system

Abstract

The system according to the embodiment aims to allow orphans to connect with memories of their parents and receive psychological support. [Solution] A system according to an embodiment includes a storage unit, a generation unit, and a provision unit. The storage unit stores data of parental memories in the cloud. The generation unit analyzes the data stored by the storage unit and generates an automatic response. The provision unit provides the automatic response generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has limited opportunities for orphans to connect with memories of their parents, and there has been a lack of psychological support.

[0005] The system according to the embodiment aims to allow orphans to connect with memories of their parents and receive psychological support. [Means for solving the problem]

[0006] The system according to the embodiment includes a storage unit, a generation unit, and a provision unit. The storage unit stores data of the parent's memories in the cloud. The generation unit analyzes the data stored by the storage unit and generates an automatic response. The provision unit provides the automatic response generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows orphans to connect with memories of their parents and receive psychological support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention stores parents' memory albums, emails, letters, and past social media interactions in a closed cloud environment and loads them into a generation AI to create an automatic chat response system. This system sets up an account on a social media platform as a virtual personality and can respond 24 hours a day. It also has the ability to periodically share memorable photos and send messages via social media. For those without smartphones, a photo frame equipped with a speaker is provided, allowing users to talk to the device. This device is installed at a bedside or other location so that conversations can be held at any time. This system aims to provide support to orphans and is intended for use in photo frames in Japan and social media overseas. For example, the system may include a storage unit that stores parents' memory data in the cloud, a generation unit that analyzes the stored data and generates automatic responses, and a provision unit that provides the generated automatic responses. The provision unit sets up an account on a social media platform as a virtual personality and can respond 24 hours a day. The provision unit also periodically shares memorable photos and sends messages via social media. The photo frame is equipped with a speaker and a response unit that responds when spoken to. The response unit is installed at a bedside or other location so that conversations can be held at any time. This allows the system to store parents' memories in the cloud and generate and provide automated responses to support orphaned children.

[0029] The system according to the embodiment includes a storage unit, a generation unit, and a provision unit. The storage unit stores data about the parent's memories in the cloud. The data about the parent's memories includes, but is not limited to, photos, videos, and voice messages. The storage unit can store data using cloud services such as AWS (registered trademark), Google (registered trademark), and Azure (registered trademark). The generation unit analyzes the data stored by the storage unit and generates an automatic response. The automatic response includes, but is not limited to, text messages and voice responses. The generation unit uses a generation AI to analyze the data about the parent's memories and generate an appropriate response. The generation AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. The provision unit provides the automatic response generated by the generation unit. The provision unit can, for example, set up an account as a virtual personality on a social networking site (SNS) and respond 24 hours a day. The provision unit can also periodically distribute photos of memories and send messages via the SNS. Furthermore, the provision unit is equipped with a speaker in the photo frame, and a response unit that responds when spoken to. The response unit is installed at the bedside or elsewhere so that conversations can be held at any time. This allows the system to store parents' memories in the cloud and generate and provide automatic responses, allowing it to be there for orphans.

[0030] The providing unit can set up an account on a social networking site as a virtual personality and respond 24 hours a day. The providing unit can, for example, set up an account on a social networking site as a virtual personality and respond 24 hours a day. Methods for setting up a virtual personality include, for example, setting personality characteristics and response patterns. The virtual personality can, for example, imitate a parent's personality or speaking style. Methods for implementing 24-hour responses include, for example, operating a shift system or automatic responses by AI. Automatic responses by AI can, for example, use a generation AI to generate appropriate responses to user messages. This makes it possible to provide support to orphans by responding 24 hours a day as a virtual personality on a social networking site.

[0031] The providing unit can periodically distribute memorable photos and send messages through SNS. The providing unit can, for example, periodically distribute memorable photos and send messages through SNS. Specific frequency and timing of "periodically" include, for example, daily, weekly, monthly, etc. Memorable photos include, for example, family photos and travel photos. The providing unit can, for example, select memorable photos of parents and distribute them through SNS. The providing unit can also send messages through SNS. Messages include, for example, words of parents or messages of encouragement. In this way, by periodically distributing memorable photos and sending messages through SNS, it is possible to support orphans.

[0032] The system includes a photo frame equipped with a speaker and a response unit that responds when spoken to. The photo frame has specific specifications, such as display size, resolution, and connection method. The speaker has specific specifications, such as sound quality, output, and connection method. The response unit can respond when a user speaks to it, for example, using the speaker equipped in the photo frame. The response unit can generate an appropriate response to a user's question using, for example, a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the photo frame equipped with a speaker that responds when spoken to can provide support to orphans.

[0033] The response unit can be installed at the bedside so that conversation can be held at any time. The response unit is installed, for example, at the bedside. The installation location includes, for example, a nightstand or a wall hanging. The response unit can respond when the user speaks to it, for example, using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. In this way, by installing the response unit at the bedside so that conversation can be held at any time, it is possible to provide support to orphans.

[0034] The storage unit can select an appropriate storage method depending on the type of parental memory data. For example, the storage unit selects the optimal storage method depending on the type of parental memory data. Specific types of parental memory data include, for example, photos, emails, and letters. For example, the storage unit can save photo data at high resolution and tag it. The storage unit can also perform text analysis on email data to extract and save important parts. Furthermore, the storage unit can scan letter data, save it in PDF format, and convert it into a searchable format. This allows for efficient data management by selecting the optimal storage method depending on the type of parental memory data.

