System

Abstract

To provide a system for enabling persons who remain following the death of a decedent, to mitigate their feelings of loss and to recover their mental health.SOLUTION: A system includes a learning part, an incorporating part, an instruction part, an interactive part, and a stop part. The learning part generates AI having learned a user's voice, way of speaking, and personality. The incorporating part incorporates the AI generated by the learning part in a tablet terminal. The instruction part instructs delivery of a tablet terminal to a person specified by a user, after the death of the user. The interactive part interacts with a person specified by the user, by using the AI incorporated in the tablet terminal. The stop part stops services according to AI after the lapse of a predetermined period after the death of the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] There is room for improvement in that the current technology does not adequately provide adequate means for those left behind to ease their sense of loss after the death of a deceased person.

[0005] The system of the embodiment aims to help those left behind after the death of a deceased person to ease their sense of loss and regain their mental health. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, an embedding unit, an instruction unit, a dialogue unit, and a stopping unit. The learning unit generates an AI that has learned the user's voice, speaking style, and personality. The embedding unit embeds the AI ​​generated by the learning unit into a tablet device. The instruction unit instructs the tablet device to be delivered to a person designated by the user after the user's death. The dialogue unit uses the AI ​​embedded in the tablet device to dialogue with the person designated by the user. The stopping unit stops the AI's service a predetermined period of time after the user's death. Effect of the Invention

[0007] The system according to the embodiment can ease the sense of loss left behind after the death of a loved one and help them regain their mental health. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system that aims to ease the sense of loss and restore mental health by creating one's own AI on a dedicated tablet while alive, and delivering the tablet to a designated person after death, thereby interacting with the deceased's AI. This system generates an AI that learns the user's voice, speech style, and personality, incorporates the generated AI into a tablet terminal, and delivers it to the designated person after the user's death. When the tablet terminal arrives at the designated person's place, a dialogue is conducted using the AI, and the user can feel as if they are interacting with the deceased. This is expected to ease the sense of loss and restore mental health. Furthermore, when a predetermined period (for example, 49 days) has passed since the death of the user, the service by the AI ​​is stopped. When this period has passed, the tablet terminal stops the service and it becomes impossible to interact with the AI. When the service is stopped, the system also has a function to request the return of the tablet terminal. For example, by returning the tablet terminal, reuse or appropriate processing is performed. In addition, the system also has a function to delete data when the service is stopped, thereby protecting privacy. In this way, the present invention provides a system that aims to ease the sense of loss and restore mental health by interacting with the deceased's AI. By generating an AI that learns the user's voice, speaking style, and personality and incorporating it into a tablet device, the user can feel as if they are talking to the deceased. The system also has functions such as suspending the service, returning the tablet device, and deleting data, ensuring privacy protection and appropriate processing. This allows the system to ease the sense of loss and restore mental health by talking to the AI ​​of the deceased.

[0029] The system according to the embodiment includes a learning unit, an embedding unit, an instruction unit, a dialogue unit, and a stopping unit. The learning unit generates an AI that has learned the user's voice, speech style, and personality. The user's voice includes, for example, a method for collecting voice samples and voice features, but is not limited to such examples. The learning unit, for example, records the user's voice and extracts voice features using voice recognition technology. The learning unit also analyzes the user's speech style. The speech style includes, for example, speech speed, intonation, and accent, but is not limited to such examples. The learning unit, for example, records the user's speech style and analyzes the speech style features using natural language processing technology. Furthermore, the learning unit evaluates the user's personality. The personality includes, for example, a personality diagnostic test and an analysis of behavioral patterns, but is not limited to such examples. The learning unit, for example, administers a personality diagnostic test to the user and evaluates the personality based on the results. The embedding unit embeds the AI ​​generated by the learning unit into a tablet terminal. The embedding includes, for example, a software installation procedure and a hardware setting method, but is not limited to such examples. The embedding unit, for example, installs AI software in the tablet terminal and performs necessary settings. The instruction unit instructs the delivery of the tablet terminal to a person designated by the user after the death of the user. The delivery includes, for example, but is not limited to, the selection of a delivery company and a delivery schedule. The instruction unit, for example, checks the address of the person designated by the user, selects an appropriate delivery company, and instructs the delivery. The dialogue unit uses AI to have a dialogue when the tablet terminal arrives at the designated person. The dialogue includes, for example, but is not limited to, a dialogue scenario and a natural language processing technology to be used. For example, the dialogue unit reproduces the voice and speaking style of the deceased person and has a dialogue with the designated person. The stopping unit stops the service by AI when a predetermined period has passed after the death of the user. The service stopping includes, for example, but is not limited to, a trigger condition for stopping and a process after stopping. For example, the stopping unit stops the service 49 days after the death of the user and requests the return of the tablet terminal. As a result, the system of the embodiment uses AI that has learned the user's voice, speaking style, and personality to provide the feeling of interacting with the deceased, thereby easing the sense of loss.

[0030] The learning unit generates an AI that has learned the user's voice, speaking style, and personality. The user's voice includes, for example, a method for collecting voice samples and voice features, but is not limited to such examples. The learning unit, for example, records the user's voice and extracts voice features using voice recognition technology. Specifically, the user's voice is recorded with a high-quality microphone, and the voice data is analyzed using digital signal processing technology. Voice features include fundamental frequency, formant, and spectral energy distribution. These features are important for expressing the individuality of the user's voice. In addition, the learning unit analyzes the user's speaking style. The speaking style includes, for example, speech speed, intonation, and accent, but is not limited to such examples. For example, the learning unit records the user's speaking style and analyzes the characteristics of the speaking style using natural language processing technology. Specifically, the recording data of the user's speaking style is converted into text, and the text data is analyzed to extract patterns of speaking speed and intonation. Furthermore, the learning unit evaluates the user's personality. The personality includes, for example, a personality diagnostic test and an analysis of behavioral patterns, but is not limited to such examples. The learning unit, for example, administers a personality diagnostic test to the user and evaluates the personality based on the results. Specifically, the results of the personality diagnostic test are quantified and the numerical data is input into the AI ​​model to evaluate the personality. This allows the learning unit to comprehensively learn the user's voice, speaking style, and personality, and generate an AI that resembles the user.

[0031] The embedding unit embeds the AI ​​generated by the learning unit into the tablet terminal. Embedding includes, for example, software installation procedures and hardware configuration methods, but is not limited to such examples. Specifically, first, the AI ​​software is downloaded to the tablet terminal and installed. After installation, necessary settings are made so that the AI ​​software operates normally. For example, compatibility between the operating system of the tablet terminal and the AI ​​software is confirmed, and necessary drivers and libraries are installed. In addition, settings are made for the audio output device and microphone so that the AI ​​software reproduces the user's voice and speaking style. Furthermore, network settings are made for the tablet terminal so that the AI ​​software can communicate with the cloud server. This allows the AI ​​software to access a database on the cloud and obtain necessary data. The embedding unit reliably executes these procedures and verifies that the AI ​​software operates normally on the tablet terminal. This allows the embedding unit to embed the AI ​​generated by the learning unit into the tablet terminal and provide it to the user.

[0032] The instruction unit instructs the delivery of the tablet terminal to a person designated by the user after the death of the user. The delivery includes, for example, the selection of a delivery company and a delivery schedule, but is not limited to such examples. Specifically, first, the address of the person designated by the user is confirmed, and a company capable of delivery to the address is selected. The selected company is given detailed instructions on how to handle the tablet terminal and the delivery schedule. For example, appropriate packaging materials are used to prevent the tablet terminal from being damaged, and impact during delivery is minimized. In addition, regarding the delivery schedule, the date and time at which the designated person can certainly receive the tablet terminal are adjusted, and instructions are given to the delivery company. Furthermore, the instruction unit tracks the delivery status in real time, and notifies the designated person of the delivery status. As a result, the designated person can understand the expected date and time of arrival of the tablet terminal and can prepare for reception. The instruction unit reliably executes these procedures, and ensures that the tablet terminal is delivered to the designated person after the death of the user. As a result, the instruction unit respects the user's intention and can smoothly deliver the tablet terminal.

[0033] When the tablet terminal arrives at the designated person's place, the dialogue unit uses AI to engage in dialogue. The dialogue includes, for example, a dialogue scenario and a natural language processing technology to be used, but is not limited to such examples. Specifically, the AI ​​reproduces the voice and manner of speech of the deceased and engages in dialogue with the designated person. The dialogue scenario is created based on the contents set by the user before their death. For example, it includes a message that the deceased wanted to convey before their death and dialogue content according to a specific situation. The AI ​​uses natural language processing technology to engage in natural dialogue based on these scenarios. Specifically, the AI ​​converts the utterance of the designated person into text using voice recognition technology, analyzes the text, and generates an appropriate response. The generated response is played back in the voice of the deceased using voice synthesis technology. This allows the designated person to feel as if they are having a dialogue with the deceased. Furthermore, the dialogue unit can flexibly change the AI's response depending on the content and situation of the dialogue. For example, if the designated person shows an emotional reaction, the AI ​​generates a response according to that emotion and continues the dialogue. This allows the dialogue unit to provide the designated person with support to ease their sense of loss through dialogue with the deceased.

[0034] The stop unit stops the service by the AI ​​when a predetermined period of time has passed after the death of the user. The service stop includes, for example, trigger conditions for the stop and processing after the stop, but is not limited to such examples. Specifically, the service is stopped 49 days after the death of the user, and the tablet terminal is requested to be returned. The trigger conditions for the stop include the submission of a death certificate of the user and a notification from a designated person. When these conditions are met, the stop unit starts a procedure to stop the service by the AI. Specifically, first, the AI ​​software on the tablet terminal is stopped and the provision of the service is terminated. Next, the designated person is requested to return the tablet terminal and is notified of the details of the return procedure. The return procedure includes, for example, the provision of packaging materials for return and notification of the return address. Furthermore, the stop unit erases the data of the returned tablet terminal and takes measures to protect privacy. As a result, the stop unit can stop the service by the AI ​​at an appropriate time after the death of the user and ensure the return of the tablet terminal. As a result, the stop unit can respect the user's wishes and appropriately terminate the provision of the service.

[0035] The tablet terminal includes a request unit that requests the return of the tablet terminal when the AI-based service is stopped by the stop unit. The request unit requests the return of the tablet terminal when the service is stopped. The return request includes, for example, a method of notifying the return request and a return deadline, but is not limited to such examples. For example, the request unit displays a message of the return request on the screen of the tablet terminal and notifies the return deadline. The request unit can also send the return request notification by email or SMS. For example, the request unit sends the return request notification based on contact information of a person designated by the user. Furthermore, the request unit can send the return request notification by mail. For example, the request unit sends the return request notification by mail and clearly indicates the return deadline. In this way, the tablet terminal can be reused or appropriately processed by requesting the return of the tablet terminal after the service is stopped.

