System

Abstract

An object of a system according to an embodiment is to efficiently record contents spoken by an elderly person and share the contents with family members and friends.SOLUTION: A system includes a voice recognition unit, a generation unit, a storage unit, and a sharing unit. The voice recognition unit converts the content spoken by the elderly person into text using a voice recognition technique. The generation unit organizes the text converted by the voice recognition unit. The storage unit stores the text arranged by the generation unit as a digital album. The sharing unit shares the digital album stored by the storage unit with family members or friends.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently record what elderly people say and share it with family and friends.

[0005] The system according to the embodiment aims to efficiently record what elderly people say and share it with their family and friends. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice recognition unit, a generation unit, a storage unit, and a sharing unit. The voice recognition unit converts what the elderly person speaks into text using voice recognition technology. The generation unit organizes the text converted by the voice recognition unit. The storage unit stores the text organized by the generation unit as a digital album. The sharing unit shares the digital album stored by the storage unit with family or friends. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently record what the elderly person says and share it with family and friends. [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 converts an elderly person's spoken memories into text using speech recognition technology, organizes them using generation technology, saves them as a digital album, and shares them with family and friends. For example, the elderly person's spoken memories are converted into text using speech recognition technology. The speech recognition technology converts speech to text with high accuracy and accurately records the content of the conversation. Next, the converted text is organized using generation technology. The generation technology automatically categorizes the content of the conversation and extracts key points. For example, it categorizes places visited and events in travel memories and extracts important episodes. The organized text is saved as a digital album. Photos and videos can also be saved together. For example, adding photos and videos related to travel memories to the album can enrich the recording of memories. Finally, the digital album can be shared with family and friends. The sharing function includes an easy-to-share interface and privacy settings. For example, an album can be set to be shared only with specific family and friends. This allows the system to easily record and share the elderly person's precious memories. For example, by combining voice recognition and generation technologies, it becomes easier to record memories, and sharing them with family and friends spreads the joy of sharing memories.

[0029] The system according to the embodiment includes a speech recognition unit, a generation unit, a storage unit, and a sharing unit. The speech recognition unit converts speech spoken by the elderly person into text using speech recognition technology. For example, the speech recognition unit converts speech spoken by the elderly person into text with high accuracy. The speech recognition unit can convert speech to text using technologies such as deep learning and HMM (Hidden Markov Model). The generation unit organizes the text converted by the speech recognition unit. For example, the generation unit automatically classifies the converted text and extracts key points. The generation unit can classify the text and evaluate its importance using natural language processing technology. The storage unit saves the text organized by the generation unit as a digital album. For example, the storage unit can save the organized text as a digital album, together with photos and videos. The storage unit can save data in formats such as JPEG and MP4. The sharing unit shares the digital album saved by the storage unit with family and friends. For example, the sharing unit includes an interface for easy sharing and privacy settings. The sharing section can be set to share the album only with specific family members and friends, allowing the system according to the embodiment to efficiently record and share memories of the elderly.

[0030] The speech recognition unit can accurately convert what the elderly person speaks into text. For example, the speech recognition unit converts what the elderly person speaks into text with high accuracy. The speech recognition unit can convert speech into text using technologies such as deep learning and HMM (hidden Markov model). The speech recognition unit can analyze the tone and accent of the voice to perform accurate text conversion. For example, the speech recognition unit converts what the elderly person speaks into text in real time and accurately records the content of the speech. The speech recognition unit can learn the voice characteristics of specific speakers to improve the accuracy of speech recognition. For example, the speech recognition unit accumulates voice data of elderly people and learns the voice characteristics of each elderly person to improve the accuracy of speech recognition. This enables accurate text conversion through high-accuracy speech recognition.

[0031] The generation unit can automatically classify the converted text and extract important points. For example, the generation unit automatically classifies the converted text and extracts important points. The generation unit can use natural language processing technology to classify the text and evaluate its importance. For example, the generation unit extracts important points using frequently occurring keywords or context analysis. The generation unit can analyze the content of the text and extract important episodes and events. For example, the generation unit can classify places and events visited in travel memories and extract important episodes. The generation unit can automatically classify the content of the story using a text classification algorithm. For example, the generation unit classifies the content of the story by theme and extracts important points based on each theme. This makes it easier to organize the text by extracting important points.

[0032] The storage unit stores organized text as a digital album, and can store photos or videos together. For example, the storage unit stores organized text as a digital album, and can store photos and videos together. The storage unit can store data in formats such as JPEG and MP4. The storage unit can record richer memories by adding related photos and videos to the digital album. For example, the storage unit adds photos and videos related to travel memories to the album. The storage unit can regularly back up the contents of the digital album to ensure data security. For example, the storage unit can create backups in cloud storage to ensure data security. The storage unit can encrypt the contents of the digital album to protect privacy. For example, the storage unit uses encryption technology to securely store the contents of the digital album. This allows richer memories to be recorded by storing photos and videos together.

[0033] The sharing section may include an easy-to-share interface or privacy settings. The sharing section may include, for example, an easy-to-share interface and privacy settings. The sharing section may be set to share the album only with specific family and friends. The sharing section may share the contents of the digital album in conjunction with social media. For example, the sharing section may widely share the contents of the digital album using a social media platform. The sharing section may encrypt the contents of the digital album to protect privacy. For example, the sharing section may securely share the contents of the digital album using encryption technology. The sharing section may provide a preview function for the digital album when sharing, allowing the contents to be checked. For example, the sharing section may provide a function for previewing and editing the contents of the digital album before sharing. As a result, the easy-to-share interface and privacy settings allow sharing with peace of mind.

