system

Abstract

The system according to the embodiment aims to extract text from an image, play it aloud, and provide appropriate answers to questions from the user. [Solution] A system according to an embodiment includes an image acquisition unit, an analysis unit, a voice synthesis unit, a playback unit, a question reception unit, an answer generation unit, and an answer provision unit. The image acquisition unit acquires an image. The analysis unit analyzes the image acquired by the image acquisition unit and converts it into text data. The voice synthesis unit converts the text data converted by the analysis unit into voice. The playback unit plays back the voice generated by the voice synthesis unit. The question reception unit accepts questions from users. The answer generation unit generates answers to questions accepted by the question reception unit. The answer provision unit provides the answers generated by the answer generation unit by voice.

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 the problem that the process of extracting text from an image and reproducing it as audio is complicated, making it difficult to provide appropriate answers to user questions.

[0005] The system according to the embodiment aims to extract text from an image, play it aloud, and provide appropriate answers to questions from the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, an analysis unit, a voice synthesis unit, a playback unit, a question reception unit, an answer generation unit, and an answer providing unit. The image acquisition unit acquires an image. The analysis unit analyzes the image acquired by the image acquisition unit and converts it into text data. The voice synthesis unit converts the text data converted by the analysis unit into voice. The playback unit plays back the voice generated by the voice synthesis unit. The question reception unit accepts questions from users. The answer generation unit generates answers to the questions accepted by the question reception unit. The answer providing unit provides the answers generated by the answer generation unit by voice. [Effects of the Invention]

[0007] The system according to the embodiment can extract text from an image, play it aloud, and provide appropriate answers to user questions. [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 scans a book as an image and plays it back using voice synthesis. In this system, a user scans a page of a book as an image, and a generation AI analyzes the image and converts it into text data. The converted text data is then played back as audio using voice synthesis technology. The system also includes a generation AI, enabling it to converse with the user. For example, when a child asks, "Why?", the generation AI can respond via voice. Furthermore, the generation AI can also analyze the text content to "consider what the protagonist was thinking," "generalize," and "summarize the content," allowing users to learn how to interpret the text. First, a user scans a page of a book as an image. To do this, the user photographs the page using a smartphone or tablet camera. For example, if a child photographs a page of a picture book, the image is input into the system. Next, the generation AI analyzes the image and converts it into text data. The generation AI recognizes the characters in the image and extracts them as text data. For example, it analyzes the characters written on the page of a picture book and acquires them as text data. The converted text data is then played back as audio using voice synthesis technology. The generation AI converts text data into speech and reads it aloud to the user. For example, it can read the contents of a picture book aloud to a child. Furthermore, the system is equipped with a generation AI and has the ability to converse with the user. For example, if a child asks, "Why?", the generation AI will respond verbally. For example, in response to the question, "Why is the sky blue?", the generation AI might reply, "The sky is blue because sunlight is scattered by molecules in the atmosphere." The generation AI can also "consider what the protagonist thought," "generalize," and "summarize the content" from the text. For example, it can consider how the protagonist of a story felt and provide those thoughts to the user. This allows users to learn how to consider the text. This system makes reading fun for children and provides immediate answers to their questions. Learning to consider and summarize texts also improves reading comprehension and thinking skills.This allows the system to read the book as an image, play it using speech synthesis, and provide spoken answers to the user's questions.

[0029] The system according to the embodiment includes an image acquisition unit, an analysis unit, a voice synthesis unit, a playback unit, a question reception unit, an answer generation unit, and an answer provision unit. The image acquisition unit allows a user to read a page of a book as an image. When a user reads a page of a book as an image, the user can use the camera of a smartphone or tablet. For example, when a child takes a picture of a page of a picture book, the image is input into the system. The analysis unit uses a generation AI to analyze the image acquired by the image acquisition unit and convert it into text data. For example, the analysis unit recognizes characters in the image and extracts them as text data. The generation AI analyzes the characters in the image and acquires them as text data. The voice synthesis unit converts the text data converted by the analysis unit into voice. For example, the voice synthesis unit converts the text data into voice using a generation AI and reads it out to the user. The playback unit plays back the voice generated by the voice synthesis unit. The playback unit plays back the voice using, for example, a speaker or headphones. The question reception unit accepts questions from the user. The question receiving unit receives questions from users using, for example, voice input or text input. The answer generation unit generates answers to questions received by the question receiving unit using a generation AI. The answer generation unit generates, for example, appropriate answers to user questions. The generation AI generates appropriate answers to user questions. The answer providing unit provides the answers generated by the answer generation unit by voice. The answer providing unit provides, for example, the answers generated using the generation AI by voice. As a result, the system according to the embodiment can read a book as an image, play it using voice synthesis, and provide answers to user questions by voice.

[0030] The analysis unit can recognize characters in an image and extract them as text data. The analysis unit can recognize characters in an image using, for example, OCR technology and extract them as text data. OCR technology can recognize characters in an image with high accuracy and convert them into text data. For example, the analysis unit can analyze characters written on the pages of a picture book and acquire them as text data. The analysis unit can also recognize characters in an image using generation AI and extract them as text data. The generation AI analyzes characters in an image and acquires them as text data. This makes it possible to accurately recognize characters in an image and extract them as text data.

[0031] The answer generation unit can generate an appropriate answer to a user's question. The answer generation unit generates an appropriate answer to a user's question using, for example, a generation AI. The generation AI uses natural language processing technology to generate an appropriate answer to a user's question. For example, the generation AI analyzes the user's question and generates an appropriate answer. The answer generation unit can also generate an appropriate answer to a user's question using the generation AI. In order to generate an appropriate answer to a user's question, the generation AI analyzes the content of the question and generates an appropriate answer. In this way, an appropriate answer can be generated to a user's question.

[0032] The answer providing unit can provide the generated answer by voice. The answer providing unit, for example, provides an answer generated using a generation AI by voice. The generation AI converts the generated answer into voice and provides it to the user. For example, the generation AI provides an answer to a user's question by voice. The answer providing unit can also provide an answer generated using the generation AI by voice. The generation AI converts the generated answer into voice and provides it to the user. In this way, the generated answer can be provided by voice.