[0036] The tablet terminal includes a deletion unit that deletes data when the AI-based service is stopped by the stopping unit. The deletion unit deletes data when the service is stopped. Data deletion includes, for example, the type of data to be deleted and a deletion procedure, but is not limited to such examples. The deletion unit deletes, for example, user data and dialogue history in the tablet terminal. The deletion unit can also automate the data deletion procedure. For example, the deletion unit automatically executes data deletion when the service is stopped. Furthermore, the deletion unit can also confirm data deletion. For example, the deletion unit displays a confirmation message before deleting data, and executes deletion after obtaining the user's consent. In this way, privacy is protected by deleting data after the service is stopped.

[0037] The learning unit analyzes the user's past dialogue history and selects an appropriate learning algorithm. The learning unit analyzes the user's past dialogue history and selects an optimal learning algorithm. The dialogue history analysis includes, for example, natural language processing technology and analysis standards, but is not limited to such examples. The learning unit analyzes, for example, words and phrases frequently used by the user and adjusts the learning algorithm based on the results. The learning unit can also analyze the user's dialogue pattern and select an optimal learning algorithm. For example, the learning unit selects a learning algorithm corresponding to a specific emotional state from the user's dialogue history. Furthermore, the learning unit can customize the learning data based on the analysis result of the dialogue history. For example, the learning unit optimizes the learning data based on information obtained from the user's dialogue history. In this way, the optimal learning algorithm can be selected by analyzing the past dialogue history.

[0038] The learning unit customizes the learning data based on the user's lifestyle and hobbies. The learning unit customizes the learning data based on the user's lifestyle and hobbies. Lifestyle includes, for example, daily behavior patterns and health data, but is not limited to such examples. The learning unit learns, for example, conversation data conducted during a specific time period based on the user's lifestyle. The learning unit can also preferentially learn conversation data related to the user's hobbies. For example, the learning unit collects conversation data related to the user's hobby activities and reflects the collected data in the learning data. Furthermore, the learning unit can customize and learn related conversation data based on the user's interests and concerns. For example, the learning unit collects data related to topics that interest the user and incorporates it into the learning data. This allows the learning data to be customized based on the user's lifestyle and hobbies, thereby generating a more personalized AI.

[0039] The learning unit analyzes the user's social media activity and reflects it in the learning data. The learning unit analyzes the user's social media activity and reflects it in the learning data. The analysis of social media activity includes, for example, an analysis of the posting content and an analysis of the followers, but is not limited to such examples. The learning unit, for example, reflects the words and phrases that the user frequently uses on social media in the learning data. The learning unit can also analyze the user's activity pattern on social media and reflect it in the learning data. For example, the learning unit analyzes the posting content of the user and customizes the learning data based on the user's interests and concerns. Furthermore, the learning unit can optimize the learning data based on the information the user shares on social media. For example, the learning unit analyzes the reaction of the user's followers and reflects it in the learning data. In this way, by analyzing social media activity, it is possible to generate learning data that is more in line with reality.

[0040] The learning unit incorporates a language and culture specific to a region into the learning data based on the geographical location information of the user. The learning unit incorporates a language and culture specific to a region into the learning data, taking into account the geographical location information of the user. Collection of geographical location information includes, for example, collection of GPS data and a method of analyzing location information, but is not limited to such examples. The learning unit incorporates, for example, dialects and expressions of the region where the user lives into the learning data. In addition, the learning unit can also reflect a culture and customs specific to a region into the learning data based on the geographical location information of the user. For example, the learning unit incorporates information on places frequently visited by the user into the learning data. Furthermore, the learning unit can also reflect information on events and occasions specific to a region into the learning data. For example, the learning unit collects data on festivals and traditional events in the region and incorporates it into the learning data. In this way, an AI that reflects a language and culture specific to a region can be generated by taking into account the geographical location information.

[0041] The embedded unit generates an AI optimized for the hardware characteristics of the tablet terminal. The embedded unit generates an AI optimized for the hardware characteristics of the tablet terminal. Optimization of the hardware characteristics includes, for example, CPU performance and memory capacity, but is not limited to such examples. The embedded unit generates an AI optimized for, for example, the processor performance of the tablet terminal. The embedded unit can also generate an AI optimized for the memory capacity of the tablet terminal. For example, the embedded unit optimizes the memory usage of the tablet terminal to achieve efficient operation. Furthermore, the embedded unit can also generate an AI optimized in consideration of the battery life of the tablet terminal. For example, the embedded unit sets the operation of the AI ​​to a low power consumption mode to reduce battery consumption. As a result, the performance of the terminal can be maximized by generating an AI optimized for the hardware characteristics of the tablet terminal.

[0042] The embedded unit customizes the operation mode of the AI ​​according to the user's usage environment. The embedded unit customizes the operation mode of the AI ​​according to the user's usage environment. Customization of the usage environment includes, for example, indoor and outdoor environments and applications used, but is not limited to such examples. For example, when the user uses it outdoors, the embedded unit provides an operation mode that reduces battery consumption. In addition, when the user uses it indoors, the embedded unit can also provide a high-performance operation mode. For example, the embedded unit automatically adjusts the operation mode of the AI ​​according to the user's usage environment. Furthermore, when the user uses it while moving, the embedded unit can also adjust the operation mode in real time. For example, the embedded unit selects the optimal operation mode based on the user's location information. As a result, by customizing the operation mode according to the usage environment, it is possible to provide optimal operation according to the user's needs.

[0043] The embedded unit adjusts the operation of the AI ​​in consideration of the battery life of the tablet terminal. The embedded unit adjusts the operation of the AI ​​in consideration of the battery life of the tablet terminal. Optimization of battery life includes, for example, battery consumption patterns and power saving modes, but is not limited to such examples. For example, the embedded unit sets the operation of the AI ​​to a low power consumption mode in order to reduce battery consumption. The embedded unit can also optimize the operation of the AI ​​in order to extend the battery life. For example, the embedded unit automatically adjusts the operation mode of the AI ​​according to the remaining battery power. Furthermore, the embedded unit can analyze the battery consumption pattern and select the optimal operation mode. For example, the embedded unit optimizes the operation of the AI ​​based on the battery consumption pattern. As a result, the use time of the terminal can be extended by optimizing the operation of the AI ​​in consideration of the battery life.

[0044] The embedded unit incorporates an additional function for enhancing the security function of the tablet terminal. The embedded unit incorporates an additional function for enhancing the security function of the tablet terminal. Enhancement of the security function includes, for example, encryption technology and authentication methods, but is not limited to such examples. For example, the embedded unit enhances the security of the tablet terminal by incorporating a face recognition function using AI. The embedded unit can also incorporate a fingerprint authentication function using AI. For example, the embedded unit enhances access control of the tablet terminal using fingerprint authentication technology. Furthermore, the embedded unit can also incorporate a voice recognition function using AI. For example, the embedded unit enhances the security of the tablet terminal using voice recognition technology. This enhances the security function, thereby improving the safety of the terminal.

[0045] The instruction unit issues a delivery instruction taking into consideration the time period when the designated person can receive the parcel. The instruction unit issues a delivery instruction taking into consideration the time period when the designated person can receive the parcel. Consideration of the time period when the designated person can receive the parcel includes, for example, a designated time period and a date when the parcel can be received, but is not limited to such examples. The instruction unit issues an optimal delivery time taking into consideration the time period when the designated person can receive the parcel. The instruction unit can also issue a delivery instruction during the time period when the designated person can receive the parcel by referring to the schedule of the designated person. For example, the instruction unit issues an optimal delivery time by referring to the past reception history of the designated person. Furthermore, the instruction unit can also optimize the delivery schedule based on the time period when the parcel can be received. For example, the instruction unit issues an instruction to a delivery company based on the time period when the designated person can receive the parcel. In this way, the time period when the parcel can be received is taken into consideration, thereby ensuring that the designated person can receive the parcel.

[0046] The instruction unit selects the optimal delivery method in consideration of the geographical conditions of the delivery destination. The instruction unit selects the optimal delivery method in consideration of the geographical conditions of the delivery destination. Consideration of geographical conditions includes, for example, the topography and traffic conditions of the delivery destination, but is not limited to such examples. For example, when the delivery destination is an urban area, the instruction unit selects a quick delivery method. Furthermore, when the delivery destination is a suburban area, the instruction unit can also select an efficient delivery method. For example, when the delivery destination is a remote area, the instruction unit selects the optimal delivery method. Furthermore, the instruction unit can optimize the delivery schedule based on the geographical conditions. For example, the instruction unit issues instructions to a delivery company based on the geographical conditions of the delivery destination. In this way, efficient and quick delivery is realized by considering the geographical conditions.

[0047] The instruction unit automatically checks the contact information of the designated person and issues a delivery instruction. The instruction unit automatically checks the contact information of the designated person and issues a delivery instruction. The contact information includes, for example, a telephone number and an email address, but is not limited to such examples. The instruction unit automatically checks the contact information of the designated person and issues a delivery instruction. In addition, when the contact information of the designated person is changed, the instruction unit can automatically update the contact information and issue a delivery instruction. For example, the instruction unit detects a change in the contact information in real time and updates the delivery instruction. Furthermore, the instruction unit can select an optimal delivery method based on the contact information. For example, the instruction unit refers to the contact information of the designated person and selects an optimal delivery company. In this way, accurate delivery instructions can be issued by automatically checking the contact information of the designated person.

[0048] The instruction unit adds a function of tracking the delivery status in real time. The instruction unit adds a function of tracking the delivery status in real time. Delivery status tracking includes, for example, GPS tracking and a delivery status update method, but is not limited to such examples. For example, the instruction unit tracks the delivery status in real time and notifies a designated person. The instruction unit can also track the delivery status in real time and notify a user. For example, the instruction unit monitors the progress of the delivery status in real time and notifies a user. Furthermore, the instruction unit can select an optimal delivery method based on the delivery status. For example, the instruction unit tracks the delivery status in real time and selects an optimal delivery company. This allows the progress of delivery to be confirmed by tracking the delivery status in real time.

[0049] The dialogue unit customizes the tone of the dialogue according to the current situation of the designated person. The dialogue unit customizes the tone of the dialogue according to the current situation of the designated person. Customization of the dialogue tone includes, for example, the atmosphere and the use of words in the dialogue, but is not limited to such examples. For example, if the designated person is relaxed, the dialogue unit conducts the dialogue in a calm tone. In addition, if the designated person is stressed, the dialogue unit can also conduct the dialogue in an encouraging tone. For example, the dialogue unit dynamically adjusts the tone of the dialogue according to the emotional state of the designated person. Furthermore, if the designated person is excited, the dialogue unit can also conduct the dialogue in a calming tone. For example, the dialogue unit monitors the emotional state of the designated person in real time and optimizes the tone of the dialogue. This allows a more appropriate dialogue to be provided by customizing the tone of the dialogue according to the situation of the designated person.

