based on customer interests. This enables efficient receiving by analyzing the social media activities of delivery companies. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not using AI.

[0084] The delivery unit can estimate the user's emotions and adjust the delivery presentation based on those emotions. For example, the delivery unit can use an emotion analysis algorithm to estimate the user's emotions and adjust the delivery presentation accordingly. For instance, if the user is relaxed, the delivery unit can deliver at a leisurely pace. If the user is in a hurry, the delivery unit can deliver quickly. Furthermore, if the user is excited, the delivery unit can add visually stimulating effects to the delivery. This allows for more appropriate deliveries by adjusting the delivery presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The delivery department can adjust the level of detail in deliveries based on the homeowner's return information. For example, the delivery department can provide detailed delivery information based on the homeowner's return information. For example, the delivery department can provide detailed delivery information just before the homeowner returns home. The delivery department can also provide simplified delivery information if the homeowner is already home. Furthermore, if the homeowner is absent, the delivery department can provide detailed information about redelivery. By adjusting the level of detail in deliveries based on the homeowner's return information, efficient deliveries become possible. Some or all of the above processing in the delivery department may be performed using AI, for example, or without AI.

[0086] The delivery department can apply different delivery algorithms depending on the homeowner's lifestyle. For example, the delivery department can select the optimal delivery time based on the homeowner's lifestyle. For example, the delivery department can make deliveries to coincide with the homeowner's regular return home times. The delivery department can also analyze the homeowner's lifestyle and apply an efficient delivery algorithm. Furthermore, the delivery department can apply different delivery algorithms based on the homeowner's lifestyle. This enables optimal deliveries tailored to the homeowner's lifestyle. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI.

[0087] The delivery unit can estimate the user's emotions and adjust the delivery length based on those emotions. For example, the delivery unit can use an emotion analysis algorithm to estimate the user's emotions and adjust the delivery length accordingly. For instance, if the user is in a hurry, the delivery unit can complete the delivery in a shorter time. Conversely, if the user is relaxed, the delivery unit can deliver within the normal timeframe. Furthermore, if the user is excited, the delivery unit can add visually stimulating effects to the delivery. This allows for more appropriate deliveries by adjusting the delivery length according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The delivery department can determine delivery priorities based on when the homeowner returns home. For example, the delivery department can prioritize the delivery of important packages based on when the homeowner returns home. For example, the delivery department can prioritize the delivery of important packages just before the homeowner returns home. The delivery department can also deliver packages with normal priority if the homeowner is already home. Furthermore, the delivery department can determine the priority for redelivery if the homeowner is absent. This enables efficient delivery by determining delivery priorities based on when the homeowner returns home. Some or all of the above processes in the delivery department may be performed using AI, for example, or not.

[0089] The delivery department can adjust the delivery order based on the homeowner's relevance. For example, the delivery department can prioritize the delivery of important packages based on the homeowner's relevance. For instance, if a homeowner needs to receive an important package, the delivery department can prioritize its delivery. The delivery department can also deliver regular packages in a standard order. Furthermore, the delivery department can postpone deliveries of less important packages to homeowners. This allows for efficient delivery by adjusting the delivery order based on the homeowner's relevance. Some or all of the above processes in the delivery department may be performed using AI, for example, or not.

[0090] The collaboration unit can estimate the user's emotions and adjust the data sharing method based on the estimated emotions. For example, the collaboration unit can estimate the user's emotions using an emotion analysis algorithm and adjust the data sharing method accordingly. For instance, if the user is stressed, the collaboration unit can provide a simple data sharing method. If the user is relaxed, it can also provide more detailed data sharing options. Furthermore, if the user is in a hurry, it can provide a rapid data sharing method. This allows for more appropriate data sharing by adjusting the data sharing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The integration unit can select the optimal sharing method by referring to the history of existing systems of delivery companies and condominium management companies. For example, the integration unit can analyze the past data sharing history of delivery companies and propose the optimal sharing method. For example, the integration unit can refer to the history of existing systems of condominium management companies and select an efficient data sharing method. Furthermore, the integration unit can integrate the data sharing history of delivery companies and condominium management companies and select the optimal sharing method. This enables efficient data sharing by referring to the history of existing systems of delivery companies and condominium management companies. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0092] The collaboration unit can adjust the level of detail shared based on the attribute information of delivery companies and condominium management companies. For example, the collaboration unit can prioritize the sharing of important data based on the attribute information of delivery companies. For example, the collaboration unit can share detailed data based on the attribute information of condominium management companies. The collaboration unit can also integrate the attribute information of delivery companies and condominium management companies and select the optimal data sharing method. This enables efficient data sharing by adjusting the level of detail shared based on the attribute information of delivery companies and condominium management companies. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without using AI.

