System

Abstract

To provide a system that enables appropriate verbalization of sensations or expressions and their communication to others.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives expressions from the user. The analysis unit analyzes the expressions received by the reception unit. The generation unit generates appropriate words based on the expressions analyzed by the analysis unit. The provision unit communicates the words generated by the generation unit to others.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, it was difficult to properly verbalize sensations and expressions and convey them to others.

[0005] The system according to the embodiment aims to appropriately verbalize sensations and expressions and convey them to others. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an expression from a user. The analysis unit analyzes the expression received by the reception unit. The generation unit generates an appropriate word based on the expression analyzed by the analysis unit. The provision unit conveys the word generated by the generation unit to another person. Effect of the Invention

[0007] The system according to the embodiment can appropriately verbalize sensations and expressions and convey them to others. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The sensory verbalization support system according to the embodiment of the present invention is a system that supports verbalizing sensations and communicating them to others. Even if an individual lacks vocabulary, this system allows AI to complement the vocabulary and verbalize the sensation. Specifically, when a user expresses a sensation using words, onomatopoeia, movements, etc., AI analyzes the expression and generates appropriate words. The generated words can be communicated concisely and appropriately to others. In addition, this system can also perform intuitive language translation by combining it with a translation function. Furthermore, it can be used to support people with disabilities to communicate intuitively. For example, a user expresses a sensation using words, onomatopoeia, and movements. For example, the user moves his / her hand while saying "Hmm, it feels like this." This expression is input to the AI. Next, the AI ​​analyzes the input expression. The AI ​​understands the sensation from the user's words, onomatopoeia, and movements, and generates appropriate words corresponding to it. For example, it generates words such as "a movement that makes a large circle" from the movement of the user's hand. The generated words are used to communicate to others. For example, the user can appropriately communicate the sensation to others by explaining it in words as "a movement that makes a large circle." Furthermore, this system, when combined with a translation function, enables intuitive communication between different languages. For example, AI can translate the sensations expressed by a user in Japanese into English and communicate them to others. It can also be used to support people with disabilities in intuitive communication. For example, a person who has difficulty speaking can express sensations through hand movements, and AI can analyze those movements to generate words and communicate them to others. In this way, the AI ​​complements vocabulary and verbalizes sensations, making it possible to properly communicate them to others. In this way, the sensory verbalization support system can support verbalizing sensations and communicating them to others.

[0029] The sensory verbalization support system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's expression. The user's expression includes, but is not limited to, words, onomatopoeia, and movements. For example, the reception unit can receive an expression in which the user moves his / her hand while saying, "Hmm, it feels like this." The analysis unit uses AI to analyze the expression received by the reception unit. The analysis unit understands a sensation from, for example, the user's words, onomatopoeia, and movements. For example, the analysis unit extracts information for generating a word such as "a movement that draws a large circle" from the movement of the user's hand. The generation unit uses AI to generate an appropriate word based on the expression analyzed by the analysis unit. The generation unit generates, for example, a word such as "a movement that draws a large circle" from the movement of the user's hand. The generation unit can generate an appropriate word corresponding to the sensation using a generation AI (for example, a text generation AI or a multimodal generation AI). The provision unit conveys the word generated by the generation unit to others. The provision unit displays, for example, the generated word as text. The providing unit can also output the generated words as voice. As a result, the sensory verbalization support system according to the embodiment can appropriately analyze the user's expression, generate words, and convey them to others. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can generate words using an AI model for displaying the generated words as text.

[0030] The reception unit receives the user's expression. The user's expression includes, but is not limited to, words, onomatopoeia, and movements. For example, the reception unit can receive an expression in which the user moves his / her hand while saying, "Hmm, it feels like this." Specifically, the reception unit captures the user's voice and movement with high accuracy using a voice input device and a motion capture device. The voice input device uses a microphone with a noise canceling function to clearly collect the user's speech. The motion capture device accurately tracks the user's hand and body movements in three-dimensional space and acquires the movement data in real time. This allows the reception unit to receive the user's various expressions without omission. In addition, the reception unit temporarily stores the user's expression and performs preprocessing of the data before transmitting it to the analysis unit. For example, noise removal from the voice data and smoothing of the movement data are performed so that the analysis unit can analyze efficiently. Furthermore, the reception unit can continuously monitor the user's expression and collect additional data as necessary. This allows the reception unit to accurately grasp the user's intention and improve the accuracy of the entire system.