[0035] The storage unit can determine the storage priority based on the importance of the data. For example, when storing data, the storage unit determines the storage priority based on the importance of the data. Specific evaluation criteria and methods for the importance of data include, for example, user ratings and access frequency. The storage unit can preferentially store data with high importance, such as a parent's last message. The storage unit can also store data of daily interactions later. Furthermore, the storage unit can preferentially store data related to important events designated by the user. In this way, by determining the storage priority based on the importance of the data, important data can be preferentially stored.

[0036] The storage unit can preferentially store highly relevant data based on the user's geographical location information. For example, when storing data, the storage unit preferentially stores highly relevant data taking into account the user's geographical location information. Specific methods for acquiring and using geographical location information include, for example, GPS data and location information services. For example, when the user is in a specific location, the storage unit can preferentially store data of memories related to that location. Furthermore, when the user is traveling, the storage unit can preferentially store data related to the travel destination. Furthermore, when the user is at home, the storage unit can preferentially store data related to the home. In this way, by preferentially storing highly relevant data taking into account the user's geographical location information, more relevant data can be stored.

[0037] The storage unit can analyze the user's social media activity and store related data. For example, when storing data, the storage unit analyzes the user's social media activity and stores related data. Specific analysis methods and target data of social media activity include, for example, the content of posts, the number of likes, and comments. For example, if a user posts about a specific event on the SNS, the storage unit can preferentially store data related to the event. Furthermore, if a user frequently interacts with a specific person on the SNS, the storage unit can preferentially store data related to that person. Furthermore, if a user posts about a specific place on the SNS, the storage unit can preferentially store data related to that place. In this way, by analyzing the user's social media activity and storing related data, more relevant data can be stored.

[0038] The generation unit can adjust the level of detail of the response based on the content of the parent's memory data. For example, when generating an automatic response, the generation unit adjusts the level of detail of the response based on the content of the parent's memory data. Specific content of the parent's memory data includes, for example, photos, videos, and audio messages. Specific methods and criteria for adjusting the level of detail of the response include, for example, a detailed explanation or a concise response. For example, a detailed response can be generated based on the parent's last message. Also, a concise response can be generated based on daily interactions. Furthermore, a detailed response can be generated based on data related to a specific event. As a result, by adjusting the level of detail of the response based on the content of the parent's memory data, a more appropriate response can be provided.

[0039] The generation unit can apply different generation algorithms depending on the data category. For example, when generating an automatic response, the generation unit applies different generation algorithms depending on the data category. Specific classification criteria and types of data categories include, for example, photos, videos, and voice messages. Specific types and application methods of the generation algorithm include, for example, natural language generation and image generation. For example, the generation unit can generate a visual response based on photo data. The generation unit can also generate a text-based response based on email data. Furthermore, the generation unit can generate an emotional response based on letter data. This allows for more appropriate responses to be provided by applying different generation algorithms depending on the data category.

[0040] The generation unit can determine the priority of responses based on the time of data submission. For example, when generating an automatic response, the generation unit determines the priority of responses based on the time of data submission. Specific criteria and acquisition methods for the time of data submission include, for example, the submission date and time or the time of event occurrence. Specific criteria and methods for determining the priority of responses include, for example, a priority score based on the time of submission. For example, the generation unit can generate responses preferentially based on recently submitted data. Also, the generation unit can generate responses later based on older data. Furthermore, the generation unit can generate responses preferentially based on data related to a specific event. In this way, by determining the priority of responses based on the time of data submission, more appropriate responses can be provided.

[0041] The generation unit can adjust the order of responses based on the relevance of the data. For example, when generating an automatic response, the generation unit adjusts the order of responses based on the relevance of the data. Specific evaluation criteria and methods for the relevance of the data include, for example, a relevance score and a co-occurrence relationship. Specific adjustment methods and criteria for the order of responses include, for example, ordering based on relevance. For example, the generation unit can first generate a response based on the parent's last message. The generation unit can also generate a response later based on daily interactions. Furthermore, the generation unit can prioritize generating a response based on data related to a specific event. This allows for providing a more appropriate response by adjusting the order of responses based on the relevance of the data.

[0042] The providing unit can adjust the level of detail of the provided data based on the importance of the parent's memory data. For example, when providing the data, the providing unit adjusts the level of detail of the provided data based on the importance of the parent's memory data. Specific evaluation criteria and methods for the importance of the parent's memory data include, for example, user ratings and access frequency. Specific adjustment methods and criteria for the level of detail of the provided data include, for example, a detailed explanation or a concise response. For example, a detailed automatic response can be provided based on the parent's last message. Also, a concise automatic response can be provided based on daily interactions. Furthermore, a detailed automatic response can be provided based on data related to a specific event. As a result, by adjusting the level of detail of the provided data based on the importance of the parent's memory data, a more appropriate response can be provided.

[0043] The providing unit may apply different providing methods depending on the data category. For example, the providing unit may apply different providing methods depending on the data category when providing the data. Specific classification criteria and types of data categories include, for example, photos, videos, and voice messages. Specific types and application methods of the providing method include, for example, text messages and voice responses. For example, the providing unit may provide a visual automatic response based on photo data. The providing unit may also provide a text-based automatic response based on email data. Furthermore, the providing unit may provide an emotional automatic response based on letter data. This allows for more appropriate responses to be provided by applying different providing methods depending on the data category.