[0033] The analysis unit can generate "reflections on what the protagonist thought," "general comments," and "summaries of the content" from the content of the text. For example, the analysis unit uses generation AI to generate "reflections on what the protagonist thought," "general comments," and "summaries of the content" from the content of the text. The generation AI analyzes the content of the text, considers what the protagonist thought, and provides those considerations to the user. For example, the generation AI considers how the protagonist of a story felt and provides those considerations to the user. The analysis unit can also use generation AI to generate "reflections on what the protagonist thought," "general comments," and "summaries of the content" from the content of the text. The generation AI analyzes the content of the text and generates general comments and summaries. This makes it possible to generate considerations, general comments, and summaries from the content of the text.

[0034] Furthermore, the system includes an image acquisition unit that analyzes the user's past reading history and selects the optimal acquisition method when acquiring images. The image acquisition unit suggests the optimal image acquisition method based on, for example, the types of books the user has read in the past. The image acquisition unit can also preferentially suggest image acquisition methods that the user has used in the past. The image acquisition unit can also select an image acquisition method suitable for a specific genre from the user's reading history. This makes it possible to select the optimal image acquisition method based on the user's past reading history.

[0035] The system further includes an image acquisition unit that performs filtering based on the user's current field of interest when acquiring images. For example, the image acquisition unit preferentially acquires images of books related to the user's current field of interest. The image acquisition unit can also filter and acquire related images based on the user's field of interest. The image acquisition unit can also acquire images related to topics in which the user has expressed interest. This makes it possible to acquire related images based on the user's current field of interest.

[0036] Furthermore, the system includes an image acquisition unit that, when acquiring images, prioritizes acquiring highly relevant images in consideration of the user's geographical location information. The image acquisition unit, for example, prioritizes acquiring images related to the user's current location. The image acquisition unit can also filter and acquire relevant images based on the user's geographical location information. The image acquisition unit can also prioritize acquiring images related to places the user is visiting. This makes it possible to acquire highly relevant images based on the user's geographical location information.

[0037] The system further includes an image acquisition unit that analyzes the user's social media activity and acquires related images when acquiring images. The image acquisition unit acquires related images based on, for example, content shared by the user on social media. The image acquisition unit can also acquire images related to topics of interest from the user's social media activity. The image acquisition unit can also acquire images related to accounts the user follows on social media. This makes it possible to acquire related images based on the user's social media activity.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. For example, the analysis unit performs a detailed analysis on an important image. The analysis unit can also perform a brief analysis on an image with low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the image. This makes it possible to adjust the level of detail of the analysis according to the importance of the image.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the image. For example, the analysis unit can apply a character recognition algorithm to an image with a lot of text. The analysis unit can also apply a diagram analysis algorithm to an image with a lot of diagrams. The analysis unit can also apply an image recognition algorithm to an image with a lot of photographs. This makes it possible to apply an appropriate analysis algorithm depending on the category of the image.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time when the images were acquired. For example, the analysis unit prioritizes analysis of the most recent images. The analysis unit can also postpone analysis of images that were acquired earlier. The analysis unit can also adjust the order of analysis based on the time when the images were acquired. This makes it possible to determine the priority of analysis based on the time when the images were acquired.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. For example, the analysis unit prioritizes analysis of highly relevant images. The analysis unit can also postpone analysis of less relevant images. The analysis unit can also adjust the order of analysis based on the relevance of the images. This makes it possible to adjust the order of analysis based on the relevance of the images.

[0042] The speech synthesis unit can adjust the level of detail of the speech based on the importance of the text during speech synthesis. For example, the speech synthesis unit synthesizes detailed speech for important text. The speech synthesis unit can also synthesize concise speech for text with low importance. The speech synthesis unit can also adjust the level of detail of the speech according to the importance of the text. This makes it possible to adjust the level of detail of the speech according to the importance of the text.

[0043] The speech synthesis unit can apply different speech synthesis algorithms depending on the text category during speech synthesis. For example, the speech synthesis unit can apply an emotional speech synthesis algorithm to narrative text. The speech synthesis unit can also apply a clear and accurate speech synthesis algorithm to technical text. The speech synthesis unit can also apply an easy-to-understand speech synthesis algorithm to educational text. This makes it possible to apply an appropriate speech synthesis algorithm depending on the text category.

[0044] The speech synthesis unit can determine the priority of speech based on the submission date of the text during speech synthesis. For example, the speech synthesis unit prioritizes the most recent text for speech synthesis. It can also postpone older text submissions. Furthermore, the speech synthesis unit can adjust the order of speech synthesis based on the text submission date. This allows for the determination of speech priority based on the text submission date.

[0045] The speech synthesis unit can adjust the order of speech based on the relevance of the text during speech synthesis. For example, the speech synthesis unit prioritizes the synthesis of highly relevant text. It can also postpone the synthesis of less relevant text. Furthermore, the speech synthesis unit can adjust the order of speech synthesis based on the relevance of the text. This allows for the adjustment of the speech order based on the relevance of the text.

[0046] The playback unit can analyze the user's past playback history and select the optimal playback method during playback. For example, the playback unit may prioritize suggesting playback methods that the user has preferred in the past. It can also select the optimal playback method based on the user's playback history. Furthermore, the playback unit can suggest the optimal playback method by referring to playback methods the user has used in the past. This allows the system to select the optimal playback method based on the user's past playback history.

[0047] The playback unit can filter playback based on the user's current areas of interest. For example, the playback unit can prioritize playing audio related to areas the user is currently interested in. The playback unit can also filter and play relevant audio based on the user's areas of interest. Furthermore, the playback unit can play audio related to topics the user has shown interest in. This allows for the playback of relevant audio based on the user's current areas of interest.

[0048] During playback, the playback unit can prioritize playback of highly relevant audio in consideration of the user's geographical location information. For example, the playback unit prioritizes playback of audio related to the user's current location. The playback unit can also filter and play back relevant audio based on the user's geographical location information. The playback unit can also prioritize playback of audio related to places the user is visiting. This makes it possible to play back highly relevant audio based on the user's geographical location information.