[0031] The analysis unit uses AI to analyze the expressions received by the reception unit. For example, the analysis unit understands the user's senses from the user's words, onomatopoeia, and movements. Specifically, the analysis unit uses natural language processing (NLP) technology and computer vision technology to analyze the user's expressions from multiple angles. Using NLP technology, the user's speech and onomatopoeia are converted into text data and their meaning is analyzed. For example, if a user says, "Hmm, it feels like this," the NLP model understands the ambiguous expression from the context and extracts the appropriate meaning. Using computer vision technology, the user's movements are analyzed and what the movements mean is understood. For example, if a user makes a movement to draw a large circle with their hand, the computer vision model recognizes the movement and analyzes it as a "movement that draws a large circle." Furthermore, the analysis unit integrates these analysis results to comprehensively understand the user's senses and intentions. The analysis unit can deepen its understanding of the user's expressions by utilizing past data and trained models, and perform more accurate analysis. This enables the analysis unit to accurately analyze the user's various expressions and provide the information necessary for the next step, which is word generation.

[0032] The generation unit uses AI to generate appropriate words based on the expressions analyzed by the analysis unit. For example, the generation unit generates words such as "movements that draw a large circle" from the user's hand movements. Specifically, the generation unit can use generation AI (for example, text generation AI or multimodal generation AI) to generate appropriate words that correspond to the sensation. The text generation AI generates natural words based on the data provided by the analysis unit. For example, if the user's movements are analyzed as "movements that draw a large circle," the text generation AI generates appropriate words to express that movement. The multimodal generation AI can integrate multiple modalities such as voice and movement to generate richer expressions. For example, it combines the user's speech and movements to generate complex expressions such as "Hmm, a movement that draws a large circle like this." The generation unit can also evaluate the naturalness and appropriateness of the generated words and make corrections as necessary. This allows the generation unit to accurately convert the user's sensations and intentions into words and prepare them to be conveyed to others. In addition, the generation unit also has a function of translating the generated words into multiple languages, thereby supporting communication between users who speak different languages.

[0033] The providing unit conveys the words generated by the generating unit to others. For example, the providing unit displays the generated words as text. The providing unit can also output the generated words as audio. Specifically, the providing unit uses an output device such as a display or a speaker to provide the generated words visually or audibly. The display displays the generated text in an easy-to-read font and layout so that the user can easily understand it. The speaker plays the generated words in a natural voice so that the user can receive the information audibly. Furthermore, the providing unit also has a function of transmitting the generated words to other devices or systems. For example, the generated words can be transmitted as a message app or email to convey information to others in remote locations. The providing unit also has a function of recording the generated words and making them available for later reference. This allows the user to look back on past expressions and generated words and reuse them as necessary. The providing unit can collect user feedback and continuously improve the accuracy and appropriateness of the generated words. For example, when a user makes corrections to the generated words, the corrections are learned and reflected in the next and subsequent generation. This allows the providing unit to always provide optimal information to the user, thereby improving the reliability and user satisfaction of the entire system.

[0034] The sensory verbalization support system includes a sign language analysis unit that analyzes sign language. The sign language analysis unit analyzes the sign language. The sign language includes, for example, American Sign Language and Japanese Sign Language, but is not limited to such examples. The sign language analysis unit, for example, analyzes American Sign Language and converts the content into text data. The sign language analysis unit can also analyze Japanese Sign Language and convert the content into text data. In this way, by analyzing the sign language, expressions using sign language can also be appropriately converted into words. A part or all of the above-mentioned processing in the sign language analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the sign language analysis unit can input the action data of the sign language to a generation AI and convert the content of the sign language into text data.