[0044] The providing unit can provide a highly relevant automatic response preferentially by taking into account the user's geographical location information. For example, when providing, the providing unit provides a highly relevant automatic response preferentially by taking into account the user's geographical location information. Specific methods for acquiring and using geographical location information include, for example, GPS data and location information services. Specific criteria and selection methods for a highly relevant automatic response include, for example, a relevance score based on location information. For example, when a user is in a specific location, an automatic response related to that location can be provided preferentially. Furthermore, when a user is traveling, an automatic response related to the user's travel destination can be provided preferentially. Furthermore, when a user is at home, an automatic response related to the user's home can be provided preferentially. This allows for more appropriate responses to be provided by providing a highly relevant automatic response by taking into account the user's geographical location information.

[0045] The providing unit can analyze the user's social media activity and provide a relevant automatic response. For example, at the time of providing, the providing unit analyzes the user's social media activity and provides the relevant automatic response. Specific analysis methods and target data of social media activity include, for example, the content of posts, the number of likes, and comments. Specific criteria and selection methods for the relevant automatic response include, for example, a relevance score based on social media activity. For example, if a user posts about a specific event on the social media, an automatic response related to the event can be provided. Also, if a user frequently interacts with a specific person on the social media, an automatic response related to the person can be provided. Furthermore, if a user posts about a specific location on the social media, an automatic response related to the location can be provided. In this way, by analyzing the user's social media activity and providing a relevant automatic response, more appropriate responses can be provided.

[0046] The response unit can adjust the level of detail of the response based on the content of the parent's memory data. For example, when responding, the response unit adjusts the level of detail of the response based on the content of the parent's memory data. Specific content of the parent's memory data includes, for example, photos, videos, and voice messages. Specific methods and criteria for adjusting the level of detail of the response include, for example, a detailed explanation or a concise response. For example, a detailed response can be provided based on the parent's last message. Alternatively, a concise response can be provided based on daily interactions. Furthermore, a detailed response can be provided based on data related to a specific event. Thus, by adjusting the level of detail of the response based on the content of the parent's memory data, a more appropriate response can be provided.

[0047] The response unit can apply different response algorithms depending on the data category. For example, when responding, the response unit applies different response algorithms depending on the data category. Specific classification criteria and types of data categories include, for example, photos, videos, and voice messages. Specific types and application methods of response algorithms include, for example, natural language generation and image generation. For example, the response unit can provide a visual response based on photo data. The response unit can also provide a text-based response based on email data. Furthermore, the response unit can provide an emotional response based on letter data. This allows for more appropriate responses to be provided by applying different response algorithms depending on the data category.

[0048] The response unit can determine the priority of responses based on the time of data submission. For example, at the time of response, the response unit determines the priority of responses based on the time of data submission. Specific criteria and acquisition methods for the time of data submission include, for example, the submission date and time or the time of event occurrence. Specific criteria and methods for determining the priority of responses include, for example, a priority score based on the time of submission. For example, the response unit can provide a response preferentially based on recently submitted data. Also, the response unit can provide a response later based on older data. Furthermore, the response unit can provide a response preferentially based on data related to a specific event. In this way, by determining the priority of responses based on the time of data submission, more appropriate responses can be provided.

[0049] The response unit can adjust the order of responses based on the relevance of the data. For example, when responding, the response unit adjusts the order of responses based on the relevance of the data. Specific evaluation criteria and methods for the relevance of the data include, for example, a relevance score and a co-occurrence relationship. Specific adjustment methods and criteria for the order of responses include, for example, ordering based on relevance. For example, the response unit can provide a response first based on the parent's last message. Also, the response unit can provide a response later based on daily interactions. Furthermore, the response unit can provide a response preferentially based on data related to a specific event. This allows for providing a more appropriate response by adjusting the order of responses based on the relevance of the data.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The storage unit can select an appropriate storage method depending on the type of parental memory data. For example, it can save photo data at high resolution and tag it. It can also analyze email data and extract and save important parts. It can also scan letter data, save it in PDF format, and convert it into a searchable format. This allows the system to select the optimal storage method depending on the type of parental memory data, making data management more efficient.

[0052] The storage unit can determine the storage priority based on the importance of the data. For example, it can store data of high importance, such as a parent's last message, with priority. It can also store data of daily interactions later. It can also store data related to important events designated by the user with priority. Thus, by determining the storage priority based on the importance of the data, it is possible to store important data with priority.

[0053] The storage unit can preferentially store highly relevant data based on the user's geographical location information. For example, when the user is in a specific location, data of memories related to that location can be preferentially stored. When the user is traveling, data related to the travel destination can be preferentially stored. When the user is at home, data related to the home can be preferentially stored. In this way, by preferentially storing highly relevant data in consideration of the user's geographical location information, more highly relevant data can be stored.

[0054] The storage unit can analyze the user's social media activity and store related data. For example, if the user posts about a specific event on the SNS, data related to the event can be preferentially stored. Also, if the user frequently interacts with a specific person on the SNS, data related to that person can be preferentially stored. Furthermore, if the user posts about a specific place on the SNS, data related to that place can be preferentially stored. In this way, by analyzing the user's social media activity and storing related data, more relevant data can be stored.

[0055] The generator can apply different generation algorithms depending on the data category. For example, a visual response can be generated based on photo data, a text-based response can be generated based on email data, or an emotional response can be generated based on letter data. This allows for more appropriate responses to be provided by applying different generation algorithms depending on the data category.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The storage unit stores the parent's memory data in the cloud. The parent's memory data may include, but is not limited to, photos, videos, and voice messages. The storage unit may store the data using a cloud service such as AWS, Google Cloud, or Azure. Step 2: The generation unit analyzes the data stored by the storage unit and generates an automatic response. The automatic response may be, for example, a text message or a voice response, but is not limited to these examples. The generation unit uses a generation AI to analyze the parent's memory data and generate an appropriate response. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Step 3: The providing unit provides the automatic response generated by the generating unit. For example, the providing unit can set up an account on a social networking site as a virtual personality and respond 24 hours a day. The providing unit can also periodically distribute memorable photos and send messages via the social networking site. Furthermore, the providing unit is equipped with a response unit that is equipped with a speaker in the photo frame and responds when spoken to. The response unit is installed at the bedside or elsewhere so that conversations can be held at any time.