[0050] The dialogue unit analyzes the social media activity of the designated person and reflects it in the dialogue content. The dialogue unit analyzes the social media activity of the designated person and reflects it in the dialogue content. The analysis of social media activity includes, for example, an analysis of posted content and an analysis of followers, but is not limited to such examples. The dialogue unit reflects, for example, words and phrases frequently used by the designated person on social media in the dialogue content. The dialogue unit can also analyze the activity pattern of the designated person on social media and reflect it in the dialogue content. For example, the dialogue unit analyzes the posted content of the designated person and customizes the dialogue content based on the interests and concerns. Furthermore, the dialogue unit can optimize the dialogue content based on information shared by the designated person on social media. For example, the dialogue unit analyzes the reaction of the followers of the designated person and reflects it in the dialogue content. In this way, it is possible to provide a more realistic dialogue content by analyzing social media activity.

[0051] The dialogue unit provides a topic specific to the region in consideration of the geographical location information of the designated person. The dialogue unit provides a topic specific to the region in consideration of the geographical location information of the designated person. The collection of geographical location information includes, for example, collection of GPS data and a method of analyzing location information, but is not limited to such examples. The dialogue unit, for example, reflects topics of the region where the designated person lives in the dialogue content. In addition, the dialogue unit can also reflect cultures and customs specific to the region in the dialogue content based on the geographical location information of the designated person. For example, the dialogue unit incorporates information on places frequently visited by the designated person into the dialogue content. Furthermore, the dialogue unit can also reflect information on events and occasions specific to the region in the dialogue content. For example, the dialogue unit collects data on festivals and traditional events in the region and incorporates it into the dialogue content. In this way, a topic specific to the region can be provided by taking into consideration the geographical location information.

[0052] The stop unit performs the stop notification in consideration of the emotional state of the designated person. The stop unit performs the stop notification in consideration of the emotional state of the designated person. Consideration of the emotional state includes, for example, emotion recognition technology and emotion evaluation criteria, but is not limited to such examples. For example, when the designated person is relaxed, the stop unit performs the stop notification in a gentle tone. In addition, when the designated person is stressed, the stop unit can also perform the stop notification in an encouraging tone. For example, the stop unit dynamically adjusts the tone of the stop notification according to the emotional state of the designated person. Furthermore, when the designated person is excited, the stop unit can also perform the stop notification in a calming tone. For example, the stop unit monitors the emotional state of the designated person in real time and optimizes the tone of the stop notification. As a result, the stop notification can be performed in a more appropriate tone by considering the emotional state of the designated person.

[0053] The stopping unit adds a function of safely backing up data of the tablet terminal when the service is stopped. The stopping unit adds a function of safely backing up data of the tablet terminal when the service is stopped. Data backup includes, for example, the frequency of backup and selection of a backup destination, but is not limited to such examples. For example, the stopping unit backs up data of the tablet terminal to a cloud when the service is stopped. Furthermore, the stopping unit can also back up data of the tablet terminal to external storage when the service is stopped. For example, the stopping unit encrypts and backs up the data. Furthermore, the stopping unit can also automate the data backup procedure. For example, the stopping unit automatically executes a data backup when the service is stopped. This allows the data to be protected by safely backing up the data.

[0054] The stop unit automatically checks the contact information of the designated person and issues a stop notification. The stop unit automatically checks the contact information of the designated person and issues a stop notification. The confirmation of the contact information includes, for example, a telephone number and an email address, but is not limited to such examples. The stop unit automatically checks, for example, the contact information of the designated person and issues a stop notification. In addition, when the contact information of the designated person is changed, the stop unit can automatically update the contact information and issue a stop notification. For example, the stop unit detects a change in the contact information in real time and updates the stop notification. Furthermore, the stop unit can also select an optimal stop notification method based on the contact information. For example, the stop unit refers to the contact information of the designated person and selects an optimal notification method. In this way, an accurate stop notification can be issued by automatically checking the contact information of the designated person.

[0055] The stopping unit incorporates an additional function for enhancing the security function of the tablet terminal when stopped. The stopping unit incorporates an additional function for enhancing the security function of the tablet terminal when stopped. Enhancement of the security function includes, for example, encryption technology and authentication methods, but is not limited to such examples. The stopping unit enhances the security of the tablet terminal, for example, by incorporating a face recognition function using AI. In addition, the stopping unit can also incorporate a fingerprint authentication function using AI. For example, the stopping unit enhances access control of the tablet terminal using fingerprint authentication technology. Furthermore, the stopping unit can also incorporate a voice recognition function using AI. For example, the stopping unit enhances the security of the tablet terminal using voice recognition technology. In this way, the safety of the terminal can be improved by enhancing the security function.

[0056] The request unit makes a return request taking into consideration the time period during which the designated person can return the item. The request unit makes a return request taking into consideration the time period during which the designated person can return the item. Consideration of the time period during which the designated person can return the item includes, but is not limited to, the designated time period and the date on which the item can be returned. For example, the request unit requests the optimal return time taking into consideration the time period during which the designated person can return the item. In addition, the request unit can also make a return request during the time period during which the designated person can return the item by referring to the schedule of the designated person. For example, the request unit requests the optimal return time by referring to the past return history of the designated person. Furthermore, the request unit can optimize the return schedule based on the time period during which the item can be returned. For example, the request unit issues an instruction to the return company based on the time period during which the designated person can return the item. In this way, the designated person can be sure to return the item by taking into consideration the time period during which the item can be returned.

[0057] The request unit automatically checks the contact information of the designated person and makes a return request. The request unit automatically checks the contact information of the designated person and makes a return request. The contact information includes, for example, a telephone number and an email address, but is not limited to such examples. The request unit automatically checks, for example, the contact information of the designated person and makes a return request. In addition, if the contact information of the designated person is changed, the request unit can automatically update the contact information and make a return request. For example, the request unit detects changes in the contact information in real time and updates the return request. Furthermore, the request unit can select an optimal return request method based on the contact information. For example, the request unit refers to the contact information of the designated person and selects an optimal return request method. In this way, an accurate return request can be made by automatically checking the contact information of the designated person.

[0058] The deletion unit issues a deletion notification in consideration of the emotional state of the designated person. The deletion unit issues a deletion notification in consideration of the emotional state of the designated person. Consideration of the emotional state includes, for example, emotion recognition technology and emotion evaluation criteria, but is not limited to such examples. For example, when the designated person is relaxed, the deletion unit issues a deletion notification in a calm tone. In addition, when the designated person is stressed, the deletion unit can also issue a deletion notification in an encouraging tone. For example, the deletion unit dynamically adjusts the tone of the deletion notification according to the emotional state of the designated person. Furthermore, when the designated person is excited, the deletion unit can also issue a deletion notification in a calming tone. For example, the deletion unit monitors the emotional state of the designated person in real time and optimizes the tone of the deletion notification. As a result, the deletion notification can be issued in a more appropriate tone by considering the emotional state of the designated person.

[0059] The deletion unit automatically checks the contact information of the designated person and issues a deletion notification. The deletion unit automatically checks the contact information of the designated person and issues a deletion notification. The confirmation of the contact information includes, for example, a telephone number and an email address, but is not limited to such examples. The deletion unit automatically checks the contact information of the designated person and issues a deletion notification. In addition, when the contact information of the designated person is changed, the deletion unit can automatically update the contact information and issue a deletion notification. For example, the deletion unit detects a change in the contact information in real time and updates the deletion notification. Furthermore, the deletion unit can also select an optimal deletion notification method based on the contact information. For example, the deletion unit refers to the contact information of the designated person and selects an optimal notification method. In this way, an accurate deletion notification can be issued by automatically checking the contact information of the designated person.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The request unit can also provide options for the return method when requesting the return of the tablet terminal. For example, if it is difficult for the person designated by the user to return the tablet terminal by mail, the request unit can guide the person to a nearby return location. Also, a return label can be enclosed with the return request notice to facilitate easy return. Furthermore, a detailed explanation of the return procedure can be included in the return request notice to enable the designated person to smoothly return the tablet terminal. In this way, by providing options for the return method, the designated person can easily return the tablet terminal.

[0062] The deletion unit may also have a function of temporarily backing up data before deletion when deleting data. For example, the deletion unit temporarily backs up data in the tablet device to cloud storage before deleting the data. The deletion unit may also back up data to external storage before deleting the data. Furthermore, the deletion unit may check with the user about the backup before deleting the data, and execute the deletion only after obtaining consent. In this way, by performing a temporary backup when deleting data, data can be protected.

[0063] The learning unit can also analyze the user's past dialogue history and select an appropriate learning algorithm. For example, it analyzes words and phrases frequently used by the user and adjusts the learning algorithm based on the results. It can also analyze the user's dialogue patterns and select the optimal learning algorithm. Furthermore, it can customize the learning data based on the analysis results of the dialogue history. In this way, it is possible to select the optimal learning algorithm by analyzing the past dialogue history.

[0064] The learning unit can also customize learning data based on the user's lifestyle and hobbies. For example, conversation data that takes place during a specific time period can be learned based on the user's lifestyle. Also, conversation data related to the user's hobbies can be preferentially learned. Furthermore, related conversation data can be customized and learned based on the user's interests and concerns. This allows for the generation of more personalized AI by customizing learning data based on the user's lifestyle and hobbies.

[0065] The learning unit can also analyze the user's social media activities and reflect them in the learning data. For example, words and phrases frequently used by the user on social media can be reflected in the learning data. In addition, the learning unit can analyze the user's social media activity patterns and reflect them in the learning data. Furthermore, the learning data can be optimized based on information shared by the user on social media. In this way, by analyzing social media activities, more realistic learning data can be generated.

[0066] The learning unit can also incorporate region-specific language and culture into the learning data, taking into account the user's geographical location information. For example, the dialect and expressions of the region where the user lives can be incorporated into the learning data. In addition, region-specific culture and customs can be reflected in the learning data based on the user's geographical location information. Furthermore, information about region-specific events and occasions can be reflected in the learning data. In this way, by taking into account the geographical location information, it is possible to generate AI that reflects region-specific language and culture.

[0067] The process flow of the first embodiment will be briefly described below.

[0068] Step 1: The learning unit generates an AI that has learned the user's voice, speaking style, and personality. Specifically, the user's voice is recorded and voice features are extracted using voice recognition technology. The user's speaking style is also recorded and the characteristics of the speaking style are analyzed using natural language processing technology. Furthermore, the user is given a personality diagnostic test and their personality is evaluated based on the results. Step 2: The embedding unit embeds the AI ​​generated by the learning unit into the tablet device. Specifically, it installs the AI ​​software on the tablet device and performs the necessary settings. Step 3: The instruction unit instructs the delivery of the tablet device to a person designated by the user after the user's death. Specifically, the instruction unit checks the address of the person designated by the user, selects an appropriate delivery company, and instructs the delivery. Step 4: When the tablet device is delivered to the designated person, the dialogue unit uses AI to carry out a dialogue. Specifically, the AI ​​reproduces the voice and manner of speaking of the deceased person and engages in a dialogue with the designated person. Step 5: The suspension unit will suspend the AI-based service a predetermined period of time after the user's death. Specifically, the service will be suspended 49 days after the user's death, and the tablet device will be requested to be returned.