[0035] The sensory verbalization support system includes a translation unit that provides a translation function. The translation unit provides the translation function. The translation function includes, for example, a type of supported language, a translation algorithm, and the like, but is not limited to such examples. The translation unit performs, for example, translation from Japanese to English. The translation unit can also perform translation from English to Japanese. In this way, by providing the translation function, intuitive communication between different languages ​​is possible. Some or all of the above-mentioned processing in the translation unit may be performed, for example, using AI, or may be performed without using AI. For example, the translation unit can input text data to be translated into a generation AI and generate a translation result.

[0036] The sensory verbalization support system includes a support unit that supports a person with a disability. The support unit supports a person with a disability. The support includes, for example, visual impairment, hearing impairment, and the like, but is not limited to such examples. The support unit provides, for example, voice support to a person with a visual impairment. The support unit can also provide text support to a person with a hearing impairment. This enables support for a person with a disability to communicate intuitively. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the user's motion data to a generation AI and generate support content.

[0037] The analysis unit can analyze the sensation from the user's words, onomatopoeia, and movements. The analysis unit, for example, analyzes the user's words. For example, the analysis unit converts the words uttered by the user into text data using voice recognition technology and analyzes the contents. The analysis unit can also analyze the user's onomatopoeia. For example, the analysis unit converts the onomatopoeia uttered by the user into text data using voice recognition technology and analyzes the contents. The analysis unit can also analyze the user's movements. For example, the analysis unit digitizes the movements of the user's hands using motion capture technology and analyzes the contents. This makes it possible to understand the sensation from the user's various ways of expression. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's movement data to the generation AI and cause the generation AI to execute the analysis of the sensation.

[0038] The generation unit can generate appropriate words corresponding to the sensation. The generation unit generates appropriate words corresponding to the sensation, for example. For example, the generation unit generates words such as "movements that draw a large circle" from the user's hand movements. The generation unit can also generate words such as "rain pouring" from the user's onomatopoeia. The generation unit can also generate words such as "very delicious" from the user's words. In this way, by generating appropriate words corresponding to the sensation, the sensation can be appropriately conveyed to others. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's motion data to the generation AI and cause the generation AI to generate appropriate words.

[0039] The reception unit can analyze the user's past expression history and select an appropriate reception method. The reception unit, for example, analyzes the user's past expression history. For example, the reception unit preferentially suggests an expression method (words, onomatopoeia, movement, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an expression method to be used in a specific situation from the user's past expression history. The reception unit can also select the most efficient reception method based on the user's past expression history. For example, the reception unit analyzes an expression method used by the user in the past and suggests an optimal reception method. As a result, the reception of expressions is made more efficient by selecting an optimal reception method based on the user's past expression history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past expression history data to the generation AI and cause the generation AI to select the optimal reception method.

[0040] The reception unit can perform filtering based on the user's current situation and environment when receiving an expression. The reception unit considers, for example, the user's current situation and environment. For example, when the user is in a noisy environment, the reception unit suppresses voice input and prioritizes text input. In addition, when the user is in a quiet environment, the reception unit can also prioritize voice input and quickly receive an expression. In addition, when the user is moving, the reception unit can suggest a simple input method and smoothly receive an expression. This makes it possible to receive a more appropriate expression by adjusting the reception of the expression according to the user's situation and environment. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's current situation data to the generation AI and cause the generation AI to execute filtering.

[0041] The reception unit can preferentially receive highly relevant expressions in consideration of the geographical location information of the user when receiving an expression. The reception unit, for example, considers the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially receives expressions related to the area. In addition, when the user is traveling, the reception unit can also preferentially receive expressions related to the travel destination. In addition, when the user is at home, the reception unit can also preferentially receive expressions related to daily life. In this way, by considering the geographical location information of the user, highly relevant expressions can be preferentially received. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select highly relevant expressions.