[0034] The speech recognition unit can dynamically change the speech recognition algorithm depending on the speaking speed and accent of the elderly person. For example, if the elderly person speaks slowly, the speech recognition unit switches the speech recognition algorithm to a slow mode to perform accurate text conversion. If the elderly person speaks quickly, the speech recognition unit can switch the speech recognition algorithm to a fast mode to perform text conversion in real time. The speech recognition unit can adapt the speech recognition algorithm to regional pronunciation depending on the elderly person's accent. For example, the speech recognition unit analyzes the elderly person's speaking speed and accent and dynamically changes the speech recognition algorithm. The speech recognition unit can optimize the speech recognition algorithm using real-time parameter adjustment and adaptive learning. For example, the speech recognition unit adjusts the parameters of the speech recognition algorithm in real time based on the elderly person's speaking speed and accent. This enables accurate text conversion by changing the algorithm depending on the speaking speed and accent.

[0035] The speech recognition unit can learn from the elderly's past speech data and generate an individual speech recognition model. For example, the speech recognition unit collects speech data that the elderly has spoken in the past and generates an individual speech recognition model. The speech recognition unit can learn the elderly's speaking patterns and characteristics and optimize the individual speech recognition model. The speech recognition unit builds an individual speech recognition model using past conversation data and speech samples. For example, the speech recognition unit generates an individual speech recognition model that recognizes specific phrases and expressions based on the elderly's past speech data. The speech recognition unit can build a speech recognition model that corresponds to the elderly's specific speaking style using personalized models or custom training. For example, the speech recognition unit learns from the elderly's past speech data and generates an individual speech recognition model. As a result, the individual speech recognition model enables recognition that corresponds to the elderly's specific speaking style.

[0036] The voice recognition unit can recognize speech by emphasizing specific keywords according to the content of speech by the elderly person. For example, the voice recognition unit recognizes speech by emphasizing specific keywords (e.g., places or people's names) based on the content of speech by the elderly person. The voice recognition unit can analyze the content of speech and extract important keywords using natural language processing technology. The voice recognition unit can recognize speech by emphasizing keywords related to a specific theme (e.g., travel or family). For example, the voice recognition unit recognizes speech by emphasizing important episodes or events based on the content of speech by the elderly person. The voice recognition unit can accurately record important information using keyword selection criteria and emphasis methods. For example, the voice recognition unit recognizes speech by emphasizing specific keywords based on the content of speech and accurately records important information. In this way, important information can be accurately recorded by recognizing speech by emphasizing specific keywords.

[0037] The generation unit can automatically divide the generated text into chapters based on what the elderly person speaks. For example, the generation unit automatically divides chapters into important episodes based on what the elderly person speaks. The generation unit can use natural language processing technology to analyze the content of the text and generate chapter titles. The generation unit can automatically divide the content of the story into chapters using content segmentation. For example, the generation unit automatically divides chapters into places visited or events based on what the elderly person speaks. The generation unit can automatically divide chapters into specific themes (for example, travel or family). For example, the generation unit automatically divides chapters into important episodes or events based on what the elderly person speaks. This makes it easier to organize the text through automatic chapter division.

[0038] The generation unit can automatically add relevant citations and references to the generated text. For example, the generation unit automatically adds relevant citations based on what the elderly person speaks. The generation unit can add citations according to a citation format using a reliable source. The generation unit can automatically add relevant references. For example, the generation unit automatically adds citations and references related to a specific topic based on what the elderly person speaks. The generation unit can add reliable information using selection criteria for citations and references. For example, the generation unit automatically adds relevant citations and references based on what the elderly person speaks. This increases the reliability of the text by adding relevant citations and references.

[0039] The generation unit can format the generated text in different styles based on what the elderly person speaks. For example, the generation unit formats the text in a narrative style based on what the elderly person speaks. The generation unit can change the style of the text by changing the font or adjusting the layout. The generation unit can format the text in a report style. For example, the generation unit formats the text in a particular style (e.g., essay style) based on what the elderly person speaks. The generation unit can format the text in different styles using an algorithm for changing the style of the text. For example, the generation unit formats the text in a narrative style or a report style based on what the elderly person speaks. This enables diverse expression of the text by formatting in different styles.

[0040] The storage unit can automatically back up data when saving the data, thereby ensuring the safety of the data. For example, the storage unit can automatically create a backup when saving the data, thereby ensuring the safety of the data. The storage unit can create a backup in cloud storage, thereby ensuring the safety of the data. The storage unit can create a backup in external storage, thereby ensuring the safety of the data. For example, the storage unit can automatically create a backup when saving the data, thereby ensuring the safety of the data. The storage unit can set a backup schedule and periodically update the backup. For example, the storage unit can automatically back up data, thereby ensuring the safety of the data. This improves the safety of the data through automatic backups.

[0041] The storage unit can periodically perform maintenance on the stored digital albums to maintain data consistency. The storage unit, for example, periodically checks the consistency of data on the stored digital albums and performs maintenance. The storage unit can detect and correct data duplication and errors. The storage unit can periodically update backups to maintain data consistency. For example, the storage unit periodically checks the consistency of data on the stored digital albums and performs maintenance. The storage unit can check error logs and check data consistency. For example, the storage unit periodically performs maintenance on the stored digital albums to maintain data consistency. In this way, data consistency is maintained through regular maintenance.

[0042] The storage unit can automatically add metadata to the stored digital album. For example, the storage unit can automatically add tags to the stored digital album based on the content of the conversation. The storage unit can automatically add metadata (for example, tags and comments). The storage unit can add metadata based on specific keywords. For example, the storage unit can automatically add comments to the stored digital album based on the content of the conversation. The storage unit can improve the searchability of the digital album by using the type and addition method of metadata. For example, the storage unit can automatically add metadata to the stored digital album based on specific keywords. In this way, the addition of metadata improves the searchability of the digital album.