[0069] (Example 2) The system according to the embodiment of the present invention is a system that aims to ease the sense of loss and restore mental health by creating one's own AI on a dedicated tablet while alive, and delivering the tablet to a designated person after death, thereby interacting with the deceased's AI. This system generates an AI that learns the user's voice, speech style, and personality, incorporates the generated AI into a tablet terminal, and delivers it to the designated person after the user's death. When the tablet terminal arrives at the designated person's place, a dialogue is conducted using the AI, and the user can feel as if they are interacting with the deceased. This is expected to ease the sense of loss and restore mental health. Furthermore, when a predetermined period (for example, 49 days) has passed since the death of the user, the service by the AI ​​is stopped. When this period has passed, the tablet terminal stops the service and it becomes impossible to interact with the AI. When the service is stopped, the system also has a function to request the return of the tablet terminal. For example, by returning the tablet terminal, reuse or appropriate processing is performed. In addition, the system also has a function to delete data when the service is stopped, thereby protecting privacy. In this way, the present invention provides a system that aims to ease the sense of loss and restore mental health by interacting with the deceased's AI. By generating an AI that learns the user's voice, speaking style, and personality and incorporating it into a tablet device, the user can feel as if they are talking to the deceased. The system also has functions such as suspending the service, returning the tablet device, and deleting data, ensuring privacy protection and appropriate processing. This allows the system to ease the sense of loss and restore mental health by talking to the AI ​​of the deceased.

[0070] The system according to the embodiment includes a learning unit, an embedding unit, an instruction unit, a dialogue unit, and a stopping unit. The learning unit generates an AI that has learned the user's voice, speech style, and personality. The user's voice includes, for example, a method for collecting voice samples and voice features, but is not limited to such examples. The learning unit, for example, records the user's voice and extracts voice features using voice recognition technology. The learning unit also analyzes the user's speech style. The speech style includes, for example, speech speed, intonation, and accent, but is not limited to such examples. The learning unit, for example, records the user's speech style and analyzes the speech style features using natural language processing technology. Furthermore, the learning unit evaluates the user's personality. The personality includes, for example, a personality diagnostic test and an analysis of behavioral patterns, but is not limited to such examples. The learning unit, for example, administers a personality diagnostic test to the user and evaluates the personality based on the results. The embedding unit embeds the AI ​​generated by the learning unit into a tablet terminal. The embedding includes, for example, a software installation procedure and a hardware setting method, but is not limited to such examples. The embedding unit, for example, installs AI software in the tablet terminal and performs necessary settings. The instruction unit instructs the delivery of the tablet terminal to a person designated by the user after the death of the user. The delivery includes, for example, but is not limited to, the selection of a delivery company and a delivery schedule. The instruction unit, for example, checks the address of the person designated by the user, selects an appropriate delivery company, and instructs the delivery. The dialogue unit uses AI to have a dialogue when the tablet terminal arrives at the designated person. The dialogue includes, for example, but is not limited to, a dialogue scenario and a natural language processing technology to be used. For example, the dialogue unit reproduces the voice and speaking style of the deceased person and has a dialogue with the designated person. The stopping unit stops the service by AI when a predetermined period has passed after the death of the user. The service stopping includes, for example, but is not limited to, a trigger condition for stopping and a process after stopping. For example, the stopping unit stops the service 49 days after the death of the user and requests the return of the tablet terminal. As a result, the system of the embodiment uses AI that has learned the user's voice, speaking style, and personality to provide the feeling of interacting with the deceased, thereby easing the sense of loss.

[0071] The learning unit generates an AI that has learned the user's voice, speaking style, and personality. The user's voice includes, for example, a method for collecting voice samples and voice features, but is not limited to such examples. The learning unit, for example, records the user's voice and extracts voice features using voice recognition technology. Specifically, the user's voice is recorded with a high-quality microphone, and the voice data is analyzed using digital signal processing technology. Voice features include fundamental frequency, formant, and spectral energy distribution. These features are important for expressing the individuality of the user's voice. In addition, the learning unit analyzes the user's speaking style. The speaking style includes, for example, speech speed, intonation, and accent, but is not limited to such examples. For example, the learning unit records the user's speaking style and analyzes the characteristics of the speaking style using natural language processing technology. Specifically, the recording data of the user's speaking style is converted into text, and the text data is analyzed to extract patterns of speaking speed and intonation. Furthermore, the learning unit evaluates the user's personality. The personality includes, for example, a personality diagnostic test and an analysis of behavioral patterns, but is not limited to such examples. The learning unit, for example, administers a personality diagnostic test to the user and evaluates the personality based on the results. Specifically, the results of the personality diagnostic test are quantified and the numerical data is input into the AI ​​model to evaluate the personality. This allows the learning unit to comprehensively learn the user's voice, speaking style, and personality, and generate an AI that resembles the user.

[0072] The embedding unit embeds the AI ​​generated by the learning unit into the tablet terminal. Embedding includes, for example, software installation procedures and hardware configuration methods, but is not limited to such examples. Specifically, first, the AI ​​software is downloaded to the tablet terminal and installed. After installation, necessary settings are made so that the AI ​​software operates normally. For example, compatibility between the operating system of the tablet terminal and the AI ​​software is confirmed, and necessary drivers and libraries are installed. In addition, settings are made for the audio output device and microphone so that the AI ​​software reproduces the user's voice and speaking style. Furthermore, network settings are made for the tablet terminal so that the AI ​​software can communicate with the cloud server. This allows the AI ​​software to access a database on the cloud and obtain necessary data. The embedding unit reliably executes these procedures and verifies that the AI ​​software operates normally on the tablet terminal. This allows the embedding unit to embed the AI ​​generated by the learning unit into the tablet terminal and provide it to the user.

[0073] The instruction unit instructs the delivery of the tablet terminal to a person designated by the user after the death of the user. The delivery includes, for example, the selection of a delivery company and a delivery schedule, but is not limited to such examples. Specifically, first, the address of the person designated by the user is confirmed, and a company capable of delivery to the address is selected. The selected company is given detailed instructions on how to handle the tablet terminal and the delivery schedule. For example, appropriate packaging materials are used to prevent the tablet terminal from being damaged, and impact during delivery is minimized. In addition, regarding the delivery schedule, the date and time at which the designated person can certainly receive the tablet terminal are adjusted, and instructions are given to the delivery company. Furthermore, the instruction unit tracks the delivery status in real time, and notifies the designated person of the delivery status. As a result, the designated person can understand the expected date and time of arrival of the tablet terminal and can prepare for reception. The instruction unit reliably executes these procedures, and ensures that the tablet terminal is delivered to the designated person after the death of the user. As a result, the instruction unit respects the user's intention and can smoothly deliver the tablet terminal.

[0074] When the tablet terminal arrives at the designated person's place, the dialogue unit uses AI to engage in dialogue. The dialogue includes, for example, a dialogue scenario and a natural language processing technology to be used, but is not limited to such examples. Specifically, the AI ​​reproduces the voice and manner of speech of the deceased and engages in dialogue with the designated person. The dialogue scenario is created based on the contents set by the user before their death. For example, it includes a message that the deceased wanted to convey before their death and dialogue content according to a specific situation. The AI ​​uses natural language processing technology to engage in natural dialogue based on these scenarios. Specifically, the AI ​​converts the utterance of the designated person into text using voice recognition technology, analyzes the text, and generates an appropriate response. The generated response is played back in the voice of the deceased using voice synthesis technology. This allows the designated person to feel as if they are having a dialogue with the deceased. Furthermore, the dialogue unit can flexibly change the AI's response depending on the content and situation of the dialogue. For example, if the designated person shows an emotional reaction, the AI ​​generates a response according to that emotion and continues the dialogue. This allows the dialogue unit to provide the designated person with support to ease their sense of loss through dialogue with the deceased.

[0075] The stop unit stops the service by the AI ​​when a predetermined period of time has passed after the death of the user. The service stop includes, for example, trigger conditions for the stop and processing after the stop, but is not limited to such examples. Specifically, the service is stopped 49 days after the death of the user, and the tablet terminal is requested to be returned. The trigger conditions for the stop include the submission of a death certificate of the user and a notification from a designated person. When these conditions are met, the stop unit starts a procedure to stop the service by the AI. Specifically, first, the AI ​​software on the tablet terminal is stopped and the provision of the service is terminated. Next, the designated person is requested to return the tablet terminal and is notified of the details of the return procedure. The return procedure includes, for example, the provision of packaging materials for return and notification of the return address. Furthermore, the stop unit erases the data of the returned tablet terminal and takes measures to protect privacy. As a result, the stop unit can stop the service by the AI ​​at an appropriate time after the death of the user and ensure the return of the tablet terminal. As a result, the stop unit can respect the user's wishes and appropriately terminate the provision of the service.

[0076] The tablet terminal includes a request unit that requests the return of the tablet terminal when the AI-based service is stopped by the stop unit. The request unit requests the return of the tablet terminal when the service is stopped. The return request includes, for example, a method of notifying the return request and a return deadline, but is not limited to such examples. For example, the request unit displays a message of the return request on the screen of the tablet terminal and notifies the return deadline. The request unit can also send the return request notification by email or SMS. For example, the request unit sends the return request notification based on contact information of a person designated by the user. Furthermore, the request unit can send the return request notification by mail. For example, the request unit sends the return request notification by mail and clearly indicates the return deadline. In this way, the tablet terminal can be reused or appropriately processed by requesting the return of the tablet terminal after the service is stopped.

[0077] The tablet terminal includes a deletion unit that deletes data when the AI-based service is stopped by the stopping unit. The deletion unit deletes data when the service is stopped. Data deletion includes, for example, the type of data to be deleted and a deletion procedure, but is not limited to such examples. The deletion unit deletes, for example, user data and dialogue history in the tablet terminal. The deletion unit can also automate the data deletion procedure. For example, the deletion unit automatically executes data deletion when the service is stopped. Furthermore, the deletion unit can also confirm data deletion. For example, the deletion unit displays a confirmation message before deleting data, and executes deletion after obtaining the user's consent. In this way, privacy is protected by deleting data after the service is stopped.