[0049] During playback, the playback unit can analyze the user's social media activity and play related audio. For example, the playback unit plays related audio based on content shared by the user on social media. The playback unit can also play audio related to topics of interest from the user's social media activity. The playback unit can also play audio related to accounts the user follows on social media. This makes it possible to play related audio based on the user's social media activity.

[0050] When accepting a question, the question acceptance unit can analyze the user's past question history and select the optimal acceptance method. For example, the question acceptance unit prioritizes accepting questions that the user has frequently asked in the past. The question acceptance unit can also select the optimal acceptance method from the user's question history. The question acceptance unit can also suggest the optimal acceptance method by referring to question methods used by the user in the past. This makes it possible to select the optimal acceptance method based on the user's past question history.

[0051] The question receiving unit can perform filtering based on the user's current field of interest when receiving a question. For example, the question receiving unit preferentially receives questions related to fields in which the user is currently interested. The question receiving unit can also filter and receive related questions based on the user's field of interest. The question receiving unit can also receive questions related to topics in which the user has shown interest. This makes it possible to receive related questions based on the user's current field of interest.

[0052] When accepting a question, the question accepting unit can preferentially accept highly relevant questions in consideration of the user's geographical location information. For example, the question accepting unit preferentially accepts questions related to the user's current location. The question accepting unit can also filter and accept related questions based on the user's geographical location information. The question accepting unit can also preferentially accept questions related to places the user is visiting. This makes it possible to accept highly relevant questions based on the user's geographical location information.

[0053] The question accepting unit can analyze the user's social media activity and accept related questions when accepting a question. For example, the question accepting unit accepts related questions based on content shared by the user on social media. The question accepting unit can also accept questions related to topics of interest from the user's social media activity. The question accepting unit can also accept questions related to accounts the user follows on social media. This makes it possible to accept related questions based on the user's social media activity.

[0054] The answer generation unit can adjust the level of detail of the answer based on the importance of the question when generating the answer. For example, the answer generation unit generates a detailed answer for an important question. The answer generation unit can also generate a concise answer for a question with a low level of importance. The answer generation unit can also adjust the level of detail of the answer depending on the importance of the question. This makes it possible to adjust the level of detail of the answer depending on the importance of the question.

[0055] When generating an answer, the answer generation unit can apply different answer generation algorithms depending on the category of the question. For example, the answer generation unit applies an answer generation algorithm based on accurate data to a scientific question. The answer generation unit can also apply an answer generation algorithm based on historical facts to a historical question. The answer generation unit can also apply an easy-to-understand answer generation algorithm to an educational question. This makes it possible to apply an appropriate answer generation algorithm depending on the category of the question.

[0056] When generating answers, the answer generation unit can determine the priority of answers based on the time of submission of the question. For example, the answer generation unit gives priority to answering the most recent question. The answer generation unit can also postpone questions that were submitted earlier. The answer generation unit can also adjust the order of answers based on the time of submission of the question. This makes it possible to determine the priority of answers based on the time of submission of the question.

[0057] The answer generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the answer generation unit gives priority to answers to highly relevant questions. The answer generation unit can also postpone questions with low relevance. The answer generation unit can also adjust the order of answers based on the relevance of the questions. This makes it possible to adjust the order of answers based on the relevance of the questions.

[0058] When providing an answer, the answer providing unit can analyze the user's past answer history and select the optimal answer providing method. For example, the answer providing unit preferentially suggests methods that the user has preferred in the past. The answer providing unit can also select the optimal answer providing method from the user's answer history. The answer providing unit can also suggest the optimal answer providing method by referring to answer providing methods that the user has used in the past. This makes it possible to select the optimal answer providing method based on the user's past answer history.

[0059] The answer providing unit can filter answers based on the user's current areas of interest when providing answers. For example, the answer providing unit can preferentially provide answers related to areas in which the user is currently interested. The answer providing unit can also filter and provide relevant answers based on the user's areas of interest. The answer providing unit can also provide answers related to topics in which the user has shown interest. This makes it possible to provide relevant answers based on the user's current areas of interest.

[0060] When providing an answer, the answer providing unit can provide highly relevant answers preferentially by taking into consideration the user's geographical location information. For example, the answer providing unit can provide answers preferentially that are related to the user's current location. The answer providing unit can also filter and provide relevant answers based on the user's geographical location information. The answer providing unit can also provide answers preferentially that are related to places the user is visiting. This makes it possible to provide highly relevant answers based on the user's geographical location information.

[0061] When providing an answer, the answer providing unit can analyze the user's social media activity and provide a relevant answer. The answer providing unit can provide a relevant answer based on, for example, content shared by the user on social media. The answer providing unit can also provide answers related to topics of interest from the user's social media activity. The answer providing unit can also provide answers related to accounts the user follows on social media. This makes it possible to provide relevant answers based on the user's social media activity.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The system may also include an advice provider that analyzes the user's past reading history and provides optimal reading advice. For example, the system may recommend new books based on the genre of books the user has previously read. It may also recommend similar books based on the ratings of books the user has previously read. Furthermore, the system may provide in-depth knowledge on specific topics based on the user's reading history. This improves the user's reading experience.

[0064] The system may also include an information providing unit that provides highly relevant information taking into account the user's geographical location information. For example, historical information related to the user's current location may be provided. Also, based on the user's geographical location information, nearby tourist spot and event information may be provided. Furthermore, cultural information related to the place the user is visiting may be provided. In this way, highly relevant information can be provided based on the user's geographical location information.

[0065] The system may also include a social media analytics component that analyzes a user's social media activity and provides relevant information. For example, relevant news and articles may be provided based on the content the user has shared on social media. The system may also provide information related to topics of interest based on the user's social media activity. Furthermore, the system may provide information related to accounts the user follows. This allows the system to provide relevant information based on the user's social media activity.