[0058] (Example 2) A system according to an embodiment of the present invention stores parents' memory albums, emails, letters, and past social media interactions in a closed cloud environment and loads them into a generation AI to create an automatic chat response system. This system sets up an account on a social media platform as a virtual personality and can respond 24 hours a day. It also has the ability to periodically share memorable photos and send messages via social media. For those without smartphones, a photo frame equipped with a speaker is provided, allowing users to talk to the device. This device is installed at a bedside or other location so that conversations can be held at any time. This system aims to provide support to orphans and is intended for use in photo frames in Japan and social media overseas. For example, the system may include a storage unit that stores parents' memory data in the cloud, a generation unit that analyzes the stored data and generates automatic responses, and a provision unit that provides the generated automatic responses. The provision unit sets up an account on a social media platform as a virtual personality and can respond 24 hours a day. The provision unit also periodically shares memorable photos and sends messages via social media. The photo frame is equipped with a speaker and a response unit that responds when spoken to. The response unit is installed at a bedside or other location so that conversations can be held at any time. This allows the system to store parents' memories in the cloud and generate and provide automated responses to support orphaned children.

[0059] The system according to the embodiment includes a storage unit, a generation unit, and a provision unit. The storage unit stores data of the parent's memories in the cloud. The data of the parent's memories includes, but is not limited to, photos, videos, and voice messages. The storage unit can store data using cloud services such as AWS, Google Cloud, and Azure. The generation unit analyzes the data stored by the storage unit and generates an automatic response. The automatic response includes, but is not limited to, text messages and voice responses. The generation unit uses a generation AI to analyze the data of the parent's memories and generate an appropriate response. The generation AI includes, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the automatic response generated by the generation unit. The provision unit can, for example, set up an account as a virtual personality on a social networking site (SNS) and respond 24 hours a day. The provision unit can also periodically distribute memorable photos and send messages via the SNS. The provision unit also includes a response unit equipped with a speaker in the photo frame that responds when spoken to. The response unit is installed at the bedside or other locations so that conversations can be held at any time. This allows the system to store parents' memories in the cloud and generate and provide automatic responses to support orphans.

[0060] The providing unit can set up an account on a social networking site as a virtual personality and respond 24 hours a day. The providing unit can, for example, set up an account on a social networking site as a virtual personality and respond 24 hours a day. Methods for setting up a virtual personality include, for example, setting personality characteristics and response patterns. The virtual personality can, for example, imitate a parent's personality or speaking style. Methods for implementing 24-hour responses include, for example, operating a shift system or automatic responses by AI. Automatic responses by AI can, for example, use a generation AI to generate appropriate responses to user messages. This makes it possible to provide support to orphans by responding 24 hours a day as a virtual personality on a social networking site.

[0061] The providing unit can periodically distribute memorable photos and send messages through SNS. The providing unit can, for example, periodically distribute memorable photos and send messages through SNS. Specific frequency and timing of "periodically" include, for example, daily, weekly, monthly, etc. Memorable photos include, for example, family photos and travel photos. The providing unit can, for example, select memorable photos of parents and distribute them through SNS. The providing unit can also send messages through SNS. Messages include, for example, words of parents or messages of encouragement. In this way, by periodically distributing memorable photos and sending messages through SNS, it is possible to support orphans.

[0062] The system includes a photo frame equipped with a speaker and a response unit that responds when spoken to. The photo frame has specific specifications, such as display size, resolution, and connection method. The speaker has specific specifications, such as sound quality, output, and connection method. The response unit can respond when a user speaks to it, for example, using the speaker equipped in the photo frame. The response unit can generate an appropriate response to a user's question using, for example, a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the photo frame equipped with a speaker that responds when spoken to can provide support to orphans.

[0063] The response unit can be installed at the bedside so that conversation can be held at any time. The response unit is installed, for example, at the bedside. The installation location includes, for example, a nightstand or a wall hanging. The response unit can respond when the user speaks to it, for example, using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. In this way, by installing the response unit at the bedside so that conversation can be held at any time, it is possible to provide support to orphans.

[0064] The storage unit can estimate the user's emotion and adjust the timing of data storage based on the estimated user's emotion. The storage unit, for example, estimates the user's emotion. Specific methods for estimating the user's emotion include, for example, facial expression recognition and voice analysis. The storage unit, for example, adjusts the timing of data storage based on the estimated user's emotion. Specific criteria and methods for the timing of data storage include, for example, a specific time period or when an event occurs. For example, if the user is sad, data storage can be delayed until the user's emotion calms down. Furthermore, if the user is relaxed, data can be stored immediately and a notification can be made at a timing that matches the user's emotion. Furthermore, if the user is busy, data storage can be performed according to the user's schedule. In this way, by adjusting the timing of data storage based on the user's emotion, data can be stored at a more appropriate time.