[0078] The learning unit estimates the user's emotion and selects learning data based on the estimated user's emotion. The learning unit estimates the user's emotion and selects learning data based on the estimated emotion. Emotion estimation includes, for example, emotion recognition technology and the type of data used, but is not limited to such examples. For example, the learning unit captures the user's facial expression with a camera and estimates the emotion using an emotion recognition algorithm. The learning unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the learning unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the learning unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the learning unit calculates an emotion score based on the fluctuation of the heart rate. As a result, a more appropriate AI can be generated by selecting learning data based on the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] The learning unit analyzes the user's past dialogue history and selects an appropriate learning algorithm. The learning unit analyzes the user's past dialogue history and selects an optimal learning algorithm. The dialogue history analysis includes, for example, natural language processing technology and analysis standards, but is not limited to such examples. The learning unit analyzes, for example, words and phrases frequently used by the user and adjusts the learning algorithm based on the results. The learning unit can also analyze the user's dialogue pattern and select an optimal learning algorithm. For example, the learning unit selects a learning algorithm corresponding to a specific emotional state from the user's dialogue history. Furthermore, the learning unit can customize the learning data based on the analysis result of the dialogue history. For example, the learning unit optimizes the learning data based on information obtained from the user's dialogue history. In this way, the optimal learning algorithm can be selected by analyzing the past dialogue history.

[0080] The learning unit customizes the learning data based on the user's lifestyle and hobbies. The learning unit customizes the learning data based on the user's lifestyle and hobbies. Lifestyle includes, for example, daily behavior patterns and health data, but is not limited to such examples. The learning unit learns, for example, conversation data conducted during a specific time period based on the user's lifestyle. The learning unit can also preferentially learn conversation data related to the user's hobbies. For example, the learning unit collects conversation data related to the user's hobby activities and reflects the collected data in the learning data. Furthermore, the learning unit can customize and learn related conversation data based on the user's interests and concerns. For example, the learning unit collects data related to topics that interest the user and incorporates it into the learning data. This allows the learning data to be customized based on the user's lifestyle and hobbies, thereby generating a more personalized AI.

[0081] The learning unit estimates the user's emotion and adjusts the frequency of learning based on the estimated user's emotion. The learning unit estimates the user's emotion and adjusts the frequency of learning based on the estimated emotion. The adjustment of the learning frequency includes, for example, the learning interval and the update frequency of the learning data, but is not limited to such an example. For example, when the user is relaxed, the learning unit increases the frequency of learning to collect data. In addition, when the user is stressed, the learning unit can also reduce the frequency of learning to reduce the burden. For example, the learning unit dynamically adjusts the frequency of learning according to the emotional state of the user. Furthermore, when the user is excited, the learning unit can adjust the frequency of learning to collect appropriate data. For example, the learning unit monitors the emotional state of the user in real time and optimizes the frequency of learning. As a result, by adjusting the frequency of learning based on the user's emotion, it is possible to collect appropriate data while reducing the burden on the user. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example.

[0082] The learning unit analyzes the user's social media activity and reflects it in the learning data. The learning unit analyzes the user's social media activity and reflects it in the learning data. The analysis of social media activity includes, for example, an analysis of the posting content and an analysis of the followers, but is not limited to such examples. The learning unit, for example, reflects the words and phrases that the user frequently uses on social media in the learning data. The learning unit can also analyze the user's activity pattern on social media and reflect it in the learning data. For example, the learning unit analyzes the posting content of the user and customizes the learning data based on the user's interests and concerns. Furthermore, the learning unit can optimize the learning data based on the information the user shares on social media. For example, the learning unit analyzes the reaction of the user's followers and reflects it in the learning data. In this way, by analyzing social media activity, it is possible to generate learning data that is more in line with reality.

[0083] The learning unit incorporates a language and culture specific to a region into the learning data based on the geographical location information of the user. The learning unit incorporates a language and culture specific to a region into the learning data, taking into account the geographical location information of the user. Collection of geographical location information includes, for example, collection of GPS data and a method of analyzing location information, but is not limited to such examples. The learning unit incorporates, for example, dialects and expressions of the region where the user lives into the learning data. In addition, the learning unit can also reflect a culture and customs specific to a region into the learning data based on the geographical location information of the user. For example, the learning unit incorporates information on places frequently visited by the user into the learning data. Furthermore, the learning unit can also reflect information on events and occasions specific to a region into the learning data. For example, the learning unit collects data on festivals and traditional events in the region and incorporates it into the learning data. In this way, an AI that reflects a language and culture specific to a region can be generated by taking into account the geographical location information.

[0084] The embedding unit estimates the user's emotion and adjusts the timing of embedding based on the estimated user's emotion. The embedding unit estimates the user's emotion and adjusts the timing of embedding based on the estimated emotion. The adjustment of the timing of embedding includes, for example, the user's schedule and the operating status of the system, but is not limited to such examples. For example, the embedding unit advances the timing of embedding when the user is relaxed. Also, the embedding unit can delay the timing of embedding when the user is stressed. For example, the embedding unit dynamically adjusts the timing of embedding according to the emotional state of the user. Furthermore, when the user is excited, the embedding unit can adjust the timing of embedding to embed the AI ​​at an appropriate timing. For example, the embedding unit monitors the emotional state of the user in real time and optimizes the timing of embedding. As a result, the timing of embedding can be adjusted based on the user's emotion, so that the AI ​​can be embedded at a more appropriate timing. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] The embedded unit generates an AI optimized for the hardware characteristics of the tablet terminal. The embedded unit generates an AI optimized for the hardware characteristics of the tablet terminal. Optimization of the hardware characteristics includes, for example, CPU performance and memory capacity, but is not limited to such examples. The embedded unit generates an AI optimized for, for example, the processor performance of the tablet terminal. The embedded unit can also generate an AI optimized for the memory capacity of the tablet terminal. For example, the embedded unit optimizes the memory usage of the tablet terminal to achieve efficient operation. Furthermore, the embedded unit can also generate an AI optimized in consideration of the battery life of the tablet terminal. For example, the embedded unit sets the operation of the AI ​​to a low power consumption mode to reduce battery consumption. As a result, the performance of the terminal can be maximized by generating an AI optimized for the hardware characteristics of the tablet terminal.

[0086] The embedded unit customizes the operation mode of the AI ​​according to the user's usage environment. The embedded unit customizes the operation mode of the AI ​​according to the user's usage environment. Customization of the usage environment includes, for example, indoor and outdoor environments and applications used, but is not limited to such examples. For example, when the user uses it outdoors, the embedded unit provides an operation mode that reduces battery consumption. In addition, when the user uses it indoors, the embedded unit can also provide a high-performance operation mode. For example, the embedded unit automatically adjusts the operation mode of the AI ​​according to the user's usage environment. Furthermore, when the user uses it while moving, the embedded unit can also adjust the operation mode in real time. For example, the embedded unit selects the optimal operation mode based on the user's location information. As a result, by customizing the operation mode according to the usage environment, it is possible to provide optimal operation according to the user's needs.

[0087] The embedding unit estimates the user's emotion and determines the embedding priority based on the estimated user's emotion. The embedding unit estimates the user's emotion and determines the embedding priority based on the estimated emotion. The embedding priority includes, for example, the importance and urgency of the task, but is not limited to such an example. For example, the embedding unit increases the embedding priority when the user is relaxed. Also, the embedding unit can lower the embedding priority when the user is stressed. For example, the embedding unit dynamically adjusts the embedding priority according to the user's emotional state. Furthermore, when the user is excited, the embedding unit can adjust the embedding priority to embed the AI ​​at an appropriate time. For example, the embedding unit monitors the user's emotional state in real time and optimizes the embedding priority. As a result, by determining the embedding priority based on the user's emotion, more important embedding can be prioritized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example.

[0088] The embedded unit adjusts the operation of the AI ​​in consideration of the battery life of the tablet terminal. The embedded unit adjusts the operation of the AI ​​in consideration of the battery life of the tablet terminal. Optimization of battery life includes, for example, battery consumption patterns and power saving modes, but is not limited to such examples. For example, the embedded unit sets the operation of the AI ​​to a low power consumption mode in order to reduce battery consumption. The embedded unit can also optimize the operation of the AI ​​in order to extend the battery life. For example, the embedded unit automatically adjusts the operation mode of the AI ​​according to the remaining battery power. Furthermore, the embedded unit can analyze the battery consumption pattern and select the optimal operation mode. For example, the embedded unit optimizes the operation of the AI ​​based on the battery consumption pattern. As a result, the use time of the terminal can be extended by optimizing the operation of the AI ​​in consideration of the battery life.

[0089] The embedded unit incorporates an additional function for enhancing the security function of the tablet terminal. The embedded unit incorporates an additional function for enhancing the security function of the tablet terminal. Enhancement of the security function includes, for example, encryption technology and authentication methods, but is not limited to such examples. For example, the embedded unit enhances the security of the tablet terminal by incorporating a face recognition function using AI. The embedded unit can also incorporate a fingerprint authentication function using AI. For example, the embedded unit enhances access control of the tablet terminal using fingerprint authentication technology. Furthermore, the embedded unit can also incorporate a voice recognition function using AI. For example, the embedded unit enhances the security of the tablet terminal using voice recognition technology. This enhances the security function, thereby improving the safety of the terminal.

[0090] The instruction unit estimates the user's emotion and adjusts the timing of the delivery instruction based on the estimated user's emotion. The instruction unit estimates the user's emotion and adjusts the timing of the delivery instruction based on the estimated emotion. The adjustment of the delivery instruction timing includes, for example, a delivery schedule and a receiving time, but is not limited to such examples. For example, the instruction unit advances the timing of the delivery instruction when the user is relaxed. In addition, the instruction unit can also delay the timing of the delivery instruction when the user is stressed. For example, the instruction unit dynamically adjusts the timing of the delivery instruction according to the emotional state of the user. Furthermore, the instruction unit can adjust the timing of the delivery instruction to perform delivery at an appropriate timing when the user is excited. For example, the instruction unit monitors the user's emotional state in real time and optimizes the timing of the delivery instruction. As a result, by adjusting the timing of the delivery instruction based on the user's emotion, delivery can be performed at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The instruction unit issues a delivery instruction taking into consideration the time period when the designated person can receive the parcel. The instruction unit issues a delivery instruction taking into consideration the time period when the designated person can receive the parcel. Consideration of the time period when the designated person can receive the parcel includes, for example, a designated time period and a date when the parcel can be received, but is not limited to such examples. The instruction unit issues an optimal delivery time taking into consideration the time period when the designated person can receive the parcel. The instruction unit can also issue a delivery instruction during the time period when the designated person can receive the parcel by referring to the schedule of the designated person. For example, the instruction unit issues an optimal delivery time by referring to the past reception history of the designated person. Furthermore, the instruction unit can also optimize the delivery schedule based on the time period when the parcel can be received. For example, the instruction unit issues an instruction to a delivery company based on the time period when the designated person can receive the parcel. In this way, the time period when the parcel can be received is taken into consideration, thereby ensuring that the designated person can receive the parcel.