[0043] The sharing unit may provide a preview function of the digital album at the time of sharing, allowing the user to check the content. For example, the sharing unit may provide a preview function of the digital album at the time of sharing, allowing the user to check the content. The sharing unit may provide a preview function of a specific page or section. The sharing unit may use the preview function to enable editing of the content before sharing. For example, the sharing unit may provide a preview function of the digital album at the time of sharing, allowing the user to check the content. The sharing unit may use the preview format or display content to enable the user to check the content of the digital album. For example, the sharing unit may provide a preview function of a specific page or section at the time of sharing, allowing the user to check the content. Thus, the preview function allows the user to check the content before sharing.

[0044] The sharing unit can provide a function to select and share only a portion of a digital album when sharing. For example, the sharing unit provides a function to select and share only a specific page or section of a digital album when sharing. The sharing unit can select and share only a specific episode or event. The sharing unit can select and share only specific photos or videos. For example, the sharing unit provides a function to select and share only a specific page or section of a digital album when sharing. The sharing unit can share specific content by setting a sharing range or using selection criteria. For example, the sharing unit can select and share only a specific episode or event of a digital album when sharing. This allows specific content to be shared by selecting and sharing only a portion.

[0045] The sharing unit can collect feedback from the sharing recipients when sharing and use the feedback to improve the digital album. For example, the sharing unit can collect feedback from the sharing recipients when sharing and use the feedback to improve the content of the digital album. The sharing unit can collect feedback using a questionnaire format or an evaluation score. The sharing unit can improve the layout and functions of the digital album using a feedback collection method and evaluation criteria. For example, the sharing unit can collect feedback from the sharing recipients when sharing and use the feedback to improve the content of the digital album. The sharing unit can improve the layout of the digital album based on the feedback. The sharing unit can improve the functions of the digital album based on the feedback. For example, the sharing unit can collect feedback from the sharing recipients when sharing and use the feedback to improve the content and functions of the digital album. In this way, the content and functions of the digital album can be improved by collecting feedback.

[0046] The sharing unit records an access log for the digital album when sharing, and is able to grasp the sharing status. For example, the sharing unit records an access log for the digital album when sharing, and is able to grasp the sharing status. The sharing unit can record the access date and time and access source information. The sharing unit can record an access log for a specific page or section. For example, the sharing unit records an access log for the digital album when sharing, and is able to grasp the sharing status. The sharing unit can grasp the viewing status of the sharing partner using the recording method and content of the access log. For example, the sharing unit records an access log for a specific page or section when sharing, and is able to grasp the sharing status. In this way, the sharing status can be grasped by recording the access log.

[0047] The sharing unit can encrypt the contents of the digital album when it is shared, thereby protecting privacy. For example, the sharing unit can encrypt the contents of the digital album when it is shared, thereby protecting privacy. The sharing unit can securely share the contents of the digital album by using an encryption algorithm or a key management method. The sharing unit can encrypt the contents of a specific page or section. For example, the sharing unit can encrypt the contents of the digital album when it is shared, thereby protecting privacy. The sharing unit can securely share the contents of the digital album by using encryption technology. The sharing unit can protect the contents of the digital album by using an encryption method or standard. For example, the sharing unit can encrypt the contents of a specific page or section when it is shared, thereby protecting privacy. In this way, the encryption allows the contents of the digital album to be shared securely.

[0048] The sharing unit can share the content of the digital album in cooperation with social media when sharing. For example, the sharing unit can share the content of the digital album in cooperation with social media when sharing. The sharing unit can widely share the content of the digital album by using an API or the type of social media to be linked. The sharing unit can share the content of a specific page or section in cooperation with social media when sharing. For example, the sharing unit can share the content of the digital album in cooperation with social media when sharing. The sharing unit can widely share the content of the digital album using a social media platform. The sharing unit can share the content of the digital album using a method or standard for linking with social media. For example, the sharing unit can share the content of a specific page or section in cooperation with social media when sharing. In this way, the content of the digital album can be widely shared by linking with social media.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The voice recognition unit can analyze the tone and speed of the speaker's voice and emphasize specific keywords when recognizing the voice. For example, when the speaker speaks a specific place or the name of a person, the voice recognition unit can emphasize the keyword when recognizing the voice, thereby accurately recording important information. The voice recognition unit can also emphasize keywords related to a specific theme (e.g., travel or family) based on the content of the speaker's speech when recognizing the voice. Furthermore, the voice recognition unit can accurately record important information using keyword selection criteria and emphasis methods. As a result, important information can be accurately recorded by emphasizing specific keywords when recognizing the voice.

[0051] The generator can automatically add relevant citations and references to the generated text. For example, the generator can automatically add relevant citations based on what a speaker says, thereby improving the reliability of the text. The generator can also add citations according to a citation format using a reliable source. The generator can also automatically add relevant references to complement the content of the text. This improves the reliability of the text by adding relevant citations and references.

[0052] The storage unit can periodically perform maintenance on the stored digital album to maintain data integrity. For example, the storage unit can periodically check the data integrity of the stored digital album and perform maintenance. The storage unit can also detect and correct data duplication and errors. Furthermore, the storage unit can periodically update backups to maintain data integrity. As a result, data integrity is maintained through periodic maintenance.

[0053] The sharing unit records an access log for the digital album when sharing, and is able to grasp the sharing status. For example, the sharing unit records an access log for the digital album when sharing, and is able to grasp the access date and time and access source information. The sharing unit also records an access log for a specific page or section, and is able to grasp the viewing status of the sharing recipient. Furthermore, the sharing unit can grasp the viewing status of the sharing recipient in detail using the recording method and content of the access log. In this way, the sharing status can be grasped by recording the access log.