[0065] The storage unit can select an appropriate storage method depending on the type of parental memory data. For example, the storage unit selects the optimal storage method depending on the type of parental memory data. Specific types of parental memory data include, for example, photos, emails, and letters. For example, the storage unit can save photo data at high resolution and tag it. The storage unit can also perform text analysis on email data to extract and save important parts. Furthermore, the storage unit can scan letter data, save it in PDF format, and convert it into a searchable format. This allows for efficient data management by selecting the optimal storage method depending on the type of parental memory data.

[0066] The storage unit can determine the storage priority based on the importance of the data. For example, when storing data, the storage unit determines the storage priority based on the importance of the data. Specific evaluation criteria and methods for the importance of data include, for example, user ratings and access frequency. The storage unit can preferentially store data with high importance, such as a parent's last message. The storage unit can also store data of daily interactions later. Furthermore, the storage unit can preferentially store data related to important events designated by the user. In this way, by determining the storage priority based on the importance of the data, important data can be preferentially stored.

[0067] The storage unit can estimate the user's emotion and determine the priority of data to be stored based on the estimated user's emotion. The storage unit, for example, estimates the user's emotion. Specific methods for estimating the user's emotion include, for example, facial expression recognition and voice analysis. The storage unit, for example, determines the priority of data to be stored based on the estimated user's emotion. Specific methods and criteria for determining the priority of data to be stored include, for example, importance scores and urgency. For example, if the user is sad, data of happy memories can be preferentially stored to ease the emotion. Furthermore, if the user is relaxed, data of daily interactions can be preferentially stored. Furthermore, if the user is busy, data of high importance can be preferentially stored. In this way, by determining the priority of data to be stored based on the user's emotion, more appropriate data can be preferentially stored.

[0068] The storage unit can preferentially store highly relevant data based on the user's geographical location information. For example, when storing data, the storage unit preferentially stores highly relevant data taking into account the user's geographical location information. Specific methods for acquiring and using geographical location information include, for example, GPS data and location information services. For example, when the user is in a specific location, the storage unit can preferentially store data of memories related to that location. Furthermore, when the user is traveling, the storage unit can preferentially store data related to the travel destination. Furthermore, when the user is at home, the storage unit can preferentially store data related to the home. In this way, by preferentially storing highly relevant data taking into account the user's geographical location information, more relevant data can be stored.

[0069] The storage unit can analyze the user's social media activity and store related data. For example, when storing data, the storage unit analyzes the user's social media activity and stores related data. Specific analysis methods and target data of social media activity include, for example, the content of posts, the number of likes, and comments. For example, if a user posts about a specific event on the SNS, the storage unit can preferentially store data related to the event. Furthermore, if a user frequently interacts with a specific person on the SNS, the storage unit can preferentially store data related to that person. Furthermore, if a user posts about a specific place on the SNS, the storage unit can preferentially store data related to that place. In this way, by analyzing the user's social media activity and storing related data, more relevant data can be stored.

[0070] The generation unit can estimate the user's emotion and adjust the expression style of the automatic response based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. Specific methods for estimating the user's emotion include, for example, facial expression recognition and voice analysis. The generation unit, for example, adjusts the expression style of the automatic response based on the estimated user's emotion. Specific adjustment methods and criteria for the expression style of the automatic response include, for example, the tone of the text and the intonation of the voice. For example, if the user is sad, the automatic response can be generated using gentle language. Furthermore, if the user is relaxed, the automatic response can be generated using casual language. Furthermore, if the user is excited, the automatic response can be generated using cheerful language. In this way, by adjusting the expression style of the automatic response based on the user's emotion, a more appropriate response can be provided.

[0071] The generation unit can adjust the level of detail of the response based on the content of the parent's memory data. For example, when generating an automatic response, the generation unit adjusts the level of detail of the response based on the content of the parent's memory data. Specific content of the parent's memory data includes, for example, photos, videos, and audio messages. Specific methods and criteria for adjusting the level of detail of the response include, for example, a detailed explanation or a concise response. For example, a detailed response can be generated based on the parent's last message. Also, a concise response can be generated based on daily interactions. Furthermore, a detailed response can be generated based on data related to a specific event. As a result, by adjusting the level of detail of the response based on the content of the parent's memory data, a more appropriate response can be provided.

[0072] The generation unit can apply different generation algorithms depending on the data category. For example, when generating an automatic response, the generation unit applies different generation algorithms depending on the data category. Specific classification criteria and types of data categories include, for example, photos, videos, and voice messages. Specific types and application methods of the generation algorithm include, for example, natural language generation and image generation. For example, the generation unit can generate a visual response based on photo data. The generation unit can also generate a text-based response based on email data. Furthermore, the generation unit can generate an emotional response based on letter data. This allows for more appropriate responses to be provided by applying different generation algorithms depending on the data category.

[0073] The generation unit can estimate the user's emotion and adjust the length of the automatic response based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. Specific methods for estimating the user's emotion include, for example, facial expression recognition and voice analysis. The generation unit, for example, adjusts the length of the automatic response based on the estimated user's emotion. Specific methods and criteria for adjusting the length of the automatic response include, for example, the number of characters and time. For example, if the user is sad, a longer response can be generated to soothe the emotion. Also, if the user is relaxed, a shorter response can be generated to provide a casual conversation. Furthermore, if the user is in a hurry, a concise response can be generated to provide quick communication. As a result, by adjusting the length of the automatic response based on the user's emotion, a more appropriate response can be provided.