[0092] The instruction unit selects the optimal delivery method in consideration of the geographical conditions of the delivery destination. The instruction unit selects the optimal delivery method in consideration of the geographical conditions of the delivery destination. Consideration of geographical conditions includes, for example, the topography and traffic conditions of the delivery destination, but is not limited to such examples. For example, when the delivery destination is an urban area, the instruction unit selects a quick delivery method. Furthermore, when the delivery destination is a suburban area, the instruction unit can also select an efficient delivery method. For example, when the delivery destination is a remote area, the instruction unit selects the optimal delivery method. Furthermore, the instruction unit can optimize the delivery schedule based on the geographical conditions. For example, the instruction unit issues instructions to a delivery company based on the geographical conditions of the delivery destination. In this way, efficient and quick delivery is realized by considering the geographical conditions.

[0093] The instruction unit estimates the user's emotion and determines the priority of the delivery instruction based on the estimated user's emotion. The instruction unit estimates the user's emotion and determines the priority of the delivery instruction based on the estimated emotion. The determination of the delivery instruction priority includes, for example, the urgency and importance of the delivery, but is not limited to such an example. For example, the instruction unit increases the priority of the delivery instruction when the user is relaxed. In addition, the instruction unit can also lower the priority of the delivery instruction when the user is stressed. For example, the instruction unit dynamically adjusts the priority of the delivery instruction according to the emotional state of the user. Furthermore, when the user is excited, the instruction unit can adjust the priority of the delivery instruction to perform delivery at an appropriate time. For example, the instruction unit monitors the emotional state of the user in real time and optimizes the priority of the delivery instruction. As a result, important deliveries can be prioritized by determining the priority of the delivery instruction based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example.

[0094] The instruction unit automatically checks the contact information of the designated person and issues a delivery instruction. The instruction unit automatically checks the contact information of the designated person and issues a delivery instruction. The contact information includes, for example, a telephone number and an email address, but is not limited to such examples. The instruction unit automatically checks the contact information of the designated person and issues a delivery instruction. In addition, when the contact information of the designated person is changed, the instruction unit can automatically update the contact information and issue a delivery instruction. For example, the instruction unit detects a change in the contact information in real time and updates the delivery instruction. Furthermore, the instruction unit can select an optimal delivery method based on the contact information. For example, the instruction unit refers to the contact information of the designated person and selects an optimal delivery company. In this way, accurate delivery instructions can be issued by automatically checking the contact information of the designated person.

[0095] The instruction unit adds a function of tracking the delivery status in real time. The instruction unit adds a function of tracking the delivery status in real time. Delivery status tracking includes, for example, GPS tracking and a delivery status update method, but is not limited to such examples. For example, the instruction unit tracks the delivery status in real time and notifies a designated person. The instruction unit can also track the delivery status in real time and notify a user. For example, the instruction unit monitors the progress of the delivery status in real time and notifies a user. Furthermore, the instruction unit can select an optimal delivery method based on the delivery status. For example, the instruction unit tracks the delivery status in real time and selects an optimal delivery company. This allows the progress of delivery to be confirmed by tracking the delivery status in real time.

[0096] The dialogue unit estimates the user's emotion and adjusts the content of the dialogue based on the estimated user's emotion. The dialogue unit estimates the user's emotion and adjusts the content of the dialogue based on the estimated emotion. The adjustment of the dialogue content includes, for example, the topic of the dialogue and the tone of the dialogue, but is not limited to such examples. For example, when the user is relaxed, the dialogue unit provides a dialogue with relaxing content. In addition, when the user is stressed, the dialogue unit can also provide a dialogue with content that reduces stress. For example, the dialogue unit dynamically adjusts the content of the dialogue according to the emotional state of the user. Furthermore, when the user is excited, the dialogue unit can also provide a dialogue with content that reduces excitement. For example, the dialogue unit monitors the user's emotional state in real time and optimizes the content of the dialogue. As a result, a more appropriate dialogue can be provided by adjusting the content of the dialogue based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The dialogue unit customizes the tone of the dialogue according to the current situation of the designated person. The dialogue unit customizes the tone of the dialogue according to the current situation of the designated person. Customization of the dialogue tone includes, for example, the atmosphere and the use of words in the dialogue, but is not limited to such examples. For example, if the designated person is relaxed, the dialogue unit conducts the dialogue in a calm tone. In addition, if the designated person is stressed, the dialogue unit can also conduct the dialogue in an encouraging tone. For example, the dialogue unit dynamically adjusts the tone of the dialogue according to the emotional state of the designated person. Furthermore, if the designated person is excited, the dialogue unit can also conduct the dialogue in a calming tone. For example, the dialogue unit monitors the emotional state of the designated person in real time and optimizes the tone of the dialogue. This allows a more appropriate dialogue to be provided by customizing the tone of the dialogue according to the situation of the designated person.

[0098] The dialogue unit estimates the user's emotion and determines the priority of the dialogue based on the estimated emotion of the user. The dialogue unit estimates the user's emotion and determines the priority of the dialogue based on the estimated emotion. The determination of the dialogue priority includes, for example, the importance and urgency of the dialogue, but is not limited to such an example. For example, when the user is relaxed, the dialogue unit increases the priority of the dialogue. In addition, when the user is stressed, the dialogue unit can also lower the priority of the dialogue. For example, the dialogue unit dynamically adjusts the priority of the dialogue according to the emotional state of the user. Furthermore, when the user is excited, the dialogue unit can adjust the priority of the dialogue to conduct the dialogue at an appropriate timing. For example, the dialogue unit monitors the emotional state of the user in real time and optimizes the priority of the dialogue. As a result, important dialogues can be conducted preferentially by determining the priority of the dialogue based on the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example.

[0099] The dialogue unit analyzes the social media activity of the designated person and reflects it in the dialogue content. The dialogue unit analyzes the social media activity of the designated person and reflects it in the dialogue content. The analysis of social media activity includes, for example, an analysis of posted content and an analysis of followers, but is not limited to such examples. The dialogue unit reflects, for example, words and phrases frequently used by the designated person on social media in the dialogue content. The dialogue unit can also analyze the activity pattern of the designated person on social media and reflect it in the dialogue content. For example, the dialogue unit analyzes the posted content of the designated person and customizes the dialogue content based on the interests and concerns. Furthermore, the dialogue unit can optimize the dialogue content based on information shared by the designated person on social media. For example, the dialogue unit analyzes the reaction of the followers of the designated person and reflects it in the dialogue content. In this way, it is possible to provide a more realistic dialogue content by analyzing social media activity.

[0100] The dialogue unit provides a topic specific to the region in consideration of the geographical location information of the designated person. The dialogue unit provides a topic specific to the region in consideration of the geographical location information of the designated person. The collection of geographical location information includes, for example, collection of GPS data and a method of analyzing location information, but is not limited to such examples. The dialogue unit, for example, reflects topics of the region where the designated person lives in the dialogue content. In addition, the dialogue unit can also reflect cultures and customs specific to the region in the dialogue content based on the geographical location information of the designated person. For example, the dialogue unit incorporates information on places frequently visited by the designated person into the dialogue content. Furthermore, the dialogue unit can also reflect information on events and occasions specific to the region in the dialogue content. For example, the dialogue unit collects data on festivals and traditional events in the region and incorporates it into the dialogue content. In this way, a topic specific to the region can be provided by taking into consideration the geographical location information.

[0101] The stop unit estimates the user's emotion and adjusts the timing of the service stop based on the estimated user's emotion. The stop unit estimates the user's emotion and adjusts the timing of the service stop based on the estimated emotion. The adjustment of the service stop timing includes, for example, a trigger condition for the stop and a process after the stop, but is not limited to such examples. For example, the stop unit advances the timing of the service stop when the user is relaxed. In addition, the stop unit can also delay the timing of the service stop when the user is stressed. For example, the stop unit dynamically adjusts the timing of the service stop according to the emotional state of the user. Furthermore, the stop unit can adjust the timing of the service stop to stop the service at an appropriate timing when the user is excited. For example, the stop unit monitors the emotional state of the user in real time and optimizes the timing of the service stop. As a result, the service can be stopped at a more appropriate timing by adjusting the timing of the service stop based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0102] The stop unit performs the stop notification in consideration of the emotional state of the designated person. The stop unit performs the stop notification in consideration of the emotional state of the designated person. Consideration of the emotional state includes, for example, emotion recognition technology and emotion evaluation criteria, but is not limited to such examples. For example, when the designated person is relaxed, the stop unit performs the stop notification in a gentle tone. In addition, when the designated person is stressed, the stop unit can also perform the stop notification in an encouraging tone. For example, the stop unit dynamically adjusts the tone of the stop notification according to the emotional state of the designated person. Furthermore, when the designated person is excited, the stop unit can also perform the stop notification in a calming tone. For example, the stop unit monitors the emotional state of the designated person in real time and optimizes the tone of the stop notification. As a result, the stop notification can be performed in a more appropriate tone by considering the emotional state of the designated person.

[0103] The stopping unit adds a function of safely backing up data of the tablet terminal when the service is stopped. The stopping unit adds a function of safely backing up data of the tablet terminal when the service is stopped. Data backup includes, for example, the frequency of backup and selection of a backup destination, but is not limited to such examples. For example, the stopping unit backs up data of the tablet terminal to a cloud when the service is stopped. Furthermore, the stopping unit can also back up data of the tablet terminal to external storage when the service is stopped. For example, the stopping unit encrypts and backs up the data. Furthermore, the stopping unit can also automate the data backup procedure. For example, the stopping unit automatically executes a data backup when the service is stopped. This allows the data to be protected by safely backing up the data.

[0104] The stopping unit estimates the user's emotion and determines the priority of the service stop based on the estimated emotion of the user. The stopping unit estimates the user's emotion and determines the priority of the service stop based on the estimated emotion. The determination of the service stop priority includes, for example, the urgency and importance of the stop, but is not limited to such an example. For example, when the user is relaxed, the stopping unit increases the priority of the service stop. In addition, when the user is stressed, the stopping unit can also lower the priority of the service stop. For example, the stopping unit dynamically adjusts the priority of the service stop according to the emotional state of the user. Furthermore, when the user is excited, the stopping unit can adjust the priority of the service stop to stop the service at an appropriate timing. For example, the stopping unit monitors the emotional state of the user in real time and optimizes the priority of the service stop. As a result, important service stop can be prioritized by determining the priority of the service stop based on the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example.