[0054] The sharing unit can share the contents of the digital album in cooperation with social media when sharing. For example, the sharing unit can widely share the contents of the digital album using a social media platform when sharing. The sharing unit can also share the contents of a specific page or section in cooperation with social media by using an API or the type of social media to be linked. Furthermore, the sharing unit can securely share the contents of the digital album using a method or standard for linking with social media. This allows the contents of the digital album to be widely shared through linking with social media.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The speech recognition unit converts what the elderly person is saying into text using speech recognition technology. The speech recognition unit converts speech into text with high accuracy using technologies such as deep learning and HMM (hidden Markov model). Step 2: The generator organizes the text converted by the speech recognizer. The generator uses natural language processing techniques to automatically categorize the converted text and extract key points. Step 3: The storage unit saves the text organized by the generation unit as a digital album. The storage unit saves the organized text in formats such as JPEG or MP4, and can also save photos and videos together. Step 4: The sharing section allows users to share the digital album stored by the storage section with family and friends. The sharing section includes an easy-to-share interface and privacy settings, allowing users to set the album to be shared only with specific family and friends.

[0057] (Example 2) A system according to an embodiment of the present invention converts an elderly person's spoken memories into text using speech recognition technology, organizes them using generation technology, saves them as a digital album, and shares them with family and friends. For example, the elderly person's spoken memories are converted into text using speech recognition technology. The speech recognition technology converts speech to text with high accuracy and accurately records the content of the conversation. Next, the converted text is organized using generation technology. The generation technology automatically categorizes the content of the conversation and extracts key points. For example, it categorizes places visited and events in travel memories and extracts important episodes. The organized text is saved as a digital album. Photos and videos can also be saved together. For example, adding photos and videos related to travel memories to the album can enrich the recording of memories. Finally, the digital album can be shared with family and friends. The sharing function includes an easy-to-share interface and privacy settings. For example, an album can be set to be shared only with specific family and friends. This allows the system to easily record and share the elderly person's precious memories. For example, by combining voice recognition and generation technologies, it becomes easier to record memories, and sharing them with family and friends spreads the joy of sharing memories.

[0058] The system according to the embodiment includes a speech recognition unit, a generation unit, a storage unit, and a sharing unit. The speech recognition unit converts speech spoken by the elderly person into text using speech recognition technology. For example, the speech recognition unit converts speech spoken by the elderly person into text with high accuracy. The speech recognition unit can convert speech to text using technologies such as deep learning and HMM (Hidden Markov Model). The generation unit organizes the text converted by the speech recognition unit. For example, the generation unit automatically classifies the converted text and extracts key points. The generation unit can classify the text and evaluate its importance using natural language processing technology. The storage unit saves the text organized by the generation unit as a digital album. For example, the storage unit can save the organized text as a digital album, together with photos and videos. The storage unit can save data in formats such as JPEG and MP4. The sharing unit shares the digital album saved by the storage unit with family and friends. For example, the sharing unit includes an interface for easy sharing and privacy settings. The sharing section can be set to share the album only with specific family members and friends, allowing the system according to the embodiment to efficiently record and share memories of the elderly.

[0059] The speech recognition unit can accurately convert what the elderly person speaks into text. For example, the speech recognition unit converts what the elderly person speaks into text with high accuracy. The speech recognition unit can convert speech into text using technologies such as deep learning and HMM (hidden Markov model). The speech recognition unit can analyze the tone and accent of the voice to perform accurate text conversion. For example, the speech recognition unit converts what the elderly person speaks into text in real time and accurately records the content of the speech. The speech recognition unit can learn the voice characteristics of specific speakers to improve the accuracy of speech recognition. For example, the speech recognition unit accumulates voice data of elderly people and learns the voice characteristics of each elderly person to improve the accuracy of speech recognition. This enables accurate text conversion through high-accuracy speech recognition.

[0060] The generation unit can automatically classify the converted text and extract important points. For example, the generation unit automatically classifies the converted text and extracts important points. The generation unit can use natural language processing technology to classify the text and evaluate its importance. For example, the generation unit extracts important points using frequently occurring keywords or context analysis. The generation unit can analyze the content of the text and extract important episodes and events. For example, the generation unit can classify places and events visited in travel memories and extract important episodes. The generation unit can automatically classify the content of the story using a text classification algorithm. For example, the generation unit classifies the content of the story by theme and extracts important points based on each theme. This makes it easier to organize the text by extracting important points.

[0061] The storage unit stores organized text as a digital album, and can store photos or videos together. For example, the storage unit stores organized text as a digital album, and can store photos and videos together. The storage unit can store data in formats such as JPEG and MP4. The storage unit can record richer memories by adding related photos and videos to the digital album. For example, the storage unit adds photos and videos related to travel memories to the album. The storage unit can regularly back up the contents of the digital album to ensure data security. For example, the storage unit can create backups in cloud storage to ensure data security. The storage unit can encrypt the contents of the digital album to protect privacy. For example, the storage unit uses encryption technology to securely store the contents of the digital album. This allows richer memories to be recorded by storing photos and videos together.

[0062] The sharing section may include an easy-to-share interface or privacy settings. The sharing section may include, for example, an easy-to-share interface and privacy settings. The sharing section may be set to share the album only with specific family and friends. The sharing section may share the contents of the digital album in conjunction with social media. For example, the sharing section may widely share the contents of the digital album using a social media platform. The sharing section may encrypt the contents of the digital album to protect privacy. For example, the sharing section may securely share the contents of the digital album using encryption technology. The sharing section may provide a preview function for the digital album when sharing, allowing the contents to be checked. For example, the sharing section may provide a function for previewing and editing the contents of the digital album before sharing. As a result, the easy-to-share interface and privacy settings allow sharing with peace of mind.