[0074] The generation unit can determine the priority of responses based on the time of data submission. For example, when generating an automatic response, the generation unit determines the priority of responses based on the time of data submission. Specific criteria and acquisition methods for the time of data submission include, for example, the submission date and time or the time of event occurrence. Specific criteria and methods for determining the priority of responses include, for example, a priority score based on the time of submission. For example, the generation unit can generate responses preferentially based on recently submitted data. Also, the generation unit can generate responses later based on older data. Furthermore, the generation unit can generate responses preferentially based on data related to a specific event. In this way, by determining the priority of responses based on the time of data submission, more appropriate responses can be provided.

[0075] The generation unit can adjust the order of responses based on the relevance of the data. For example, when generating an automatic response, the generation unit adjusts the order of responses based on the relevance of the data. Specific evaluation criteria and methods for the relevance of the data include, for example, a relevance score and a co-occurrence relationship. Specific adjustment methods and criteria for the order of responses include, for example, ordering based on relevance. For example, the generation unit can first generate a response based on the parent's last message. The generation unit can also generate a response later based on daily interactions. Furthermore, the generation unit can prioritize generating a response based on data related to a specific event. This allows for providing a more appropriate response by adjusting the order of responses based on the relevance of the data.

[0076] The providing unit can estimate the user's emotion and adjust the timing of the automatic response to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. Specific methods for estimating the user's emotion include, for example, facial expression recognition and voice analysis. The providing unit, for example, adjusts the timing of the automatic response to be provided based on the estimated user's emotion. Specific methods and standards for adjusting the timing of the automatic response include, for example, real-time and delayed response. For example, if the user is sad, the automatic response can be delayed until the user's emotion calms down. Also, if the user is relaxed, the automatic response can be provided immediately. Furthermore, if the user is busy, the automatic response can be provided according to the user's schedule. In this way, by adjusting the timing of the automatic response based on the user's emotion, the response can be provided at a more appropriate time.

[0077] The providing unit can adjust the level of detail of the provided data based on the importance of the parent's memory data. For example, when providing the data, the providing unit adjusts the level of detail of the provided data based on the importance of the parent's memory data. Specific evaluation criteria and methods for the importance of the parent's memory data include, for example, user ratings and access frequency. Specific adjustment methods and criteria for the level of detail of the provided data include, for example, a detailed explanation or a concise response. For example, a detailed automatic response can be provided based on the parent's last message. Also, a concise automatic response can be provided based on daily interactions. Furthermore, a detailed automatic response can be provided based on data related to a specific event. As a result, by adjusting the level of detail of the provided data based on the importance of the parent's memory data, a more appropriate response can be provided.

[0078] The providing unit may apply different providing methods depending on the data category. For example, the providing unit may apply different providing methods depending on the data category when providing the data. Specific classification criteria and types of data categories include, for example, photos, videos, and voice messages. Specific types and application methods of the providing method include, for example, text messages and voice responses. For example, the providing unit may provide a visual automatic response based on photo data. The providing unit may also provide a text-based automatic response based on email data. Furthermore, the providing unit may provide an emotional automatic response based on letter data. This allows for more appropriate responses to be provided by applying different providing methods depending on the data category.

[0079] The providing unit can estimate the user's emotion and determine the priority of automatic responses to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. Specific methods for estimating the user's emotion include, for example, facial expression recognition and voice analysis. The providing unit, for example, determines the priority of automatic responses to be provided based on the estimated user's emotion. Specific methods and criteria for determining the priority of automatic responses include, for example, a priority score based on emotion. For example, if the user is sad, an automatic response to soothe the emotion can be provided preferentially. Furthermore, if the user is relaxed, an automatic response for everyday interactions can be provided preferentially. Furthermore, if the user is busy, an automatic response with a high level of importance can be provided preferentially. In this way, by determining the priority of automatic responses based on the user's emotion, more appropriate responses can be provided.

[0080] The providing unit can provide a highly relevant automatic response preferentially by taking into account the user's geographical location information. For example, when providing, the providing unit provides a highly relevant automatic response preferentially by taking into account the user's geographical location information. Specific methods for acquiring and using geographical location information include, for example, GPS data and location information services. Specific criteria and selection methods for a highly relevant automatic response include, for example, a relevance score based on location information. For example, when a user is in a specific location, an automatic response related to that location can be provided preferentially. Furthermore, when a user is traveling, an automatic response related to the user's travel destination can be provided preferentially. Furthermore, when a user is at home, an automatic response related to the user's home can be provided preferentially. This allows for more appropriate responses to be provided by providing a highly relevant automatic response by taking into account the user's geographical location information.

[0081] The providing unit can analyze the user's social media activity and provide a relevant automatic response. For example, at the time of providing, the providing unit analyzes the user's social media activity and provides the relevant automatic response. Specific analysis methods and target data of social media activity include, for example, the content of posts, the number of likes, and comments. Specific criteria and selection methods for the relevant automatic response include, for example, a relevance score based on social media activity. For example, if a user posts about a specific event on the social media, an automatic response related to the event can be provided. Also, if a user frequently interacts with a specific person on the social media, an automatic response related to the person can be provided. Furthermore, if a user posts about a specific location on the social media, an automatic response related to the location can be provided. In this way, by analyzing the user's social media activity and providing a relevant automatic response, more appropriate responses can be provided.

[0082] The response unit can estimate the user's emotion and adjust the response expression method based on the estimated user's emotion. The response unit, for example, estimates the user's emotion. Specific methods for estimating the user's emotion include, for example, facial expression recognition and voice analysis. The response unit, for example, adjusts the response expression method based on the estimated user's emotion. Specific methods and standards for adjusting the response expression method include, for example, text tone and voice intonation. For example, if the user is sad, the response can be made using gentle language. Also, if the user is relaxed, the response can be made using casual language. Furthermore, if the user is excited, the response can be made using cheerful language. In this way, by adjusting the response expression method based on the user's emotion, a more appropriate response can be provided.