[0066] The system may also include an answer providing unit that analyzes the user's past question history and provides the most appropriate answer. For example, the system may provide related information based on questions the user has previously asked. The system may also provide in-depth knowledge on a specific topic based on the user's question history. Furthermore, the system may select the most appropriate answering method based on answers the user has previously received. This allows the system to provide the most appropriate answer based on the user's past question history.

[0067] The system may also include a filtering unit that performs filtering based on the user's current areas of interest. For example, information related to the user's current areas of interest may be preferentially provided. Also, related information may be filtered and provided based on the user's areas of interest. Furthermore, information related to topics in which the user has shown interest may be provided. In this way, related information may be provided based on the user's current areas of interest.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The image acquisition unit allows the user to capture a book page as an image. The user can take a photo of the book page using the camera on their smartphone or tablet. For example, if a child takes a photo of a picture book page, the image is input into the system. Step 2: The analysis unit uses the generation AI to analyze the image acquired by the image acquisition unit and convert it into text data. The analysis unit recognizes the characters in the image and extracts them as text data. Step 3: The speech synthesis unit converts the text data converted by the analysis unit into speech. The speech synthesis unit uses a generation AI to convert the text data into speech and read it out to the user. Step 4: The playback unit plays the voice generated by the voice synthesis unit. The playback unit plays the voice using speakers or headphones. Step 5: The question receiving unit receives a question from the user. The question receiving unit receives a question from the user using voice input or text input. Step 6: The answer generation unit uses the generation AI to generate an answer to the question accepted by the question acceptance unit. The answer generation unit generates an appropriate answer to the user's question. Step 7: The answer providing unit provides the answer generated by the answer generating unit by voice. The answer providing unit provides the answer generated by the generation AI by voice.

[0070] (Example 2) A system according to an embodiment of the present invention scans a book as an image and plays it back using voice synthesis. In this system, a user scans a page of a book as an image, and a generation AI analyzes the image and converts it into text data. The converted text data is then played back as audio using voice synthesis technology. The system also includes a generation AI, enabling it to converse with the user. For example, when a child asks, "Why?", the generation AI can respond via voice. Furthermore, the generation AI can also analyze the text content to "consider what the protagonist was thinking," "generalize," and "summarize the content," allowing users to learn how to interpret the text. First, a user scans a page of a book as an image. To do this, the user photographs the page using a smartphone or tablet camera. For example, if a child photographs a page of a picture book, the image is input into the system. Next, the generation AI analyzes the image and converts it into text data. The generation AI recognizes the characters in the image and extracts them as text data. For example, it analyzes the characters written on the page of a picture book and acquires them as text data. The converted text data is then played back as audio using voice synthesis technology. The generation AI converts text data into speech and reads it aloud to the user. For example, it can read the contents of a picture book aloud to a child. Furthermore, the system is equipped with a generation AI and has the ability to converse with the user. For example, if a child asks, "Why?", the generation AI will respond verbally. For example, in response to the question, "Why is the sky blue?", the generation AI might reply, "The sky is blue because sunlight is scattered by molecules in the atmosphere." The generation AI can also "consider what the protagonist thought," "generalize," and "summarize the content" from the text. For example, it can consider how the protagonist of a story felt and provide those thoughts to the user. This allows users to learn how to consider the text. This system makes reading fun for children and provides immediate answers to their questions. Learning to consider and summarize texts also improves reading comprehension and thinking skills.This allows the system to read the book as an image, play it using speech synthesis, and provide spoken answers to the user's questions.

[0071] The system according to the embodiment includes an image acquisition unit, an analysis unit, a voice synthesis unit, a playback unit, a question reception unit, an answer generation unit, and an answer provision unit. The image acquisition unit allows a user to read a page of a book as an image. When a user reads a page of a book as an image, the user can use the camera of a smartphone or tablet. For example, when a child takes a picture of a page of a picture book, the image is input into the system. The analysis unit uses a generation AI to analyze the image acquired by the image acquisition unit and convert it into text data. For example, the analysis unit recognizes characters in the image and extracts them as text data. The generation AI analyzes the characters in the image and acquires them as text data. The voice synthesis unit converts the text data converted by the analysis unit into voice. For example, the voice synthesis unit converts the text data into voice using a generation AI and reads it out to the user. The playback unit plays back the voice generated by the voice synthesis unit. The playback unit plays back the voice using, for example, a speaker or headphones. The question reception unit accepts questions from the user. The question receiving unit receives questions from users using, for example, voice input or text input. The answer generation unit generates answers to questions received by the question receiving unit using a generation AI. The answer generation unit generates, for example, appropriate answers to user questions. The generation AI generates appropriate answers to user questions. The answer providing unit provides the answers generated by the answer generation unit by voice. The answer providing unit provides, for example, the answers generated using the generation AI by voice. As a result, the system according to the embodiment can read a book as an image, play it using voice synthesis, and provide answers to user questions by voice.

[0072] The analysis unit can recognize characters in an image and extract them as text data. The analysis unit can recognize characters in an image using, for example, OCR technology and extract them as text data. OCR technology can recognize characters in an image with high accuracy and convert them into text data. For example, the analysis unit can analyze characters written on the pages of a picture book and acquire them as text data. The analysis unit can also recognize characters in an image using generation AI and extract them as text data. The generation AI analyzes characters in an image and acquires them as text data. This makes it possible to accurately recognize characters in an image and extract them as text data.

[0073] The answer generation unit can generate an appropriate answer to a user's question. The answer generation unit generates an appropriate answer to a user's question using, for example, a generation AI. The generation AI uses natural language processing technology to generate an appropriate answer to a user's question. For example, the generation AI analyzes the user's question and generates an appropriate answer. The answer generation unit can also generate an appropriate answer to a user's question using the generation AI. In order to generate an appropriate answer to a user's question, the generation AI analyzes the content of the question and generates an appropriate answer. In this way, an appropriate answer can be generated to a user's question.

[0074] The answer providing unit can provide the generated answer by voice. The answer providing unit, for example, provides an answer generated using a generation AI by voice. The generation AI converts the generated answer into voice and provides it to the user. For example, the generation AI provides an answer to a user's question by voice. The answer providing unit can also provide an answer generated using the generation AI by voice. The generation AI converts the generated answer into voice and provides it to the user. In this way, the generated answer can be provided by voice.