[0093] The collaboration unit can estimate the user's emotions and determine the priority of data sharing based on the estimated emotions. For example, the collaboration unit can use an emotion analysis algorithm to estimate the user's emotions and determine the priority of data sharing. For instance, if the user is in a hurry, the collaboration unit can prioritize sharing important data. Conversely, if the user is relaxed, the collaboration unit can share data with the normal priority. Furthermore, if the user is stressed, the collaboration unit can prioritize sharing specific data to alleviate that stress. This allows for more appropriate data sharing by prioritizing data sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The collaboration unit can prioritize the sharing of highly relevant data by considering the geographical location information of delivery companies and condominium management companies. For example, the collaboration unit can prioritize the sharing of nearby data based on the geographical location information of delivery companies. For example, the collaboration unit can efficiently share data within the same area based on the geographical location information of condominium management companies. Furthermore, the collaboration unit can integrate the geographical location information of delivery companies and condominium management companies and select the optimal data sharing method. This enables efficient data sharing by considering the geographical location information of delivery companies and condominium management companies. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without using AI.

[0095] The Collaboration Department can analyze the social media activities of delivery companies and condominium management companies and share relevant data. For example, the Collaboration Department can analyze the social media activities of delivery companies and prioritize the sharing of data related to specific campaigns. For example, the Collaboration Department can analyze the social media activities of condominium management companies and efficiently share data related to trends. The Collaboration Department can also integrate the social media activities of delivery companies and condominium management companies and select the optimal data sharing method. This enables efficient data sharing by analyzing the social media activities of delivery companies and condominium management companies. Some or all of the above processing in the Collaboration Department may be performed using AI, for example, or not using AI.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The autonomous delivery robot system can also be equipped with an environmental sensor unit. This unit can acquire environmental information such as temperature, humidity, and illuminance within the apartment building, and optimize delivery timing and routes based on this information. For example, if the temperature inside the apartment building is high, the unit can expedite deliveries to prevent package deterioration. If the humidity is high, it can prioritize the delivery of packages sensitive to moisture. Furthermore, if the illuminance is low, it can select routes that pass through well-lit areas for safety reasons. This enables efficient deliveries that take environmental information into consideration.

[0098] The autonomous delivery robot system can also be equipped with a health monitoring unit. This unit can monitor the homeowner's health and select a delivery method appropriate to their condition. For example, if the homeowner is unwell, the unit can either postpone the delivery or select a delivery method that takes their health into consideration. Furthermore, if the homeowner is elderly and requires assistance during delivery, the unit can arrange for support staff. Additionally, if the homeowner is exercising, the delivery can be temporarily suspended and resumed after the exercise is complete. This enables flexible delivery tailored to the homeowner's health condition.

[0099] Autonomous delivery robot systems can also be equipped with an energy management unit. This unit manages the robot's battery level and the location of charging stations, enabling efficient energy use. For example, if the battery level is low, the unit can move to the nearest charging station to recharge. It can also optimize delivery routes to minimize energy consumption. Furthermore, the energy management unit can comprehensively manage the battery levels of multiple robots and distribute energy efficiently, enabling energy-efficient deliveries.

[0100] The autonomous delivery robot system can also be equipped with an emergency response unit. This unit can respond quickly to emergencies within the apartment building. For example, in the event of a fire, earthquake, or other disaster, the emergency response unit can guide residents along evacuation routes and ensure their safety. It can also coordinate with medical institutions to provide a rapid response if emergency medical care is needed. Furthermore, the emergency response unit can integrate with the building's security system to detect intruders and issue alarms. This improves the safety of the apartment building and increases residents' sense of security.

[0101] Autonomous delivery robot systems can also be equipped with an entertainment unit. This unit can provide entertainment content to residents during deliveries, enhancing the delivery experience. For example, it can play music or videos to entertain residents during deliveries. The delivery robot can also interact with residents, offering quizzes and games. Furthermore, the entertainment unit can provide special content tailored to the season or events. This transforms deliveries from mere package handover into an enjoyable experience.

[0102] The autonomous delivery robot system can further utilize emotion estimation capabilities to provide delivery services based on residents' emotions. For example, if a resident is feeling stressed, the delivery unit can play relaxing music during delivery. If a resident is happy, the delivery robot can deliver a congratulatory message. Furthermore, if a resident is sad, the delivery robot can offer words of encouragement. This provides a delivery service that is sensitive to residents' emotions, thereby improving resident satisfaction.