[0042] The reception unit can analyze the social media activity of the user and receive related expressions when receiving the expression. The reception unit, for example, analyzes the social media activity of the user. For example, the reception unit preferentially receives expressions that the user frequently uses on social media. The reception unit can also preferentially receive expressions related to a specific topic from the social media activity of the user. The reception unit can also analyze the social media activity of the user and receive the most relevant expression. In this way, by analyzing the social media activity of the user, it is possible to preferentially receive highly relevant expressions. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the social media activity data of the user to the generation AI and cause the generation AI to select related expressions.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the expression during the analysis. The analysis unit, for example, evaluates the importance of the expression. For example, the analysis unit performs a detailed analysis on an important expression to provide a highly accurate result. The analysis unit can also perform a simple analysis on a general expression to provide a result quickly. The analysis unit can also perform a detailed analysis on an important expression in a specific situation to provide a highly accurate result. This allows a detailed analysis of an important expression to be performed by adjusting the level of detail of the analysis based on the importance of the expression. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the expression to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the expression during analysis. The analysis unit, for example, identifies the category of the expression. For example, the analysis unit applies an emotion analysis algorithm to an emotion expression. The analysis unit can also apply a motion analysis algorithm to a motion expression. The analysis unit can also apply a sound analysis algorithm to an onomatopoeia expression. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of the expression. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category data of the expression to the generation AI and cause the generation AI to apply the analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the submission time of the expression during the analysis. The analysis unit, for example, takes into account the submission time of the expression. For example, the analysis unit prioritizes the analysis of the latest expression and quickly provides the result. The analysis unit can also prioritize the analysis of the latest expression while referring to past expressions. The analysis unit can also prioritize the analysis of expressions submitted in a specific time period. In this way, the analysis priority can be determined based on the submission time of the expression, so that the latest expression can be quickly analyzed. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the expression to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the expressions during analysis. The analysis unit, for example, evaluates the relevance of the expressions. For example, the analysis unit prioritizes analysis of highly relevant expressions and quickly provides results. The analysis unit can also prioritize analysis of important expressions while leaving less relevant expressions for later. The analysis unit can also prioritize analysis of expressions related to a specific topic. As a result, highly relevant expressions can be analyzed preferentially by adjusting the order of analysis based on the relevance of the expressions. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the relevance data of the expressions to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] The generation unit can adjust the level of detail of the words to be generated based on the importance of the expression at the time of generation. The generation unit, for example, evaluates the importance of the expression. For example, the generation unit generates detailed and highly accurate words for important expressions. The generation unit can also generate words that are simple and can be generated quickly for general expressions. The generation unit can also generate detailed and highly accurate words for expressions that are important in a specific situation. As a result, detailed words can be generated for important expressions by adjusting the level of detail of the words to be generated based on the importance of the expression. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input importance data of the expression to the generation AI and cause the generation AI to adjust the level of detail of the words.

[0048] The generation unit can apply different generation algorithms depending on the category of the expression at the time of generation. The generation unit, for example, identifies the category of the expression. For example, the generation unit applies an emotion generation algorithm to an emotion expression. The generation unit can also apply a motion generation algorithm to a motion expression. The generation unit can also apply a voice generation algorithm to an onomatopoeia expression. Thereby, by applying an appropriate generation algorithm depending on the category of the expression, the accuracy of the generated words is improved. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the category data of the expression to the generation AI and cause the generation AI to apply the generation algorithm.

[0049] The generation unit can determine the priority of the words to be generated based on the submission time of the expression at the time of generation. The generation unit considers, for example, the submission time of the expression. For example, the generation unit quickly generates words for the latest expression. The generation unit can also generate words preferentially for the latest expression while referring to past expressions. The generation unit can also generate words preferentially for expressions submitted in a specific time period. In this way, by determining the priority of the words to be generated based on the submission time of the expression, it is possible to quickly generate words for the latest expression. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the submission time data of the expression to the generation AI and cause the generation AI to execute the determination of the priority of the words.

[0050] The generation unit can adjust the order of words to be generated based on the relevance of the expressions at the time of generation. The generation unit, for example, evaluates the relevance of the expressions. For example, the generation unit quickly generates words for expressions with high relevance. The generation unit can also generate words preferentially for important expressions, leaving expressions with low relevance to be generated later. The generation unit can also generate words preferentially for expressions related to a specific topic. In this way, by adjusting the order of words to be generated based on the relevance of the expressions, words can be quickly generated for expressions with high relevance. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance data of the expressions to the generation AI and cause the generation AI to adjust the order of the words.