[0083] The response unit can adjust the level of detail of the response based on the content of the parent's memory data. For example, when responding, the response unit adjusts the level of detail of the response based on the content of the parent's memory data. Specific content of the parent's memory data includes, for example, photos, videos, and voice messages. Specific methods and criteria for adjusting the level of detail of the response include, for example, a detailed explanation or a concise response. For example, a detailed response can be provided based on the parent's last message. Alternatively, a concise response can be provided based on daily interactions. Furthermore, a detailed response can be provided based on data related to a specific event. Thus, by adjusting the level of detail of the response based on the content of the parent's memory data, a more appropriate response can be provided.

[0084] The response unit can apply different response algorithms depending on the data category. For example, when responding, the response unit applies different response algorithms depending on the data category. Specific classification criteria and types of data categories include, for example, photos, videos, and voice messages. Specific types and application methods of response algorithms include, for example, natural language generation and image generation. For example, the response unit can provide a visual response based on photo data. The response unit can also provide a text-based response based on email data. Furthermore, the response unit can provide an emotional response based on letter data. This allows for more appropriate responses to be provided by applying different response algorithms depending on the data category.

[0085] The response unit can estimate the user's emotion and adjust the length of the response based on the estimated user's emotion. The response unit, for example, estimates the user's emotion. Specific methods for estimating the user's emotion include, for example, facial expression recognition and voice analysis. The response unit, for example, adjusts the length of the response based on the estimated user's emotion. Specific methods and criteria for adjusting the length of the response include, for example, the number of characters and time. For example, if the user is sad, a longer response can be provided to soothe the emotion. Also, if the user is relaxed, a shorter response can be provided to provide a casual conversation. Furthermore, if the user is in a hurry, a concise response can be provided to provide quick communication. As a result, by adjusting the length of the response based on the user's emotion, a more appropriate response can be provided.

[0086] The response unit can determine the priority of responses based on the time of data submission. For example, at the time of response, the response unit determines the priority of responses based on the time of data submission. Specific criteria and acquisition methods for the time of data submission include, for example, the submission date and time or the time of event occurrence. Specific criteria and methods for determining the priority of responses include, for example, a priority score based on the time of submission. For example, the response unit can provide a response preferentially based on recently submitted data. Also, the response unit can provide a response later based on older data. Furthermore, the response unit can provide a response preferentially based on data related to a specific event. In this way, by determining the priority of responses based on the time of data submission, more appropriate responses can be provided.

[0087] The response unit can adjust the order of responses based on the relevance of the data. For example, when responding, the response unit adjusts the order of responses based on the relevance of the data. Specific evaluation criteria and methods for the relevance of the data include, for example, a relevance score and a co-occurrence relationship. Specific adjustment methods and criteria for the order of responses include, for example, ordering based on relevance. For example, the response unit can provide a response first based on the parent's last message. Also, the response unit can provide a response later based on daily interactions. Furthermore, the response unit can provide a response preferentially based on data related to a specific event. This allows for providing a more appropriate response by adjusting the order of responses based on the relevance of the data. === Hard Collateral 1-1 === Each of the multiple elements, including the storage unit, generation unit, provision unit, and response unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the storage unit is realized by the specific processing unit 290 of the data processing device 12 and stores data of parents' memories in the cloud. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the stored data and generates an automatic response. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and sets up an account as a virtual personality on an SNS and responds 24 hours a day. The response unit is realized, for example, by the control unit 46A of the smart device 14 and has a speaker installed in a photo frame that responds when spoken to. === Hard Collateral 1-2 === Each of the multiple elements, including the storage unit, generation unit, provision unit, and response unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the storage unit is realized by the specific processing unit 290 of the data processing device 12 and stores data of parental memories in the cloud. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the stored data and generates an automatic response. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and sets up an account as a virtual personality on an SNS and responds 24 hours a day. The response unit is realized, for example, by the control unit 46A of the smart glasses 214 and has a speaker installed in the photo frame that responds when spoken to. === Hard Collateral 1-3 === Each of the multiple elements including the storage unit, generation unit, provision unit, and response unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the storage unit is realized by the specific processing unit 290 of the data processing device 12 and stores data of parents' memories in the cloud. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the stored data and generates an automatic response. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and sets up an account on an SNS as a virtual personality and responds 24 hours a day. The response unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and has a speaker installed in the photo frame that responds when spoken to. === Hard Collateral 1-4 === Each of the multiple elements including the storage unit, generation unit, provision unit, and response unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the storage unit is realized by the specific processing unit 290 of the data processing device 12 and stores data of parental memories in the cloud. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the stored data and generates an automatic response. The provision unit is realized, for example, by the control unit 46A of the robot 414 and sets up an account as a virtual personality on an SNS and responds 24 hours a day. The response unit is realized, for example, by the control unit 46A of the robot 414 and is equipped with a speaker in a photo frame that responds when spoken to.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The generator can estimate the user's emotions and adjust the content of the automatic response based on the estimated user emotions. For example, if the user is sad, the generator can generate words of comfort or a message of encouragement. If the user is happy, the generator can generate a message of sympathy or congratulations. Furthermore, if the user is angry, the generator can generate a message that includes a calm response or a suggestion for solving the problem. This makes it possible to provide an appropriate response according to the user's emotions.