[0075] The analysis unit can generate "reflections on what the protagonist thought," "general comments," and "summaries of the content" from the content of the text. For example, the analysis unit uses generation AI to generate "reflections on what the protagonist thought," "general comments," and "summaries of the content" from the content of the text. The generation AI analyzes the content of the text, considers what the protagonist thought, and provides those considerations to the user. For example, the generation AI considers how the protagonist of a story felt and provides those considerations to the user. The analysis unit can also use generation AI to generate "reflections on what the protagonist thought," "general comments," and "summaries of the content" from the content of the text. The generation AI analyzes the content of the text and generates general comments and summaries. This makes it possible to generate considerations, general comments, and summaries from the content of the text.

[0076] The system further includes an image acquisition unit that estimates the user's emotion and adjusts the timing of image acquisition based on the estimated user emotion. For example, if the user is excited, the image acquisition unit immediately acquires and starts processing images. Furthermore, if the user is relaxed, the image acquisition unit can also acquire images when the user feels ready. Furthermore, if the user is stressed, the image acquisition unit can provide guidance for relaxation before acquiring images and then acquire images. This allows the timing of image acquisition to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] Furthermore, the system includes an image acquisition unit that analyzes the user's past reading history and selects the optimal acquisition method when acquiring images. The image acquisition unit suggests the optimal image acquisition method based on, for example, the types of books the user has read in the past. The image acquisition unit can also preferentially suggest image acquisition methods that the user has used in the past. The image acquisition unit can also select an image acquisition method suitable for a specific genre from the user's reading history. This makes it possible to select the optimal image acquisition method based on the user's past reading history.

[0078] The system further includes an image acquisition unit that performs filtering based on the user's current field of interest when acquiring images. For example, the image acquisition unit preferentially acquires images of books related to the user's current field of interest. The image acquisition unit can also filter and acquire related images based on the user's field of interest. The image acquisition unit can also acquire images related to topics in which the user has expressed interest. This makes it possible to acquire related images based on the user's current field of interest.

[0079] Furthermore, the system includes an image acquisition unit that estimates the user's emotions and determines the priority of images to acquire based on the estimated emotions. For example, if the user is excited, the image acquisition unit will prioritize acquiring images that are of interest. If the user is relaxed, the image acquisition unit can also prioritize acquiring images with calming content. If the user is stressed, the image acquisition unit can also prioritize acquiring images with a relaxing effect. This allows the system to determine the priority of images to acquire according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] Furthermore, the system includes an image acquisition unit that, when acquiring images, prioritizes acquiring highly relevant images in consideration of the user's geographical location information. The image acquisition unit, for example, prioritizes acquiring images related to the user's current location. The image acquisition unit can also filter and acquire relevant images based on the user's geographical location information. The image acquisition unit can also prioritize acquiring images related to places the user is visiting. This makes it possible to acquire highly relevant images based on the user's geographical location information.

[0081] The system further includes an image acquisition unit that analyzes the user's social media activity and acquires related images when acquiring images. The image acquisition unit acquires related images based on, for example, content shared by the user on social media. The image acquisition unit can also acquire images related to topics of interest from the user's social media activity. The image acquisition unit can also acquire images related to accounts the user follows on social media. This makes it possible to acquire related images based on the user's social media activity.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. If the user is excited, the analysis unit can provide visually appealing analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. For example, the analysis unit performs a detailed analysis on an important image. The analysis unit can also perform a brief analysis on an image with low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the image. This makes it possible to adjust the level of detail of the analysis according to the importance of the image.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the image. For example, the analysis unit can apply a character recognition algorithm to an image with a lot of text. The analysis unit can also apply a diagram analysis algorithm to an image with a lot of diagrams. The analysis unit can also apply an image recognition algorithm to an image with a lot of photographs. This makes it possible to apply an appropriate analysis algorithm depending on the category of the image.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is excited, the analysis unit can also provide a visually appealing analysis. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the time when the images were acquired. For example, the analysis unit prioritizes analysis of the most recent images. The analysis unit can also postpone analysis of images that were acquired earlier. The analysis unit can also adjust the order of analysis based on the time when the images were acquired. This makes it possible to determine the priority of analysis based on the time when the images were acquired.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. For example, the analysis unit prioritizes analysis of highly relevant images. The analysis unit can also postpone analysis of less relevant images. The analysis unit can also adjust the order of analysis based on the relevance of the images. This makes it possible to adjust the order of analysis based on the relevance of the images.

[0088] The speech synthesis unit can estimate the user's emotion and adjust the tone of the voice based on the estimated emotion of the user. For example, if the user is relaxed, the speech synthesis unit synthesizes speech in a calm tone. If the user is excited, the speech synthesis unit can also synthesize speech in a bright tone. If the user is stressed, the speech synthesis unit can also synthesize speech in a calm tone. This makes it possible to adjust the tone of the voice according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The speech synthesis unit can adjust the level of detail of the speech based on the importance of the text during speech synthesis. For example, the speech synthesis unit synthesizes detailed speech for important text. The speech synthesis unit can also synthesize concise speech for text with low importance. The speech synthesis unit can also adjust the level of detail of the speech according to the importance of the text. This makes it possible to adjust the level of detail of the speech according to the importance of the text.

[0090] The speech synthesis unit can apply different speech synthesis algorithms depending on the text category during speech synthesis. For example, the speech synthesis unit can apply an emotional speech synthesis algorithm to narrative text. The speech synthesis unit can also apply a clear and accurate speech synthesis algorithm to technical text. The speech synthesis unit can also apply an easy-to-understand speech synthesis algorithm to educational text. This makes it possible to apply an appropriate speech synthesis algorithm depending on the text category.