[0051] The providing unit can select the optimal providing method by referring to the user's past reception history at the time of providing. The providing unit, for example, refers to the user's past reception history. For example, the providing unit preferentially suggests a providing method (text, voice, etc.) that the user has used in the past. The providing unit can also predict and suggest a providing method to be used in a specific situation from the user's past reception history. The providing unit can also select the most efficient providing method based on the user's past reception history. As a result, the efficiency of provision is improved by selecting the optimal providing method based on the user's past reception history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's past reception history data to the generation AI and cause the generation AI to select the optimal providing method.

[0052] The providing unit can customize the means of providing based on the user's current situation at the time of providing. The providing unit, for example, takes into account the user's current situation. For example, when the user is moving, the providing unit prioritizes providing by voice to reduce visual burden. In addition, when the user is in a quiet environment, the providing unit can also prioritize providing by text to reduce the impact on the surroundings. In addition, when the user is in a meeting, the providing unit can also prioritize providing by concise text to quickly convey information. In this way, by customizing the means of providing according to the user's current situation, words can be provided in a more appropriate manner. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data to the generating AI and cause the generating AI to execute customization of the means of providing.

[0053] The providing unit can select the optimal providing method in consideration of the geographical location information of the user at the time of providing. The providing unit, for example, considers the geographical location information of the user. For example, when the user is in a specific area, the providing unit can provide information related to the area preferentially. In addition, when the user is traveling, the providing unit can also provide information related to the travel destination preferentially. In addition, when the user is at home, the providing unit can also provide information related to daily life preferentially. In this way, by considering the geographical location information of the user, highly relevant information can be provided preferentially. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0054] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of provision. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit preferentially suggests a provision method that the user frequently uses on social media. The providing unit can also preferentially provide information related to a specific topic from the user's social media activity. The providing unit can also analyze the user's social media activity and suggest the most relevant provision method. In this way, a highly relevant provision method can be suggested by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's social media activity data to the generating AI and cause the generating AI to execute the suggestion of a means of provision.

[0055] The sign language analysis unit can adjust the level of detail of the analysis based on the importance of the sign language when analyzing the sign language. The sign language analysis unit, for example, evaluates the importance of the sign language. For example, the sign language analysis unit performs a detailed analysis on important sign languages ​​to provide highly accurate results. The sign language analysis unit can also perform a simple analysis on general sign languages ​​to provide quick results. The sign language analysis unit can also perform a detailed analysis on important sign languages ​​in a specific situation to provide highly accurate results. As a result, by adjusting the level of detail of the analysis based on the importance of the sign language, it becomes possible to perform a detailed analysis on important sign languages. A part or all of the above-mentioned processing in the sign language analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the sign language analysis unit can input the importance data of the sign language to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0056] The sign language analysis unit can determine the priority of analysis based on the time of submission of the sign language when analyzing the sign language. The sign language analysis unit, for example, takes into consideration the time of submission of the sign language. For example, the sign language analysis unit prioritizes analysis of the latest sign language and quickly provides the result. The sign language analysis unit can also prioritize analysis of the latest sign language while referring to past sign languages. The sign language analysis unit can also prioritize analysis of sign language submitted in a specific time period. In this way, the latest sign language can be quickly analyzed by determining the priority of analysis based on the time of submission of the sign language. A part or all of the above-mentioned processing in the sign language analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the sign language analysis unit can input data on the time of submission of the sign language to the generation AI and cause the generation AI to execute the determination of the priority of analysis.

[0057] The translation unit can adjust the level of detail of the translation based on the importance of the expression during translation. The translation unit, for example, evaluates the importance of the expression. For example, the translation unit performs a detailed and accurate translation for an important expression. The translation unit can also apply a method that can translate general expressions simply and quickly. The translation unit can also perform a detailed and accurate translation for an important expression in a specific situation. As a result, by adjusting the level of detail of the translation based on the importance of the expression, detailed translation is possible for an important expression. A part or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the importance data of the expression to the generation AI and cause the generation AI to adjust the level of detail of the translation.