[0103] The autonomous delivery robot system can further utilize emotion estimation capabilities to select delivery routes based on the residents' emotions. For example, if a resident is in a hurry, the delivery unit can choose the shortest route. If the resident is relaxed, it can select a scenic route. Furthermore, if the resident is feeling anxious, it can select a safer route. This ensures that the optimal delivery route is selected in accordance with the resident's emotions, improving the delivery experience.

[0104] The autonomous delivery robot system can further adjust delivery times based on residents' emotions using emotion estimation capabilities. For example, if a resident is tired, the delivery unit can delay the delivery to prioritize rest. Conversely, if the resident is energetic, it can deliver earlier. Furthermore, if a resident is busy, the delivery time can be adjusted to fit the resident's schedule. This provides flexible delivery times that respond to residents' emotions, improving convenience for residents.

[0105] The autonomous delivery robot system can also use emotion estimation capabilities to provide delivery notifications based on the resident's emotions. For example, if the resident is relaxed, the delivery unit can send a delivery notification in a calm tone. If the resident is in a hurry, it can send a delivery notification in a quick tone. Furthermore, if the resident is excited, it can send a delivery notification in a cheerful tone. This allows for delivery notifications tailored to the resident's emotions, improving the delivery experience.

[0106] The autonomous delivery robot system can also collect delivery feedback based on residents' emotions using emotion estimation capabilities. For example, the delivery unit can collect positive feedback if residents are satisfied. It can also collect suggestions for improvement if residents are dissatisfied. Furthermore, if residents have neutral emotions, it can collect general feedback. This allows for the collection of emotion-based feedback, which can be used to improve delivery services.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The consolidation unit consolidates packages from delivery companies. The consolidation unit can consolidate packages based on their type and timing. For example, it can prioritize the consolidation of fragile packages, filter packages based on their size to optimize space utilization, and select an efficient consolidation method based on their weight. Step 2: The receiving unit receives the packages aggregated by the aggregation unit. The receiving unit can receive packages based on the location and method of receipt. For example, it can receive packages at a designated location, select an efficient receiving method considering the delivery schedule of the courier company, or select the optimal receiving method by analyzing the delivery history of the courier company. Step 3: The delivery department delivers packages received by the receiving department at the optimal time, linked to the homeowner's return home information. The delivery department can make deliveries at the optimal time based on the homeowner's return time and traffic conditions. For example, by delivering just before the homeowner returns home, the need for redelivery is eliminated, and different delivery algorithms can be applied depending on the homeowner's lifestyle. Furthermore, delivery priorities can be determined based on when the homeowner returns home. Step 4: The Integration Department integrates with existing systems of delivery companies and apartment management companies to share and analyze data. The Integration Department can seamlessly integrate with existing systems of delivery companies and apartment management companies using generative AI to share and analyze data. For example, it can use generative AI to enhance security authentication through facial recognition and voice recognition, provide automatic translation functions for foreign residents, and analyze the social media activities of delivery companies and apartment management companies to share relevant data.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the aggregation unit, receiving unit, delivery unit, and collaboration unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the aggregation unit is implemented by the control unit 46A of the smart device 14 and aggregates packages based on package type and timing. The receiving unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and receives the aggregated packages. The delivery unit is implemented by, for example, the control unit 46A of the smart device 14 and delivers packages at the optimal timing in conjunction with the homeowner's return information. The collaboration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and seamlessly collaborates with existing systems of delivery companies and condominium management companies to share and analyze data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the aggregation unit, receiving unit, delivery unit, and linking unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the aggregation unit is implemented by the control unit 46A of the smart glasses 214 and aggregates packages based on package type and timing. The receiving unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and receives the aggregated packages. The delivery unit is implemented by, for example, the control unit 46A of the smart glasses 214 and delivers packages at the optimal timing in conjunction with the homeowner's return information. The linking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and seamlessly links with existing systems of delivery companies and condominium management companies to share and analyze data. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the aggregation unit, receiving unit, delivery unit, and collaboration unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the aggregation unit is implemented by the control unit 46A of the headset terminal 314 and aggregates packages based on package type and timing. The receiving unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and receives the aggregated packages. The delivery unit is implemented by, for example, the control unit 46A of the headset terminal 314 and delivers packages at the optimal timing in conjunction with the homeowner's return information. The collaboration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and seamlessly collaborates with existing systems of delivery companies and condominium management companies to share and analyze data. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the aggregation unit, receiving unit, delivery unit, and coordination unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the aggregation unit is implemented by the control unit 46A of the robot 414 and aggregates packages based on the type and timing of the packages. The receiving unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and receives the aggregated packages. The delivery unit is implemented by, for example, the control unit 46A of the robot 414 and delivers packages at the optimal timing in conjunction with the homeowner's return information. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and seamlessly cooperates with existing systems of delivery companies and condominium management companies to share and analyze data. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A consolidation area where packages from delivery companies are collected, A receiving unit that receives packages aggregated by the aggregation unit, A delivery unit delivers packages received by the aforementioned receiving unit at the optimal time, linked to the homeowner's return information. It includes a collaboration unit that integrates with existing systems of delivery companies and apartment management companies to share and analyze data. A system characterized by the following features. (Note 2) The aforementioned delivery department, The delivery system is linked to the homeowner's return home information to ensure optimal timing for delivery. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned linkage unit is, Using generation AI, we seamlessly integrate with existing systems of delivery companies and apartment management companies to facilitate data sharing and analysis. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned linkage unit is, Using generative AI to enhance security authentication through facial and voice recognition. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, We provide an automatic translation function for foreign residents using generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned aggregation unit is The system estimates the user's emotions and adjusts the timing of package consolidation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned aggregation unit is Analyze the delivery schedules of courier companies and select the optimal consolidation method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned aggregation unit is Filter based on the type and size of the package. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned aggregation unit is It estimates the user's emotions and determines the priority of items to be aggregated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned aggregation unit is Considering the geographical location information of delivery companies, the system prioritizes consolidating highly relevant packages. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned aggregation unit is Analyze the social media activity of delivery companies and aggregate related packages. The system described in Appendix 1, characterized by the features described herein. (Note 12) The receiving section is, It estimates the user's emotions and adjusts the package delivery method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The receiving section is, We analyze the delivery history of courier companies to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The receiving section is, The level of detail required for receiving a package is adjusted based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The receiving section is, It estimates the user's emotions and determines the priority of packages to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The receiving section is, The system prioritizes receiving packages that are highly relevant to the recipient, taking into account the delivery company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The receiving section is, Analyze the social media activity of delivery companies and receive related packages. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned delivery department, The system estimates the user's emotions and adjusts the delivery presentation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned delivery department, Adjust the level of detail in the delivery based on the homeowner's return information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned delivery department, Apply different delivery algorithms depending on the homeowner's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned delivery department, The system estimates the user's emotions and adjusts the delivery length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned delivery department, We prioritize deliveries based on when the homeowner is expected to return home. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned delivery department, We adjust the delivery order based on the homeowner's relationship. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, It estimates user sentiment and adjusts data sharing methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, We will select the optimal sharing method by referring to the history of existing systems of delivery companies and apartment management companies. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, Adjust the level of detail shared based on attribute information of delivery companies and apartment management companies. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, It estimates user sentiment and determines data sharing priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, Prioritize sharing highly relevant data, taking into account the geographical location information of delivery companies and apartment management companies. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, We analyze the social media activities of delivery companies and apartment management companies and share relevant data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A consolidation area where packages from delivery companies are collected, A receiving unit that receives packages aggregated by the aggregation unit, A delivery unit delivers packages received by the aforementioned receiving unit at the optimal time, linked to the homeowner's return information. It includes a collaboration unit that integrates with existing systems of delivery companies and apartment management companies to share and analyze data. A system characterized by the following features.

2. The aforementioned delivery department, The delivery system is linked to the homeowner's return home information to ensure optimal timing for delivery. The system according to feature 1.

3. The aforementioned linkage unit is, Using AI-generated data, we seamlessly integrate with existing systems of delivery companies and apartment management companies to facilitate data sharing and analysis. The system according to feature 1.

4. The aforementioned linkage unit is, Enhance security authentication using generative AI, facial recognition, and voice recognition. The system according to feature 1.

5. The aforementioned linkage unit is, We provide an automatic translation function for foreign residents using generation AI. The system according to feature 1.

6. The aforementioned aggregation unit is The system estimates the user's emotions and adjusts the timing of package consolidation based on those emotions. The system according to feature 1.

7. The aforementioned aggregation unit is Analyze the delivery schedules of courier companies and select the optimal consolidation method. The system according to feature 1.

8. The aforementioned aggregation unit is Filter based on the type and size of the package. The system according to feature 1.

9. The aforementioned aggregation unit is It estimates the user's emotions and determines the priority of items to be aggregated based on the estimated user emotions. The system according to feature 1.

10. The aforementioned aggregation unit is Considering the geographical location information of delivery companies, the system prioritizes consolidating highly relevant packages. The system according to feature 1.

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