[0105] The stop unit automatically checks the contact information of the designated person and issues a stop notification. The stop unit automatically checks the contact information of the designated person and issues a stop notification. The confirmation of the contact information includes, for example, a telephone number and an email address, but is not limited to such examples. The stop unit automatically checks, for example, the contact information of the designated person and issues a stop notification. In addition, when the contact information of the designated person is changed, the stop unit can automatically update the contact information and issue a stop notification. For example, the stop unit detects a change in the contact information in real time and updates the stop notification. Furthermore, the stop unit can also select an optimal stop notification method based on the contact information. For example, the stop unit refers to the contact information of the designated person and selects an optimal notification method. In this way, an accurate stop notification can be issued by automatically checking the contact information of the designated person.

[0106] The stopping unit incorporates an additional function for enhancing the security function of the tablet terminal when stopped. The stopping unit incorporates an additional function for enhancing the security function of the tablet terminal when stopped. Enhancement of the security function includes, for example, encryption technology and authentication methods, but is not limited to such examples. The stopping unit enhances the security of the tablet terminal, for example, by incorporating a face recognition function using AI. In addition, the stopping unit can also incorporate a fingerprint authentication function using AI. For example, the stopping unit enhances access control of the tablet terminal using fingerprint authentication technology. Furthermore, the stopping unit can also incorporate a voice recognition function using AI. For example, the stopping unit enhances the security of the tablet terminal using voice recognition technology. In this way, the safety of the terminal can be improved by enhancing the security function.

[0107] The request unit estimates the user's emotion and adjusts the timing of the return request based on the estimated emotion of the user. The request unit estimates the user's emotion and adjusts the timing of the return request based on the estimated emotion. The adjustment of the timing of the return request includes, for example, a method of notifying the return request and a return deadline, but is not limited to such examples. For example, the request unit advances the timing of the return request when the user is relaxed. In addition, the request unit can also delay the timing of the return request when the user is stressed. For example, the request unit dynamically adjusts the timing of the return request according to the emotional state of the user. Furthermore, the request unit can adjust the timing of the return request to make the return request at an appropriate timing when the user is excited. For example, the request unit monitors the emotional state of the user in real time and optimizes the timing of the return request. As a result, the return request can be made at a more appropriate timing by adjusting the timing of the return request based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0108] The request unit makes a return request taking into consideration the time period during which the designated person can return the item. The request unit makes a return request taking into consideration the time period during which the designated person can return the item. Consideration of the time period during which the designated person can return the item includes, but is not limited to, the designated time period and the date on which the item can be returned. For example, the request unit requests the optimal return time taking into consideration the time period during which the designated person can return the item. In addition, the request unit can also make a return request during the time period during which the designated person can return the item by referring to the schedule of the designated person. For example, the request unit requests the optimal return time by referring to the past return history of the designated person. Furthermore, the request unit can optimize the return schedule based on the time period during which the item can be returned. For example, the request unit issues an instruction to the return company based on the time period during which the designated person can return the item. In this way, the designated person can be sure to return the item by taking into consideration the time period during which the item can be returned.

[0109] The request unit estimates the user's emotion and determines the priority of the return request based on the estimated emotion of the user. The request unit estimates the user's emotion and determines the priority of the return request based on the estimated emotion. The determination of the priority of the return request includes, for example, the urgency and importance of the return, but is not limited to such examples. For example, the request unit increases the priority of the return request when the user is relaxed. In addition, the request unit can also lower the priority of the return request when the user is stressed. For example, the request unit dynamically adjusts the priority of the return request according to the emotional state of the user. Furthermore, the request unit can adjust the priority of the return request to make a return request at an appropriate time when the user is excited. For example, the request unit monitors the emotional state of the user in real time and optimizes the priority of the return request. As a result, important return requests can be prioritized by determining the priority of the return request based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0110] The request unit automatically checks the contact information of the designated person and makes a return request. The request unit automatically checks the contact information of the designated person and makes a return request. The contact information includes, for example, a telephone number and an email address, but is not limited to such examples. The request unit automatically checks, for example, the contact information of the designated person and makes a return request. In addition, if the contact information of the designated person is changed, the request unit can automatically update the contact information and make a return request. For example, the request unit detects changes in the contact information in real time and updates the return request. Furthermore, the request unit can select an optimal return request method based on the contact information. For example, the request unit refers to the contact information of the designated person and selects an optimal return request method. In this way, an accurate return request can be made by automatically checking the contact information of the designated person.

[0111] The deletion unit estimates the user's emotion and adjusts the timing of data deletion based on the estimated user's emotion. The deletion unit estimates the user's emotion and adjusts the timing of data deletion based on the estimated emotion. The adjustment of the data deletion timing includes, for example, a trigger condition for deletion and a process after deletion, but is not limited to such examples. For example, when the user is relaxed, the deletion unit advances the timing of data deletion. In addition, when the user is stressed, the deletion unit can also delay the timing of data deletion. For example, the deletion unit dynamically adjusts the timing of data deletion according to the emotional state of the user. Furthermore, when the user is excited, the deletion unit can adjust the timing of data deletion to delete data at an appropriate timing. For example, the deletion unit monitors the user's emotional state in real time and optimizes the timing of data deletion. As a result, by adjusting the timing of data deletion based on the user's emotion, data deletion can be performed at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0112] The deletion unit issues a deletion notification in consideration of the emotional state of the designated person. The deletion unit issues a deletion notification in consideration of the emotional state of the designated person. Consideration of the emotional state includes, for example, emotion recognition technology and emotion evaluation criteria, but is not limited to such examples. For example, when the designated person is relaxed, the deletion unit issues a deletion notification in a calm tone. In addition, when the designated person is stressed, the deletion unit can also issue a deletion notification in an encouraging tone. For example, the deletion unit dynamically adjusts the tone of the deletion notification according to the emotional state of the designated person. Furthermore, when the designated person is excited, the deletion unit can also issue a deletion notification in a calming tone. For example, the deletion unit monitors the emotional state of the designated person in real time and optimizes the tone of the deletion notification. As a result, the deletion notification can be issued in a more appropriate tone by considering the emotional state of the designated person.

[0113] The deletion unit estimates the user's emotion and determines the priority of data deletion based on the estimated user's emotion. The deletion unit estimates the user's emotion and determines the priority of data deletion based on the estimated emotion. The determination of the data deletion priority includes, for example, the urgency and importance of deletion, but is not limited to such examples. For example, when the user is relaxed, the deletion unit increases the priority of data deletion. In addition, when the user is stressed, the deletion unit can also lower the priority of data deletion. For example, the deletion unit dynamically adjusts the priority of data deletion according to the emotional state of the user. Furthermore, when the user is excited, the deletion unit can adjust the priority of data deletion to delete data at an appropriate time. For example, the deletion unit monitors the emotional state of the user in real time and optimizes the priority of data deletion. As a result, important data can be deleted preferentially by determining the priority of data deletion based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0114] The deletion unit automatically checks the contact information of the designated person and issues a deletion notification. The deletion unit automatically checks the contact information of the designated person and issues a deletion notification. The confirmation of the contact information includes, for example, a telephone number and an email address, but is not limited to such examples. The deletion unit automatically checks the contact information of the designated person and issues a deletion notification. In addition, when the contact information of the designated person is changed, the deletion unit can automatically update the contact information and issue a deletion notification. For example, the deletion unit detects a change in the contact information in real time and updates the deletion notification. Furthermore, the deletion unit can also select an optimal deletion notification method based on the contact information. For example, the deletion unit refers to the contact information of the designated person and selects an optimal notification method. In this way, an accurate deletion notification can be issued by automatically checking the contact information of the designated person.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The learning unit can also estimate the user's emotions and select learning data based on the estimated emotions. For example, if the user is relaxed, data on the voice and speech style in a relaxed state is preferentially learned. Also, if the user is stressed, data on the voice and speech style in a stressed state can be collected and reflected in the learning data. Furthermore, if the user is excited, data on the voice and speech style in an excited state can be collected and incorporated into the learning data. In this way, more appropriate AI can be generated by selecting learning data based on the user's emotions.

[0117] The request unit can also provide options for the return method when requesting the return of the tablet terminal. For example, if it is difficult for the person designated by the user to return the tablet terminal by mail, the request unit can guide the person to a nearby return location. Also, a return label can be enclosed with the return request notice to facilitate easy return. Furthermore, a detailed explanation of the return procedure can be included in the return request notice to enable the designated person to smoothly return the tablet terminal. In this way, by providing options for the return method, the designated person can easily return the tablet terminal.

[0118] The deletion unit may also have a function of temporarily backing up data before deletion when deleting data. For example, the deletion unit temporarily backs up data in the tablet device to cloud storage before deleting the data. The deletion unit may also back up data to external storage before deleting the data. Furthermore, the deletion unit may check with the user about the backup before deleting the data, and execute the deletion only after obtaining consent. In this way, by performing a temporary backup when deleting data, data can be protected.

[0119] The learning unit can also estimate the user's emotions and adjust the frequency of learning based on the estimated emotions. For example, when the user is relaxed, the learning frequency can be increased to collect data. Also, when the user is stressed, the learning frequency can be decreased to reduce the burden on the user. Furthermore, when the user is excited, the learning frequency can be adjusted to collect appropriate data. In this way, by adjusting the learning frequency based on the user's emotions, appropriate data can be collected while reducing the burden on the user.

[0120] The learning unit can also analyze the user's past dialogue history and select an appropriate learning algorithm. For example, it analyzes words and phrases frequently used by the user and adjusts the learning algorithm based on the results. It can also analyze the user's dialogue patterns and select the optimal learning algorithm. Furthermore, it can customize the learning data based on the analysis results of the dialogue history. In this way, it is possible to select the optimal learning algorithm by analyzing the past dialogue history.

[0121] The learning unit can also customize learning data based on the user's lifestyle and hobbies. For example, conversation data that takes place during a specific time period can be learned based on the user's lifestyle. Also, conversation data related to the user's hobbies can be preferentially learned. Furthermore, related conversation data can be customized and learned based on the user's interests and concerns. This allows for the generation of more personalized AI by customizing learning data based on the user's lifestyle and hobbies.

[0122] The learning unit can also estimate the user's emotions and select learning data based on the estimated emotions. For example, if the user is relaxed, data on the voice and speech style in a relaxed state is preferentially learned. Also, if the user is stressed, data on the voice and speech style in a stressed state can be collected and reflected in the learning data. Furthermore, if the user is excited, data on the voice and speech style in an excited state can be collected and incorporated into the learning data. In this way, more appropriate AI can be generated by selecting learning data based on the user's emotions.

[0123] The learning unit can also analyze the user's social media activities and reflect them in the learning data. For example, words and phrases frequently used by the user on social media can be reflected in the learning data. In addition, the learning unit can analyze the user's social media activity patterns and reflect them in the learning data. Furthermore, the learning data can be optimized based on information shared by the user on social media. In this way, by analyzing social media activities, more realistic learning data can be generated.