[0058] The translation unit can determine the priority of translation based on the submission time of the expression during translation. The translation unit, for example, takes into account the submission time of the expression. For example, the translation unit translates the latest expression with priority and quickly provides the result. The translation unit can also translate the latest expression with priority while referring to past expressions. The translation unit can also translate expressions submitted in a specific time period with priority. In this way, the latest expression can be quickly translated by determining the priority of translation based on the submission time of the expression. A part or all of the above-mentioned processing in the translation unit may be performed, for example, using AI, or may be performed without using AI. For example, the translation unit can input the submission time data of the expression to the generation AI and cause the generation AI to execute the determination of the translation priority.

[0059] The support unit can select the optimal support method by referring to the user's past support history at the time of support. The support unit, for example, refers to the user's past support history. For example, the support unit preferentially suggests a support method that the user has used in the past. The support unit can also predict and suggest a support method to be used in a specific situation from the user's past support history. The support unit can also select the most efficient support method based on the user's past support history. As a result, the efficiency of support is improved by selecting the optimal support method based on the user's past support history. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI or may be performed without using AI. For example, the support unit can input the user's past support history data to the generation AI and cause the generation AI to select the optimal support method.

[0060] The support unit can select the optimal support method in consideration of the geographical location information of the user at the time of support. The support unit considers, for example, the geographical location information of the user. For example, when the user is in a specific area, the support unit preferentially provides a support method related to the area. In addition, when the user is traveling, the support unit can also preferentially provide a support method related to the travel destination. In addition, when the user is at home, the support unit can also preferentially provide a support method related to daily life. In this way, by considering the geographical location information of the user, it is possible to preferentially provide a highly relevant support method. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select the optimal support method.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] When accepting an expression from a user, the sensory verbalization support system can analyze the user's past expression history and suggest an optimal acceptance method. For example, the acceptance unit preferentially suggests an expression method (words, onomatopoeia, movement, etc.) that the user has used frequently in the past. The acceptance unit can also predict and suggest an expression method to be used in a specific situation from the user's past expression history. This allows the acceptance of expressions to be made more efficient by selecting an optimal acceptance method based on the user's past expression history. A part or all of the above-mentioned processing in the acceptance unit may be performed, for example, using AI or may be performed without using AI. For example, the acceptance unit can input the user's past expression history data to the generation AI and cause the generation AI to select an optimal acceptance method.

[0063] The sensory verbalization support system can analyze sign language movement data in real time in the sign language analysis unit and instantly convert the user's intention into text. For example, the sign language analysis unit digitizes the user's hand movements using motion capture technology and analyzes the contents in real time. The sign language analysis unit can also input the user's sign language movement data into a generation AI and instantly convert it into text data. This allows expressions using sign language to be appropriately converted into words in real time. Some or all of the above-mentioned processing in the sign language analysis unit may be performed, for example, using AI or may be performed without using AI.

[0064] The sensory verbalization support system can select the optimal support method in the support unit by taking into account the geographical location information of the user. For example, when the user is in a specific area, the support unit can provide a support method related to that area with priority. In addition, when the user is traveling, the support unit can also provide a support method related to the travel destination with priority. In this way, by taking into account the geographical location information of the user, it is possible to provide a highly relevant support method with priority. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select the optimal support method.

[0065] The sensory verbalization support system can perform filtering in the reception unit based on the user's current situation and environment. For example, when the user is in a noisy environment, the reception unit suppresses voice input and prioritizes text input. Also, when the user is in a quiet environment, the reception unit can prioritize voice input and quickly accept expressions. Also, when the user is on the move, the reception unit can suggest a simple input method and smoothly accept expressions. This makes it possible to accept more appropriate expressions by adjusting the acceptance of expressions according to the user's situation and environment.

[0066] The sensory verbalization support system can analyze the social media activity of the user in the providing unit and provide related expressions preferentially. For example, the providing unit can provide expressions frequently used by the user on social media preferentially. The providing unit can also provide expressions related to a specific topic from the social media activity of the user preferentially. In this way, by analyzing the social media activity of the user, highly relevant expressions can be provided preferentially. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the social media activity data of the user to the generating AI and cause the generating AI to select related expressions.