[0091] The speech synthesis unit can estimate the user's emotions and adjust the speech speed based on the estimated emotions. For example, if the user is in a hurry, the speech synthesis unit will synthesize speech at a fast speed. Conversely, if the user is relaxed, the speech synthesis unit can synthesize speech at a slow speed. Furthermore, if the user is excited, the speech synthesis unit can synthesize speech at a moderate speed. This allows the speech speed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The speech synthesis unit can determine the priority of speech based on the submission date of the text during speech synthesis. For example, the speech synthesis unit prioritizes the most recent text for speech synthesis. It can also postpone older text submissions. Furthermore, the speech synthesis unit can adjust the order of speech synthesis based on the text submission date. This allows for the determination of speech priority based on the text submission date.

[0093] The speech synthesis unit can adjust the order of speech based on the relevance of the text during speech synthesis. For example, the speech synthesis unit prioritizes the synthesis of highly relevant text. It can also postpone the synthesis of less relevant text. Furthermore, the speech synthesis unit can adjust the order of speech synthesis based on the relevance of the text. This allows for the adjustment of the speech order based on the relevance of the text.

[0094] The playback unit can estimate the user's emotion and adjust the timing of playback based on the estimated user's emotion. For example, if the user is relaxed, the playback unit can start playback at a slower timing. If the user is in a hurry, the playback unit can also start playback quickly. If the user is excited, the playback unit can also start playback at an appropriate timing. This makes it possible to adjust the timing of playback according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] The playback unit can analyze the user's past playback history and select the optimal playback method during playback. For example, the playback unit may prioritize suggesting playback methods that the user has preferred in the past. It can also select the optimal playback method based on the user's playback history. Furthermore, the playback unit can suggest the optimal playback method by referring to playback methods the user has used in the past. This allows the system to select the optimal playback method based on the user's past playback history.

[0096] The playback unit can filter playback based on the user's current areas of interest. For example, the playback unit can prioritize playing audio related to areas the user is currently interested in. The playback unit can also filter and play relevant audio based on the user's areas of interest. Furthermore, the playback unit can play audio related to topics the user has shown interest in. This allows for the playback of relevant audio based on the user's current areas of interest.

[0097] The playback unit can estimate the user's emotions and determine the priority of audio to be played back based on the estimated user's emotions. For example, if the user is excited, the playback unit can prioritize playing back audio that attracts the user's attention. Furthermore, if the user is relaxed, the playback unit can also prioritize playing back audio with calming content. Furthermore, if the user is stressed, the playback unit can also prioritize playing back audio that has a relaxing effect. This makes it possible to determine the priority of audio to be played back according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] During playback, the playback unit can prioritize playback of highly relevant audio in consideration of the user's geographical location information. For example, the playback unit prioritizes playback of audio related to the user's current location. The playback unit can also filter and play back relevant audio based on the user's geographical location information. The playback unit can also prioritize playback of audio related to places the user is visiting. This makes it possible to play back highly relevant audio based on the user's geographical location information.

[0099] During playback, the playback unit can analyze the user's social media activity and play related audio. For example, the playback unit plays related audio based on content shared by the user on social media. The playback unit can also play audio related to topics of interest from the user's social media activity. The playback unit can also play audio related to accounts the user follows on social media. This makes it possible to play related audio based on the user's social media activity.

[0100] The question reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated emotions. For example, if the user is relaxed, the question reception unit will accept questions at a slow pace. If the user is in a hurry, the question reception unit can accept questions quickly. If the user is excited, the question reception unit can accept questions at an appropriate pace. In this way, the timing of question reception can be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] When accepting a question, the question acceptance unit can analyze the user's past question history and select the optimal acceptance method. For example, the question acceptance unit prioritizes accepting questions that the user has frequently asked in the past. The question acceptance unit can also select the optimal acceptance method from the user's question history. The question acceptance unit can also suggest the optimal acceptance method by referring to question methods used by the user in the past. This makes it possible to select the optimal acceptance method based on the user's past question history.

[0102] The question receiving unit can perform filtering based on the user's current field of interest when receiving a question. For example, the question receiving unit preferentially receives questions related to fields in which the user is currently interested. The question receiving unit can also filter and receive related questions based on the user's field of interest. The question receiving unit can also receive questions related to topics in which the user has shown interest. This makes it possible to receive related questions based on the user's current field of interest.

[0103] The question reception unit can estimate the user's emotions and determine the priority of questions to accept based on the estimated emotions. For example, if the user is excited, the question reception unit will prioritize questions that pique their interest. If the user is relaxed, the question reception unit can also prioritize questions with calming content. Furthermore, if the user is stressed, the question reception unit can also prioritize questions that have a relaxing effect. In this way, the priority of questions to accept can be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] When accepting a question, the question accepting unit can preferentially accept highly relevant questions in consideration of the user's geographical location information. For example, the question accepting unit preferentially accepts questions related to the user's current location. The question accepting unit can also filter and accept related questions based on the user's geographical location information. The question accepting unit can also preferentially accept questions related to places the user is visiting. This makes it possible to accept highly relevant questions based on the user's geographical location information.

[0105] The question accepting unit can analyze the user's social media activity and accept related questions when accepting a question. For example, the question accepting unit accepts related questions based on content shared by the user on social media. The question accepting unit can also accept questions related to topics of interest from the user's social media activity. The question accepting unit can also accept questions related to accounts the user follows on social media. This makes it possible to accept related questions based on the user's social media activity.

[0106] The answer generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, the answer generation unit generates a detailed answer when the user is relaxed. The answer generation unit can also generate a concise answer when the user is in a hurry. The answer generation unit can also generate a visually appealing answer when the user is excited. This makes it possible to adjust the way the answer is expressed depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] The answer generation unit can adjust the level of detail of the answer based on the importance of the question when generating the answer. For example, the answer generation unit generates a detailed answer for an important question. The answer generation unit can also generate a concise answer for a question with a low level of importance. The answer generation unit can also adjust the level of detail of the answer depending on the importance of the question. This makes it possible to adjust the level of detail of the answer depending on the importance of the question.