[0124] The learning unit can also incorporate region-specific language and culture into the learning data, taking into account the user's geographical location information. For example, the dialect and expressions of the region where the user lives can be incorporated into the learning data. In addition, region-specific culture and customs can be reflected in the learning data based on the user's geographical location information. Furthermore, information about region-specific events and occasions can be reflected in the learning data. In this way, by taking into account the geographical location information, it is possible to generate AI that reflects region-specific language and culture.

[0125] The embedding unit can also estimate the user's emotions and adjust the timing of embedding based on the estimated emotions. For example, if the user is relaxed, the embedding timing can be advanced. Also, if the user is stressed, the embedding timing can be delayed. Furthermore, if the user is excited, the embedding timing can be adjusted to embed the AI ​​at an appropriate timing. In this way, by adjusting the embedding timing based on the user's emotions, the AI ​​can be embedded at a more appropriate timing.

[0126] The process flow of the second embodiment will be briefly described below.

[0127] Step 1: The learning unit generates an AI that has learned the user's voice, speaking style, and personality. Specifically, the user's voice is recorded and voice features are extracted using voice recognition technology. The user's speaking style is also recorded and the characteristics of the speaking style are analyzed using natural language processing technology. Furthermore, the user is given a personality diagnostic test and their personality is evaluated based on the results. Step 2: The embedding unit embeds the AI ​​generated by the learning unit into the tablet device. Specifically, it installs the AI ​​software on the tablet device and performs the necessary settings. Step 3: The instruction unit instructs the delivery of the tablet device to a person designated by the user after the user's death. Specifically, the instruction unit checks the address of the person designated by the user, selects an appropriate delivery company, and instructs the delivery. Step 4: When the tablet device is delivered to the designated person, the dialogue unit uses AI to carry out a dialogue. Specifically, the AI ​​reproduces the voice and manner of speaking of the deceased person and engages in a dialogue with the designated person. Step 5: The suspension unit will suspend the AI-based service a predetermined period of time after the user's death. Specifically, the service will be suspended 49 days after the user's death, and the tablet device will be requested to be returned.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0130] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12. For example, the process for learning the user's voice, speech style, and personality is performed by the specific processing unit 290 of the data processing device 12. The embedding unit is realized by, for example, the control unit 46A of the smart device 14, and embeds the generated AI into the tablet terminal. The instruction unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and instructs the delivery of the tablet terminal after the user's death. The dialogue unit is realized by, for example, the control unit 46A of the smart device 14, and engages in dialogue with a designated person using AI. The stop unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and stops the service when a predetermined period of time has elapsed after the user's death. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12. For example, the process for learning the user's voice, speaking style, and personality is performed by the specific processing unit 290 of the data processing device 12. The embedding unit is realized by, for example, the control unit 46A of the smart glasses 214, and embeds the generated AI into the tablet terminal. The instruction unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and instructs the delivery of the tablet terminal after the death of the user. The dialogue unit is realized by, for example, the control unit 46A of the smart glasses 214, and engages in dialogue with a designated person using AI. The stop unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and stops the service when a predetermined period of time has elapsed after the death of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0155] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0158] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0163] For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12. For example, the process for learning the user's voice, speaking style, and personality is performed by the specific processing unit 290 of the data processing device 12. The embedding unit is realized by, for example, the control unit 46A of the headset type terminal 314, and embeds the generated AI into the tablet terminal. The instruction unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and instructs the delivery of the tablet terminal after the death of the user. The dialogue unit is realized by, for example, the control unit 46A of the headset type terminal 314, and engages in dialogue with a designated person using AI. The stop unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and stops the service when a predetermined period of time has passed after the death of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0168] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0170] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0171] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0172] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0175] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0176] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0180] For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12. For example, the process for learning the user's voice, speech style, and personality is performed by the specific processing unit 290 of the data processing device 12. The embedding unit is realized by, for example, the control unit 46A of the robot 414, and embeds the generated AI into the tablet terminal. The instruction unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and instructs the delivery of the tablet terminal after the user's death. The dialogue unit is realized by, for example, the control unit 46A of the robot 414, and engages in dialogue with a designated person using AI. The stopping unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and stops the service when a predetermined period of time has passed after the user's death. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0181] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0182] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0183] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0184] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0185] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0186] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0189] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0190] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0191] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0192] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0193] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0194] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0195] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0196] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0197] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0198] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0199] (Appendix 1) A learning unit that generates AI that learns the user's voice, speaking style, and personality; an embedding unit that embeds the AI ​​generated by the learning unit into a tablet terminal; an instruction unit that instructs delivery of the tablet device to a person designated by the user after the death of the user; The tablet terminal includes a dialogue unit that uses the AI ​​to dialogue with a person designated by the user; A system comprising: a stopping unit that stops the service provided by the AI ​​when a predetermined period of time has elapsed after the death of the user. (Appendix 2) The tablet terminal includes: and a request unit that requests the return of the tablet device when the service provided by the AI ​​is stopped by the stop unit. 2. The system of claim 1, (Appendix 3) The tablet terminal includes: A deletion unit that deletes data when the service provided by the AI ​​is stopped by the stopping unit. 2. The system of claim 1, (Appendix 4) The learning unit is Estimate the user's emotions and select learning data based on the estimated user emotions. 2. The system of claim 1, (Appendix 5) The learning unit is Analyze the user's past dialogue history and select the appropriate learning algorithm 2. The system of claim 1, (Appendix 6) The learning unit is Customize learning data based on user lifestyle and hobbies 2. The system of claim 1, (Appendix 7) The learning unit is Infer user emotions and adjust the frequency of learning based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The learning unit is Analyze users' social media activity and incorporate it into learning data 2. The system of claim 1, (Appendix 9) The learning unit is Incorporating local language and culture into training data based on the user's geographic location 2. The system of claim 1, (Appendix 10) The embedding portion is Infer user emotions and adjust the timing of embedding based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The embedding portion is Generate AI optimized for the hardware characteristics of tablet devices 2. The system of claim 1, (Appendix 12) The embedding portion is Customize AI operation modes according to the user's environment 2. The system of claim 1, (Appendix 13) The embedding portion is Infer user emotions and prioritize incorporations based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The embedding portion is Adjusting AI operations taking into account the battery life of tablet devices 2. The system of claim 1, (Appendix 15) The embedding portion is Incorporating additional features to enhance the security of tablet devices 2. The system of claim 1, (Appendix 16) The instruction unit is To estimate a user's emotion and adjust the timing of delivery instructions based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The instruction unit is Give delivery instructions taking into account the designated person's available time to receive the item 2. The system of claim 1, (Appendix 18) The instruction unit is Select the most suitable delivery method taking into account the geographical conditions of the delivery destination 2. The system of claim 1, (Appendix 19) The instruction unit is Estimate user emotions and prioritize delivery instructions based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The instruction unit is Automatically check the contact information of the designated person and provide delivery instructions 2. The system of claim 1, (Appendix 21) The instruction unit is Add real-time delivery tracking 2. The system of claim 1, (Appendix 22) The dialogue unit includes: Estimate the user's emotions and adjust the content of the dialogue based on the estimated user's emotions 2. The system of claim 1, (Appendix 23) The dialogue unit includes: Customize the tone of the conversation depending on the designated person's current situation 2. The system of claim 1, (Appendix 24) The dialogue unit includes: Estimate user emotions and prioritize interactions based on the estimated user emotions 2. The system of claim 1, (Appendix 25) The dialogue unit includes: Analyze the social media activity of designated individuals and reflect it in the conversations 2. The system of claim 1, (Appendix 26) The dialogue unit includes: Provides localized topics based on the geographic location of the designated person 2. The system of claim 1, (Appendix 27) The stop portion is Estimate user emotions and adjust the timing of service outages based on the estimated user emotions 2. The system of claim 1, (Appendix 28) The stop portion is Consider the emotional state of the designated person when issuing a stop notification 2. The system of claim 1, (Appendix 29) The stop portion is Add a function to safely back up data on tablet devices when the device is stopped 2. The system of claim 1, (Appendix 30) The stop portion is Estimate user sentiment and prioritize outages based on the estimated user sentiment 2. The system of claim 1, (Appendix 31) The stop portion is Automatically verify contact information of designated individuals and provide suspension notices 2. The system of claim 1, (Appendix 32) The stop portion is Incorporate additional features to enhance the security of tablet devices when they are down 2. The system of claim 1, (Appendix 33) The request unit: Estimate the user's emotions and adjust the timing of the return request based on the estimated user's emotions. 3. The system of claim 2, (Appendix 34) The request unit: Make a return request taking into consideration the time period when the designated person can return the item 3. The system of claim 2, (Appendix 35) The request unit: Estimate the user's emotions and prioritize return requests based on the estimated user emotions. 3. The system of claim 2, (Appendix 36) The request unit: Automatically check the contact information of the designated person and request the return 3. The system of claim 2, (Appendix 37) The deletion unit is Inferring user emotions and adjusting the timing of data deletion based on the estimated user emotions 4. The system of claim 3. (Appendix 38) The deletion unit is Taking into account the emotional state of the designated person in the takedown notice 4. The system of claim 3. (Appendix 39) The deletion unit is Inferring user sentiment and prioritizing data deletion based on the inferred user sentiment 4. The system of claim 3. (Appendix 40) The deletion unit is Automatically verify contact information of designated individuals and provide takedown notices 4. The system of claim 3. [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A learning unit that generates AI that learns the user's voice, speaking style, and personality; An embedding unit that embeds the AI ​​generated by the learning unit into a tablet terminal; an instruction unit that instructs delivery of the tablet device to a person designated by the user after the death of the user; The tablet terminal includes a dialogue unit that uses the AI ​​to dialogue with a person designated by the user; A system comprising: a stopping unit that stops the AI ​​service upon a predetermined period of time after the death of the user.

2. The tablet terminal includes: A request unit that requests the return of the tablet terminal when the AI ​​service is stopped by the stop unit.

2. The system of claim 1 .

3. The tablet terminal includes: A deletion unit that deletes data when the service provided by the AI ​​is stopped by the stop unit.

2. The system of claim 1 .

4. The learning unit is The user's emotion is estimated, and learning data is selected based on the estimated user's emotion.

2. The system of claim 1 .

5. The learning unit is Analyzing the user's past dialogue history and selecting an appropriate learning algorithm 2. The system of claim 1 .

6. The learning unit is Customize learning data based on the user's lifestyle and hobbies 2. The system of claim 1 .

7. The learning unit is Estimating the user's emotion and adjusting the frequency of learning based on the estimated user's emotion.

2. The system of claim 1 .

8. The learning unit is Analyzing the user's social media activity and incorporating it into learning data 2. The system of claim 1 .

9. The learning unit is Incorporating local language and culture into the learning data based on the user's geographic location.

2. The system of claim 1 .

10. The embedding portion is Estimating the user's emotion and adjusting the timing of incorporation based on the estimated user's emotion.

2. The system of claim 1 .

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