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

[0068] Step 1: The reception unit receives a user's expression. The user's expression may include, but is not limited to, words, onomatopoeia, and movements. For example, the reception unit may receive an expression in which the user moves his / her hand while saying, "Hmm, it feels like this." Step 2: The analysis unit uses AI to analyze the expression received by the reception unit. The analysis unit understands sensations from the user's words, onomatopoeia, and movements. For example, the analysis unit extracts information from the user's hand movements to generate words such as "movements that draw large circles." Step 3: The generation unit uses AI to generate appropriate words based on the expressions analyzed by the analysis unit. For example, the generation unit generates words such as "a large circular movement" from the user's hand movements. The generation unit can use a generation AI (for example, a text generation AI or a multimodal generation AI) to generate appropriate words corresponding to the sensation. Step 4: The providing unit communicates the words generated by the generating unit to others. For example, the providing unit displays the generated words as text. The providing unit can also output the generated words as audio.

[0069] (Example 2) The sensory verbalization support system according to the embodiment of the present invention is a system that supports verbalizing sensations and communicating them to others. Even if an individual lacks vocabulary, this system allows AI to complement the vocabulary and verbalize the sensation. Specifically, when a user expresses a sensation using words, onomatopoeia, movements, etc., AI analyzes the expression and generates appropriate words. The generated words can be communicated concisely and appropriately to others. In addition, this system can also perform intuitive language translation by combining it with a translation function. Furthermore, it can be used to support people with disabilities to communicate intuitively. For example, a user expresses a sensation using words, onomatopoeia, and movements. For example, the user moves his / her hand while saying "Hmm, it feels like this." This expression is input to the AI. Next, the AI ​​analyzes the input expression. The AI ​​understands the sensation from the user's words, onomatopoeia, and movements, and generates appropriate words corresponding to it. For example, it generates words such as "a movement that makes a large circle" from the movement of the user's hand. The generated words are used to communicate to others. For example, the user can appropriately communicate the sensation to others by explaining it in words as "a movement that makes a large circle." Furthermore, this system, when combined with a translation function, enables intuitive communication between different languages. For example, AI can translate the sensations expressed by a user in Japanese into English and communicate them to others. It can also be used to support people with disabilities in intuitive communication. For example, a person who has difficulty speaking can express sensations through hand movements, and AI can analyze those movements to generate words and communicate them to others. In this way, the AI ​​complements vocabulary and verbalizes sensations, making it possible to properly communicate them to others. In this way, the sensory verbalization support system can support verbalizing sensations and communicating them to others.

[0070] The sensory verbalization support system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's expression. The user's expression includes, but is not limited to, words, onomatopoeia, and movements. For example, the reception unit can receive an expression in which the user moves his / her hand while saying, "Hmm, it feels like this." The analysis unit uses AI to analyze the expression received by the reception unit. The analysis unit understands a sensation from, for example, the user's words, onomatopoeia, and movements. For example, the analysis unit extracts information for generating a word such as "a movement that draws a large circle" from the movement of the user's hand. The generation unit uses AI to generate an appropriate word based on the expression analyzed by the analysis unit. The generation unit generates, for example, a word such as "a movement that draws a large circle" from the movement of the user's hand. The generation unit can generate an appropriate word corresponding to the sensation using a generation AI (for example, a text generation AI or a multimodal generation AI). The provision unit conveys the word generated by the generation unit to others. The provision unit displays, for example, the generated word as text. The providing unit can also output the generated words as voice. As a result, the sensory verbalization support system according to the embodiment can appropriately analyze the user's expression, generate words, and convey them to others. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can generate words using an AI model for displaying the generated words as text.

[0071] The reception unit receives the user's expression. The user's expression includes, but is not limited to, words, onomatopoeia, and movements. For example, the reception unit can receive an expression in which the user moves his / her hand while saying, "Hmm, it feels like this." Specifically, the reception unit captures the user's voice and movement with high accuracy using a voice input device and a motion capture device. The voice input device uses a microphone with a noise canceling function to clearly collect the user's speech. The motion capture device accurately tracks the user's hand and body movements in three-dimensional space and acquires the movement data in real time. This allows the reception unit to receive the user's various expressions without omission. In addition, the reception unit temporarily stores the user's expression and performs preprocessing of the data before transmitting it to the analysis unit. For example, noise removal from the voice data and smoothing of the movement data are performed so that the analysis unit can analyze efficiently. Furthermore, the reception unit can continuously monitor the user's expression and collect additional data as necessary. This allows the reception unit to accurately grasp the user's intention and improve the accuracy of the entire system.