[0063] The speech recognition unit can estimate the emotion of the elderly person and adjust the accuracy of speech recognition based on the estimated emotion. For example, if the elderly person is speaking emotionally, the speech recognition unit adjusts the sensitivity of speech recognition according to the intensity of the emotion to perform accurate text conversion. The speech recognition unit can estimate the emotion using voice tone and facial expression analysis. For example, the speech recognition unit analyzes the tone and speed of the elderly person's voice and calculates an emotion score. The speech recognition unit can estimate the emotion of the elderly person in real time using an emotion estimation algorithm. For example, the speech recognition unit monitors the emotion of the elderly person in real time and immediately detects changes in emotion. The speech recognition unit can dynamically adjust the accuracy of speech recognition based on the estimated emotion. For example, if the elderly person is speaking excitedly, the speech recognition unit strengthens the filtering function of speech recognition and removes noise to perform accurate text conversion. As a result, adjusting the accuracy of speech recognition based on emotion enables more accurate text conversion.

[0064] The speech recognition unit can dynamically change the speech recognition algorithm depending on the speaking speed and accent of the elderly person. For example, if the elderly person speaks slowly, the speech recognition unit switches the speech recognition algorithm to a slow mode to perform accurate text conversion. If the elderly person speaks quickly, the speech recognition unit can switch the speech recognition algorithm to a fast mode to perform text conversion in real time. The speech recognition unit can adapt the speech recognition algorithm to regional pronunciation depending on the elderly person's accent. For example, the speech recognition unit analyzes the elderly person's speaking speed and accent and dynamically changes the speech recognition algorithm. The speech recognition unit can optimize the speech recognition algorithm using real-time parameter adjustment and adaptive learning. For example, the speech recognition unit adjusts the parameters of the speech recognition algorithm in real time based on the elderly person's speaking speed and accent. This enables accurate text conversion by changing the algorithm depending on the speaking speed and accent.

[0065] The speech recognition unit can learn from the elderly's past speech data and generate an individual speech recognition model. For example, the speech recognition unit collects speech data that the elderly has spoken in the past and generates an individual speech recognition model. The speech recognition unit can learn the elderly's speaking patterns and characteristics and optimize the individual speech recognition model. The speech recognition unit builds an individual speech recognition model using past conversation data and speech samples. For example, the speech recognition unit generates an individual speech recognition model that recognizes specific phrases and expressions based on the elderly's past speech data. The speech recognition unit can build a speech recognition model that corresponds to the elderly's specific speaking style using personalized models or custom training. For example, the speech recognition unit learns from the elderly's past speech data and generates an individual speech recognition model. As a result, the individual speech recognition model enables recognition that corresponds to the elderly's specific speaking style.

[0066] The voice recognition unit can recognize speech by emphasizing specific keywords according to the content of speech by the elderly person. For example, the voice recognition unit recognizes speech by emphasizing specific keywords (e.g., places or people's names) based on the content of speech by the elderly person. The voice recognition unit can analyze the content of speech and extract important keywords using natural language processing technology. The voice recognition unit can recognize speech by emphasizing keywords related to a specific theme (e.g., travel or family). For example, the voice recognition unit recognizes speech by emphasizing important episodes or events based on the content of speech by the elderly person. The voice recognition unit can accurately record important information using keyword selection criteria and emphasis methods. For example, the voice recognition unit recognizes speech by emphasizing specific keywords based on the content of speech and accurately records important information. In this way, important information can be accurately recorded by recognizing speech by emphasizing specific keywords.

[0067] The generation unit can estimate the emotion of the elderly person and adjust the expression method of the text based on the estimated emotion. For example, when the elderly person is speaking emotionally, the generation unit adjusts the expression method of the text according to the strength of the emotion and generates text that reflects the emotion. The generation unit can estimate the emotion using voice tone and facial expression analysis. For example, the generation unit analyzes the tone and speed of the elderly person's voice and calculates an emotion score. The generation unit can estimate the emotion of the elderly person in real time using an emotion estimation algorithm. For example, the generation unit monitors the emotion of the elderly person in real time and immediately detects changes in emotion. The generation unit can dynamically adjust the expression method of the text based on the estimated emotion. For example, when the elderly person is speaking excitedly, the generation unit generates text using an expression method that emphasizes the emotion. In this way, text that reflects the emotion is generated using an expression method based on the emotion.

[0068] The generation unit can automatically divide the generated text into chapters based on what the elderly person speaks. For example, the generation unit automatically divides chapters into important episodes based on what the elderly person speaks. The generation unit can use natural language processing technology to analyze the content of the text and generate chapter titles. The generation unit can automatically divide the content of the story into chapters using content segmentation. For example, the generation unit automatically divides chapters into places visited or events based on what the elderly person speaks. The generation unit can automatically divide chapters into specific themes (for example, travel or family). For example, the generation unit automatically divides chapters into important episodes or events based on what the elderly person speaks. This makes it easier to organize the text through automatic chapter division.

[0069] The generation unit can automatically add relevant citations and references to the generated text. For example, the generation unit automatically adds relevant citations based on what the elderly person speaks. The generation unit can add citations according to a citation format using a reliable source. The generation unit can automatically add relevant references. For example, the generation unit automatically adds citations and references related to a specific topic based on what the elderly person speaks. The generation unit can add reliable information using selection criteria for citations and references. For example, the generation unit automatically adds relevant citations and references based on what the elderly person speaks. This increases the reliability of the text by adding relevant citations and references.

[0070] The generation unit can format the generated text in different styles based on what the elderly person speaks. For example, the generation unit formats the text in a narrative style based on what the elderly person speaks. The generation unit can change the style of the text by changing the font or adjusting the layout. The generation unit can format the text in a report style. For example, the generation unit formats the text in a particular style (e.g., essay style) based on what the elderly person speaks. The generation unit can format the text in different styles using an algorithm for changing the style of the text. For example, the generation unit formats the text in a narrative style or a report style based on what the elderly person speaks. This enables diverse expression of the text by formatting in different styles.