[0108] When generating an answer, the answer generation unit can apply different answer generation algorithms depending on the category of the question. For example, the answer generation unit applies an answer generation algorithm based on accurate data to a scientific question. The answer generation unit can also apply an answer generation algorithm based on historical facts to a historical question. The answer generation unit can also apply an easy-to-understand answer generation algorithm to an educational question. This makes it possible to apply an appropriate answer generation algorithm depending on the category of the question.

[0109] The answer generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user's emotions. For example, if the user is in a hurry, the answer generation unit can generate a short and to-the-point answer. Also, if the user is relaxed, the answer generation unit can generate a detailed answer. Also, if the user is excited, the answer generation unit can generate a visually appealing answer. This allows the length of the answer to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0110] When generating answers, the answer generation unit can determine the priority of answers based on the time of submission of the question. For example, the answer generation unit gives priority to answering the most recent question. The answer generation unit can also postpone questions that were submitted earlier. The answer generation unit can also adjust the order of answers based on the time of submission of the question. This makes it possible to determine the priority of answers based on the time of submission of the question.

[0111] The answer generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the answer generation unit gives priority to answers to highly relevant questions. The answer generation unit can also postpone questions with low relevance. The answer generation unit can also adjust the order of answers based on the relevance of the questions. This makes it possible to adjust the order of answers based on the relevance of the questions.

[0112] The answer providing unit can estimate the user's emotions and adjust the timing of providing the answers based on the estimated user emotions. For example, if the user is relaxed, the answer providing unit can provide the answers at a leisurely pace. Also, if the user is in a hurry, the answer providing unit can provide the answers quickly. Also, if the user is excited, the answer providing unit can provide the answers at an appropriate timing. This makes it possible to adjust the timing of providing the answers according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] When providing an answer, the answer providing unit can analyze the user's past answer history and select the optimal answer providing method. For example, the answer providing unit preferentially suggests methods that the user has preferred in the past. The answer providing unit can also select the optimal answer providing method from the user's answer history. The answer providing unit can also suggest the optimal answer providing method by referring to answer providing methods that the user has used in the past. This makes it possible to select the optimal answer providing method based on the user's past answer history.

[0114] The answer providing unit can filter answers based on the user's current areas of interest when providing answers. For example, the answer providing unit can preferentially provide answers related to areas in which the user is currently interested. The answer providing unit can also filter and provide relevant answers based on the user's areas of interest. The answer providing unit can also provide answers related to topics in which the user has shown interest. This makes it possible to provide relevant answers based on the user's current areas of interest.

[0115] The answer providing unit can estimate the user's emotions and determine the priority of answers to be provided based on the estimated user's emotions. For example, if the user is excited, the answer providing unit can prioritize providing interesting answers. Furthermore, if the user is relaxed, the answer providing unit can prioritize providing calming answers. Furthermore, if the user is stressed, the answer providing unit can prioritize providing answers that have a relaxing effect. This makes it possible to determine the priority of answers to be provided according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0116] When providing an answer, the answer providing unit can provide highly relevant answers preferentially by taking into consideration the user's geographical location information. For example, the answer providing unit can provide answers preferentially that are related to the user's current location. The answer providing unit can also filter and provide relevant answers based on the user's geographical location information. The answer providing unit can also provide answers preferentially that are related to places the user is visiting. This makes it possible to provide highly relevant answers based on the user's geographical location information.

[0117] When providing an answer, the answer providing unit can analyze the user's social media activity and provide a relevant answer. The answer providing unit can provide a relevant answer based on, for example, content shared by the user on social media. The answer providing unit can also provide answers related to topics of interest from the user's social media activity. The answer providing unit can also provide answers related to accounts the user follows on social media. This makes it possible to provide relevant answers based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the image acquisition unit, analysis unit, voice synthesis unit, playback unit, question reception unit, answer generation unit, and answer providing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the image acquisition unit is realized by photographing a page of a book using the camera of the smart device 14. The analysis unit analyzes the image and converts it into text data using the specific processing unit 290 of the data processing device 12. The voice synthesis unit converts the text data into voice using the specific processing unit 290 of the data processing device 12. The playback unit plays the voice using the speaker of the smart device 14. The question reception unit receives a user's question using the microphone of the smart device 14. The answer generation unit generates an answer to the question using the specific processing unit 290 of the data processing device 12. The answer providing unit provides the generated answer by voice using the speaker of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the image acquisition unit, analysis unit, voice synthesis unit, playback unit, question reception unit, answer generation unit, and answer providing unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the image acquisition unit is realized by capturing an image of a book page using the camera of the smart glasses 214. The analysis unit analyzes the image and converts it into text data using the specific processing unit 290 of the data processing device 12. The voice synthesis unit converts the text data into voice using the specific processing unit 290 of the data processing device 12. The playback unit plays the voice using the speaker of the smart glasses 214. The question reception unit receives a user's question using the microphone of the smart glasses 214. The answer generation unit generates an answer to the question using the specific processing unit 290 of the data processing device 12. The answer providing unit provides the generated answer by voice using the speaker of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the image acquisition unit, analysis unit, speech synthesis unit, playback unit, question reception unit, answer generation unit, and answer provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the image acquisition unit is implemented by taking pictures of the book pages using the camera of the headset terminal 314. The analysis unit analyzes the image using the specific processing unit 290 of the data processing unit 12 and converts it into text data. The speech synthesis unit converts the text data into speech using the specific processing unit 290 of the data processing unit 12. The playback unit plays the audio using the speaker of the headset terminal 314. The question reception unit receives the user's questions using the microphone of the headset terminal 314. The answer generation unit generates answers to the questions using the specific processing unit 290 of the data processing unit 12. The answer provision unit provides the generated answers in audio using the speaker of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the image acquisition unit, analysis unit, speech synthesis unit, playback unit, question reception unit, answer generation unit, and answer provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the image acquisition unit is implemented by using the camera of the robot 414 to photograph the pages of a book. The analysis unit analyzes the image using the specific processing unit 290 of the data processing unit 12 and converts it into text data. The speech synthesis unit converts the text data into speech using the specific processing unit 290 of the data processing unit 12. The playback unit plays the speech using the speaker of the robot 414. The question reception unit receives the user's question using the microphone of the robot 414. The answer generation unit generates an answer to the question using the specific processing unit 290 of the data processing unit 12. The answer provision unit provides the generated answer in voice using the speaker of the robot 414.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The system may also include a voice synthesis unit that estimates the user's emotion and adjusts the tone of the voice based on the estimated emotion. For example, if the user is relaxed, the voice synthesis unit synthesizes voice in a calm tone. If the user is excited, the voice synthesis unit may synthesize voice in a bright tone. Furthermore, if the user is stressed, the voice synthesis unit may synthesize voice in a calm tone. In this way, the tone of the voice can be adjusted according to the user's emotion.