[0090] The providing unit can estimate the user's emotions and adjust the timing of the automatic response to be provided based on the estimated user's emotions. For example, if the user is sad, the automatic response can be delayed until the user calms down. Alternatively, if the user is relaxed, the automatic response can be provided immediately. Furthermore, if the user is busy, the automatic response can be provided according to the user's schedule. In this way, by adjusting the timing of the automatic response based on the user's emotions, the response can be provided at a more appropriate time.

[0091] The storage unit can estimate the user's emotions and adjust the timing of data storage based on the estimated user emotions. For example, if the user is sad, the storage of data can be delayed until the user's emotions calm down. Alternatively, if the user is relaxed, the data can be stored immediately and a notification can be sent at a timing that matches the user's emotions. Furthermore, if the user is busy, the data can be stored in accordance with the user's schedule. In this way, by adjusting the timing of data storage based on the user's emotions, the data can be stored at a more appropriate time.

[0092] The generation unit can estimate the user's emotions and adjust the expression style of the automatic response based on the estimated user's emotions. For example, if the user is sad, the automatic response can be generated using gentle language. If the user is relaxed, the automatic response can be generated using casual language. Furthermore, if the user is excited, the automatic response can be generated using cheerful language. In this way, by adjusting the expression style of the automatic response based on the user's emotions, a more appropriate response can be provided.

[0093] The response unit can estimate the user's emotion and adjust the length of the response based on the estimated user's emotion. For example, if the user is sad, a longer response can be provided to soften the emotion. If the user is relaxed, a shorter response can be provided to provide a casual conversation. Furthermore, if the user is in a hurry, a concise response can be provided to provide quick communication. Thus, by adjusting the length of the response based on the user's emotion, a more appropriate response can be provided.

[0094] The storage unit can select an appropriate storage method depending on the type of parental memory data. For example, it can save photo data at high resolution and tag it. It can also analyze email data and extract and save important parts. It can also scan letter data, save it in PDF format, and convert it into a searchable format. This allows the system to select the optimal storage method depending on the type of parental memory data, making data management more efficient.

[0095] The storage unit can determine the storage priority based on the importance of the data. For example, it can store data of high importance, such as a parent's last message, with priority. It can also store data of daily interactions later. It can also store data related to important events designated by the user with priority. Thus, by determining the storage priority based on the importance of the data, it is possible to store important data with priority.

[0096] The storage unit can preferentially store highly relevant data based on the user's geographical location information. For example, when the user is in a specific location, data of memories related to that location can be preferentially stored. When the user is traveling, data related to the travel destination can be preferentially stored. When the user is at home, data related to the home can be preferentially stored. In this way, by preferentially storing highly relevant data in consideration of the user's geographical location information, more highly relevant data can be stored.

[0097] The storage unit can analyze the user's social media activity and store related data. For example, if the user posts about a specific event on the SNS, data related to the event can be preferentially stored. Also, if the user frequently interacts with a specific person on the SNS, data related to that person can be preferentially stored. Furthermore, if the user posts about a specific place on the SNS, data related to that place can be preferentially stored. In this way, by analyzing the user's social media activity and storing related data, more relevant data can be stored.

[0098] The generator can apply different generation algorithms depending on the data category. For example, a visual response can be generated based on photo data, a text-based response can be generated based on email data, or an emotional response can be generated based on letter data. This allows for more appropriate responses to be provided by applying different generation algorithms depending on the data category.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The storage unit stores the parent's memory data in the cloud. The parent's memory data may include, but is not limited to, photos, videos, and voice messages. The storage unit may store the data using a cloud service such as AWS, Google Cloud, or Azure. Step 2: The generation unit analyzes the data stored by the storage unit and generates an automatic response. The automatic response may be, for example, a text message or a voice response, but is not limited to these examples. The generation unit uses a generation AI to analyze the parent's memory data and generate an appropriate response. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Step 3: The providing unit provides the automatic response generated by the generating unit. For example, the providing unit can set up an account on a social networking site as a virtual personality and respond 24 hours a day. The providing unit can also periodically distribute memorable photos and send messages via the social networking site. Furthermore, the providing unit is equipped with a response unit that is equipped with a speaker in the photo frame and responds when spoken to. The response unit is installed at the bedside or elsewhere so that conversations can be held at any time.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] 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.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] 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.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] 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.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 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 storage unit that stores parental memory data in the cloud; a generating unit that analyzes the data stored by the storing unit and generates an automatic response; a providing unit that provides the automatic response generated by the generating unit. A system characterized by:

2. The providing unit Set up a virtual personality account on social media and respond 24 hours a day The system of claim 1 .

3. The providing unit Regularly share memorable photos and send messages via social media The system of claim 1 .

4. The photo frame is equipped with a speaker and a response unit that responds when you speak to it. The system of claim 1 .

5. The response unit It will be installed at the bedside so that conversations can be made at any time.

5. The system of claim 4.

6. The storage unit is The user's emotions are estimated, and the timing of data storage is adjusted based on the estimated user's emotions. The system of claim 1 .

7. The storage unit is Choose the appropriate storage method depending on the type of parent's memories data The system of claim 1 .

8. The storage unit is Prioritize storage based on data importance The system of claim 1 .

9. The storage unit is The user's emotions are estimated, and the priority of data to be stored is determined based on the estimated user's emotions. The system of claim 1 .

10. The storage unit is Prioritize storage of relevant data based on the user's geographic location The system of claim 1 .

11. The storage unit is Analyze your social media activity and store related data The system of claim 1 .

Images