[0071] The storage unit can estimate the elderly person's emotions and determine the priority of data to be stored based on the estimated emotions. For example, if the elderly person is speaking emotionally, the storage unit determines the priority of data to be stored according to the strength of the emotions. The storage unit can estimate emotions using voice tone and facial expression analysis. For example, the storage unit can analyze the elderly person's tone and speed of voice and calculate an emotion score. The storage unit can estimate the elderly person's emotions in real time using an emotion estimation algorithm. For example, the storage unit can monitor the elderly person's emotions in real time and immediately detect changes in emotions. The storage unit can dynamically determine the priority of data to be stored based on the estimated emotions. For example, if the elderly person is speaking excitedly, the storage unit can preferentially store data that emphasizes emotions. In this way, important data can be preferentially stored by determining the priority of data based on emotions.

[0072] The storage unit can automatically back up data when saving the data, thereby ensuring the safety of the data. For example, the storage unit can automatically create a backup when saving the data, thereby ensuring the safety of the data. The storage unit can create a backup in cloud storage, thereby ensuring the safety of the data. The storage unit can create a backup in external storage, thereby ensuring the safety of the data. For example, the storage unit can automatically create a backup when saving the data, thereby ensuring the safety of the data. The storage unit can set a backup schedule and periodically update the backup. For example, the storage unit can automatically back up data, thereby ensuring the safety of the data. This improves the safety of the data through automatic backups.

[0073] The storage unit can periodically perform maintenance on the stored digital albums to maintain data consistency. The storage unit, for example, periodically checks the consistency of data on the stored digital albums and performs maintenance. The storage unit can detect and correct data duplication and errors. The storage unit can periodically update backups to maintain data consistency. For example, the storage unit periodically checks the consistency of data on the stored digital albums and performs maintenance. The storage unit can check error logs and check data consistency. For example, the storage unit periodically performs maintenance on the stored digital albums to maintain data consistency. In this way, data consistency is maintained through regular maintenance.

[0074] The storage unit can automatically add metadata to the stored digital album. For example, the storage unit can automatically add tags to the stored digital album based on the content of the conversation. The storage unit can automatically add metadata (for example, tags and comments). The storage unit can add metadata based on specific keywords. For example, the storage unit can automatically add comments to the stored digital album based on the content of the conversation. The storage unit can improve the searchability of the digital album by using the type and addition method of metadata. For example, the storage unit can automatically add metadata to the stored digital album based on specific keywords. In this way, the addition of metadata improves the searchability of the digital album.

[0075] The sharing unit can estimate the elderly person's emotions and select a sharing partner based on the estimated emotions. For example, if the elderly person is speaking emotionally, the sharing unit selects a sharing partner based on the strength of the emotion. The sharing unit can estimate emotions using voice tone and facial expression analysis. For example, the sharing unit analyzes the elderly person's tone and speed of voice and calculates an emotion score. The sharing unit can estimate the elderly person's emotions in real time using an emotion estimation algorithm. For example, the sharing unit monitors the elderly person's emotions in real time and instantly detects changes in emotion. The sharing unit can dynamically select a sharing partner based on the estimated emotions. For example, if the elderly person is speaking excitedly, the sharing unit preferentially selects a partner with whom the elderly person wants to share their emotions. This allows sharing with an appropriate partner by selecting a sharing partner based on emotions.

[0076] The sharing unit may provide a preview function of the digital album at the time of sharing, allowing the user to check the content. For example, the sharing unit may provide a preview function of the digital album at the time of sharing, allowing the user to check the content. The sharing unit may provide a preview function of a specific page or section. The sharing unit may use the preview function to enable editing of the content before sharing. For example, the sharing unit may provide a preview function of the digital album at the time of sharing, allowing the user to check the content. The sharing unit may use the preview format or display content to enable the user to check the content of the digital album. For example, the sharing unit may provide a preview function of a specific page or section at the time of sharing, allowing the user to check the content. Thus, the preview function allows the user to check the content before sharing.

[0077] The sharing unit can provide a function to select and share only a portion of a digital album when sharing. For example, the sharing unit provides a function to select and share only a specific page or section of a digital album when sharing. The sharing unit can select and share only a specific episode or event. The sharing unit can select and share only specific photos or videos. For example, the sharing unit provides a function to select and share only a specific page or section of a digital album when sharing. The sharing unit can share specific content by setting a sharing range or using selection criteria. For example, the sharing unit can select and share only a specific episode or event of a digital album when sharing. This allows specific content to be shared by selecting and sharing only a portion.

[0078] The sharing unit can collect feedback from the sharing recipients when sharing and use the feedback to improve the digital album. For example, the sharing unit can collect feedback from the sharing recipients when sharing and use the feedback to improve the content of the digital album. The sharing unit can collect feedback using a questionnaire format or an evaluation score. The sharing unit can improve the layout and functions of the digital album using a feedback collection method and evaluation criteria. For example, the sharing unit can collect feedback from the sharing recipients when sharing and use the feedback to improve the content of the digital album. The sharing unit can improve the layout of the digital album based on the feedback. The sharing unit can improve the functions of the digital album based on the feedback. For example, the sharing unit can collect feedback from the sharing recipients when sharing and use the feedback to improve the content and functions of the digital album. In this way, the content and functions of the digital album can be improved by collecting feedback.