[0120] The system may also include an advice provider that analyzes the user's past reading history and provides optimal reading advice. For example, the system may recommend new books based on the genre of books the user has previously read. It may also recommend similar books based on the ratings of books the user has previously read. Furthermore, the system may provide in-depth knowledge on specific topics based on the user's reading history. This improves the user's reading experience.

[0121] The system may also include a question acceptance unit that estimates the user's emotions and adjusts the timing of accepting questions based on the estimated emotions. For example, if the user is relaxed, questions can be accepted at a slower pace. If the user is in a hurry, questions can be accepted quickly. Furthermore, if the user is excited, questions can be accepted at an appropriate time. This makes it possible to adjust the timing of accepting questions according to the user's emotions.

[0122] The system may also include an information providing unit that provides highly relevant information taking into account the user's geographical location information. For example, historical information related to the user's current location may be provided. Also, based on the user's geographical location information, nearby tourist spot and event information may be provided. Furthermore, cultural information related to the place the user is visiting may be provided. In this way, highly relevant information can be provided based on the user's geographical location information.

[0123] The system may also include an analysis unit that estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, detailed analysis results may be provided. If the user is in a hurry, concise analysis results may be provided. Furthermore, if the user is excited, visually appealing analysis results may be provided. In this way, the presentation of the analysis can be adjusted according to the user's emotions.

[0124] The system may also include a social media analytics component that analyzes a user's social media activity and provides relevant information. For example, relevant news and articles may be provided based on the content the user has shared on social media. The system may also provide information related to topics of interest based on the user's social media activity. Furthermore, the system may provide information related to accounts the user follows. This allows the system to provide relevant information based on the user's social media activity.

[0125] The system may also include a playback unit that estimates the user's emotions and adjusts the timing of playback based on the estimated emotions. For example, if the user is relaxed, playback can be started slowly. If the user is in a hurry, playback can be started quickly. Furthermore, if the user is excited, playback can be started at an appropriate timing. In this way, the timing of playback can be adjusted according to the user's emotions.

[0126] The system may also include an answer providing unit that analyzes the user's past question history and provides the most appropriate answer. For example, the system may provide related information based on questions the user has previously asked. The system may also provide in-depth knowledge on a specific topic based on the user's question history. Furthermore, the system may select the most appropriate answering method based on answers the user has previously received. This allows the system to provide the most appropriate answer based on the user's past question history.

[0127] The system may also include an answer providing unit that estimates the user's emotions and determines the priority of answers to be provided based on the estimated emotions. For example, if the user is excited, answers that attract attention may be provided preferentially. Alternatively, if the user is relaxed, answers with calming content may be provided preferentially. Furthermore, if the user is stressed, answers that have a relaxing effect may be provided preferentially. In this way, the priority of answers to be provided can be determined according to the user's emotions.

[0128] The system may also include a filtering unit that performs filtering based on the user's current areas of interest. For example, information related to the user's current areas of interest may be preferentially provided. Also, related information may be filtered and provided based on the user's areas of interest. Furthermore, information related to topics in which the user has shown interest may be provided. In this way, related information may be provided based on the user's current areas of interest.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The image acquisition unit allows the user to capture a book page as an image. The user can take a photo of the book page using the camera on their smartphone or tablet. For example, if a child takes a photo of a picture book page, the image is input into the system. Step 2: The analysis unit uses the generation AI to analyze the image acquired by the image acquisition unit and convert it into text data. The analysis unit recognizes the characters in the image and extracts them as text data. Step 3: The speech synthesis unit converts the text data converted by the analysis unit into speech. The speech synthesis unit uses a generation AI to convert the text data into speech and read it out to the user. Step 4: The playback unit plays the voice generated by the voice synthesis unit. The playback unit plays the voice using speakers or headphones. Step 5: The question receiving unit receives a question from the user. The question receiving unit receives a question from the user using voice input or text input. Step 6: The answer generation unit uses the generation AI to generate an answer to the question accepted by the question acceptance unit. The answer generation unit generates an appropriate answer to the user's question. Step 7: The answer providing unit provides the answer generated by the answer generating unit by voice. The answer providing unit provides the answer generated by the generation AI by voice.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 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. an image acquisition unit that acquires an image; An analysis unit analyzes the image acquired by the image acquisition unit and converts it into text data, A speech synthesis unit converts the text data converted by the analysis unit into speech, A playback unit that plays back the sound generated by the aforementioned speech synthesis unit, A question reception department that accepts questions from users, A response generation unit that generates answers to questions received by the aforementioned question receiving unit, The response providing unit provides the response generated by the response generation unit in voice, Equipped with A system characterized by:

2. The analysis unit Recognizes text within an image and extracts it as text data. The system of claim 1 .

3. The answer generation unit Generate appropriate answers to user questions. The system of claim 1 .

4. The answer providing unit Provide generated answers by voice The system of claim 1 .

5. The analysis unit Based on the text content, generate an analysis, overall assessment, or summary of what the protagonist thought. The system of claim 1 .

6. The image acquisition unit The system estimates the user's emotions and adjusts the timing of image acquisition based on those emotions. The system of claim 1 .

7. The image acquisition unit When acquiring images, the system analyzes the user's past reading history and selects the appropriate acquisition method. The system of claim 1 .

8. The image acquisition unit When acquiring images, filtering is performed based on the user's current areas of interest. The system of claim 1 .

Images