[0079] The sharing unit can estimate the elderly person's emotions and adjust the timing of sharing based on the estimated emotions. For example, if the elderly person is speaking emotionally, the sharing unit adjusts the timing of sharing according to the strength of the emotion. The sharing unit can estimate the emotion using voice tone and facial expression analysis. For example, the sharing unit analyzes the tone and speed of the elderly person's voice and calculates an emotion score. The sharing unit can estimate the elderly person's emotions in real time using an emotion estimation algorithm. For example, the sharing unit monitors the elderly person's emotions in real time and immediately detects changes in emotion. The sharing unit can dynamically adjust the timing of sharing based on the estimated emotions. For example, if the elderly person is speaking excitedly, the sharing unit prioritizes adjusting the timing at which they want to share their emotions. This allows sharing to be done at an appropriate time by adjusting the timing of sharing based on emotions.

[0080] The sharing unit records an access log for the digital album when sharing, and is able to grasp the sharing status. For example, the sharing unit records an access log for the digital album when sharing, and is able to grasp the sharing status. The sharing unit can record the access date and time and access source information. The sharing unit can record an access log for a specific page or section. For example, the sharing unit records an access log for the digital album when sharing, and is able to grasp the sharing status. The sharing unit can grasp the viewing status of the sharing partner using the recording method and content of the access log. For example, the sharing unit records an access log for a specific page or section when sharing, and is able to grasp the sharing status. In this way, the sharing status can be grasped by recording the access log.

[0081] The sharing unit can encrypt the contents of the digital album when it is shared, thereby protecting privacy. For example, the sharing unit can encrypt the contents of the digital album when it is shared, thereby protecting privacy. The sharing unit can securely share the contents of the digital album by using an encryption algorithm or a key management method. The sharing unit can encrypt the contents of a specific page or section. For example, the sharing unit can encrypt the contents of the digital album when it is shared, thereby protecting privacy. The sharing unit can securely share the contents of the digital album by using encryption technology. The sharing unit can protect the contents of the digital album by using an encryption method or standard. For example, the sharing unit can encrypt the contents of a specific page or section when it is shared, thereby protecting privacy. In this way, the encryption allows the contents of the digital album to be shared securely.

[0082] The sharing unit can share the content of the digital album in cooperation with social media when sharing. For example, the sharing unit can share the content of the digital album in cooperation with social media when sharing. The sharing unit can widely share the content of the digital album by using an API or the type of social media to be linked. The sharing unit can share the content of a specific page or section in cooperation with social media when sharing. For example, the sharing unit can share the content of the digital album in cooperation with social media when sharing. The sharing unit can widely share the content of the digital album using a social media platform. The sharing unit can share the content of the digital album using a method or standard for linking with social media. For example, the sharing unit can share the content of a specific page or section in cooperation with social media when sharing. In this way, the content of the digital album can be widely shared by linking with social media. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned voice recognition unit, generation unit, storage unit, and sharing unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the voice recognition unit can detect what the elderly person is saying using the microphone 38B of the smart device 14 and convert the voice into text using the control unit 46A. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, automatically classifies the converted text and extracts important points. The storage unit, for example, stores organized text and related photos and videos in the storage 32 of the data processing device 12. The sharing unit, implemented, for example, by the control unit 46A of the smart device 14, provides an interface for easy sharing and privacy settings. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned voice recognition unit, generation unit, storage unit, and sharing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the voice recognition unit can detect what the elderly person is saying using the microphone 238 of the smart glasses 214 and convert the voice into text using the control unit 46A. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, automatically classifies the converted text and extracts important points. The storage unit, for example, stores organized text and related photos and videos in the storage 32 of the data processing device 12. The sharing unit, implemented, for example, by the control unit 46A of the smart glasses 214, provides an easy-to-share interface and privacy settings. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned voice recognition unit, generation unit, storage unit, and sharing unit, is implemented, for example, in at least one of the headset-type terminal 314 and the data processing device 12. For example, the voice recognition unit can detect what the elderly person is saying using the microphone 238 of the headset-type terminal 314 and convert the voice into text using the control unit 46A. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and automatically classifies the converted text and extracts important points. The storage unit stores organized text and related photos and videos in the storage 32 of the data processing device 12, for example. The sharing unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and provides an interface for easy sharing and privacy settings. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned voice recognition unit, generation unit, storage unit, and sharing unit, is implemented, for example, in at least one of the robot 414 and the data processing device 12. For example, the voice recognition unit can detect what the elderly person is saying using the microphone 238 of the robot 414 and convert the voice into text using the control unit 46A. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, automatically classifies the converted text and extracts important points. The storage unit, for example, stores organized text and related photos and videos in the storage 32 of the data processing device 12. The sharing unit, implemented, for example, by the control unit 46A of the robot 414, provides an interface for easy sharing and privacy settings.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The speech recognition unit can analyze the tone and speed of the speaker's voice and detect changes in emotion in real time. For example, if the speaker is excited, the speech recognition unit can adjust the sensitivity of speech recognition and remove noise to ensure accurate text conversion. Furthermore, if the speaker is calm, the speech recognition unit can strengthen the filtering function to improve speech recognition accuracy. Furthermore, the speech recognition unit can emphasize and recognize specific keywords based on the speaker's emotion. This allows for more accurate text conversion by adjusting speech recognition based on emotions.

[0085] The generation unit can estimate the speaker's emotion and adjust the text expression method based on the estimated emotion. For example, if the speaker is speaking emotionally, the generation unit can adjust the text expression method according to the strength of the emotion and generate text that reflects the emotion. The generation unit can also emphasize specific episodes or events based on the speaker's emotion and reflect them in the text. Furthermore, the generation unit can estimate the speaker's emotion in real time using an emotion estimation algorithm and dynamically adjust the text expression method according to changes in emotion. In this way, text that reflects emotion is generated using an emotion-based expression method.

[0086] The storage unit can estimate the emotion of the speaker to determine the priority of data to be stored. For example, if the speaker is speaking emotionally, the storage unit can determine the priority of data to be stored according to the strength of the emotion, and prioritize storing important data. The storage unit can also estimate the emotion of the speaker in real time using an emotion estimation algorithm and dynamically determine the priority of data to be stored according to changes in emotion. Furthermore, the storage unit can emphasize and store specific episodes or events based on the emotion. This allows important data to be prioritized by determining the priority of data based on the emotion.

[0087] The sharing unit can estimate the speaker's emotions in order to select sharing partners. For example, if the speaker is speaking emotionally, the sharing unit can select sharing partners according to the strength of the emotions and share with appropriate partners. The sharing unit can also estimate the speaker's emotions in real time using an emotion estimation algorithm and dynamically select sharing partners according to changes in emotions. Furthermore, the sharing unit can emphasize and share specific episodes or events based on emotions. This allows sharing with appropriate partners by selecting sharing partners based on emotions.

[0088] The sharing unit can estimate the emotion of the speaker in order to adjust the timing of sharing. For example, if the speaker is speaking emotionally, the timing of sharing can be adjusted according to the intensity of the emotion, allowing sharing at an appropriate time. The sharing unit can also estimate the emotion of the speaker in real time using an emotion estimation algorithm and dynamically adjust the timing of sharing according to changes in emotion. Furthermore, the sharing unit can emphasize and share specific episodes or events based on the emotion. This allows sharing at an appropriate time by adjusting the timing of sharing based on the emotion.

[0089] The voice recognition unit can analyze the tone and speed of the speaker's voice and emphasize specific keywords when recognizing the voice. For example, when the speaker speaks a specific place or the name of a person, the voice recognition unit can emphasize the keyword when recognizing the voice, thereby accurately recording important information. The voice recognition unit can also emphasize keywords related to a specific theme (e.g., travel or family) based on the content of the speaker's speech when recognizing the voice. Furthermore, the voice recognition unit can accurately record important information using keyword selection criteria and emphasis methods. As a result, important information can be accurately recorded by emphasizing specific keywords when recognizing the voice.

[0090] The generator can automatically add relevant citations and references to the generated text. For example, the generator can automatically add relevant citations based on what a speaker says, thereby improving the reliability of the text. The generator can also add citations according to a citation format using a reliable source. The generator can also automatically add relevant references to complement the content of the text. This improves the reliability of the text by adding relevant citations and references.

[0091] The storage unit can periodically perform maintenance on the stored digital album to maintain data integrity. For example, the storage unit can periodically check the data integrity of the stored digital album and perform maintenance. The storage unit can also detect and correct data duplication and errors. Furthermore, the storage unit can periodically update backups to maintain data integrity. As a result, data integrity is maintained through periodic maintenance.

[0092] The sharing unit records an access log for the digital album when sharing, and is able to grasp the sharing status. For example, the sharing unit records an access log for the digital album when sharing, and is able to grasp the access date and time and access source information. The sharing unit also records an access log for a specific page or section, and is able to grasp the viewing status of the sharing recipient. Furthermore, the sharing unit can grasp the viewing status of the sharing recipient in detail using the recording method and content of the access log. In this way, the sharing status can be grasped by recording the access log.

[0093] The sharing unit can share the contents of the digital album in cooperation with social media when sharing. For example, the sharing unit can widely share the contents of the digital album using a social media platform when sharing. The sharing unit can also share the contents of a specific page or section in cooperation with social media by using an API or the type of social media to be linked. Furthermore, the sharing unit can securely share the contents of the digital album using a method or standard for linking with social media. This allows the contents of the digital album to be widely shared through linking with social media.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The speech recognition unit converts what the elderly person is saying into text using speech recognition technology. The speech recognition unit converts speech into text with high accuracy using technologies such as deep learning and HMM (hidden Markov model). Step 2: The generator organizes the text converted by the speech recognizer. The generator uses natural language processing techniques to automatically categorize the converted text and extract key points. Step 3: The storage unit saves the text organized by the generation unit as a digital album. The storage unit saves the organized text in formats such as JPEG or MP4, and can also save photos and videos together. Step 4: The sharing section allows users to share the digital album stored by the storage section with family and friends. The sharing section includes an easy-to-share interface and privacy settings, allowing users to set the album to be shared only with specific family and friends.

[0096] 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.

[0097] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0135] 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.

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

[0137] 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).

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

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] [Explanation of symbols]

[0168] 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 system comprising: a voice recognition unit that converts what the elderly person speaks into text using voice recognition technology; a generation unit that organizes the text converted by the voice recognition unit; a storage unit that saves the text organized by the generation unit as a digital album; and a sharing unit that shares the digital album saved by the storage unit with family or friends.

2. The system of claim 1, wherein the speech recognition unit accurately converts what the elderly person speaks into text.

3. The generation unit Automatically classify converted text and extract key points The system of claim 1 .

4. The system of claim 1 , wherein the storage unit stores the organized text as a digital album together with photos or videos.

5. The system of claim 1 , wherein the sharing portion includes an easy sharing interface or privacy settings.

6. The voice recognition unit Estimate the emotions of elderly people and adjust the accuracy of speech recognition based on the estimated emotions. The system of claim 1 .

7. The voice recognition unit Dynamically change speech recognition algorithms based on the speaking speed and accent of the elderly person The system of claim 1 .

8. The voice recognition unit Generate personalized speech recognition models by learning from the elderly's past speech data The system of claim 1 .

9. The voice recognition unit Emphasizes and recognizes specific keywords depending on what the elderly person is saying The system of claim 1 .

10. The generation unit Estimate the emotions of elderly people and adjust the way text is expressed based on the estimated emotions. The system of claim 1 .

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