[0072] The analysis unit uses AI to analyze the expressions received by the reception unit. For example, the analysis unit understands the user's senses from the user's words, onomatopoeia, and movements. Specifically, the analysis unit uses natural language processing (NLP) technology and computer vision technology to analyze the user's expressions from multiple angles. Using NLP technology, the user's speech and onomatopoeia are converted into text data and their meaning is analyzed. For example, if a user says, "Hmm, it feels like this," the NLP model understands the ambiguous expression from the context and extracts the appropriate meaning. Using computer vision technology, the user's movements are analyzed and what the movements mean is understood. For example, if a user makes a movement to draw a large circle with their hand, the computer vision model recognizes the movement and analyzes it as a "movement that draws a large circle." Furthermore, the analysis unit integrates these analysis results to comprehensively understand the user's senses and intentions. The analysis unit can deepen its understanding of the user's expressions by utilizing past data and trained models, and perform more accurate analysis. This enables the analysis unit to accurately analyze the user's various expressions and provide the information necessary for the next step, which is word generation.

[0073] The generation unit uses AI to generate appropriate words based on the expressions analyzed by the analysis unit. For example, the generation unit generates words such as "movements that draw a large circle" from the user's hand movements. Specifically, the generation unit can use generation AI (for example, text generation AI or multimodal generation AI) to generate appropriate words that correspond to the sensation. The text generation AI generates natural words based on the data provided by the analysis unit. For example, if the user's movements are analyzed as "movements that draw a large circle," the text generation AI generates appropriate words to express that movement. The multimodal generation AI can integrate multiple modalities such as voice and movement to generate richer expressions. For example, it combines the user's speech and movements to generate complex expressions such as "Hmm, a movement that draws a large circle like this." The generation unit can also evaluate the naturalness and appropriateness of the generated words and make corrections as necessary. This allows the generation unit to accurately convert the user's sensations and intentions into words and prepare them to be conveyed to others. In addition, the generation unit also has a function of translating the generated words into multiple languages, thereby supporting communication between users who speak different languages.

[0074] The providing unit conveys the words generated by the generating unit to others. For example, the providing unit displays the generated words as text. The providing unit can also output the generated words as audio. Specifically, the providing unit uses an output device such as a display or a speaker to provide the generated words visually or audibly. The display displays the generated text in an easy-to-read font and layout so that the user can easily understand it. The speaker plays the generated words in a natural voice so that the user can receive the information audibly. Furthermore, the providing unit also has a function of transmitting the generated words to other devices or systems. For example, the generated words can be transmitted as a message app or email to convey information to others in remote locations. The providing unit also has a function of recording the generated words and making them available for later reference. This allows the user to look back on past expressions and generated words and reuse them as necessary. The providing unit can collect user feedback and continuously improve the accuracy and appropriateness of the generated words. For example, when a user makes corrections to the generated words, the corrections are learned and reflected in the next and subsequent generation. This allows the providing unit to always provide optimal information to the user, thereby improving the reliability and user satisfaction of the entire system.

[0075] The sensory verbalization support system includes a sign language analysis unit that analyzes sign language. The sign language analysis unit analyzes the sign language. The sign language includes, for example, American Sign Language and Japanese Sign Language, but is not limited to such examples. The sign language analysis unit, for example, analyzes American Sign Language and converts the content into text data. The sign language analysis unit can also analyze Japanese Sign Language and convert the content into text data. In this way, by analyzing the sign language, expressions using sign language can also be appropriately converted into words. A part or all of the above-mentioned processing in the sign language analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the sign language analysis unit can input the action data of the sign language to a generation AI and convert the content of the sign language into text data.

[0076] The sensory verbalization support system includes a translation unit that provides a translation function. The translation unit provides the translation function. The translation function includes, for example, a type of supported language, a translation algorithm, and the like, but is not limited to such examples. The translation unit performs, for example, translation from Japanese to English. The translation unit can also perform translation from English to Japanese. In this way, by providing the translation function, intuitive communication between different languages ​​is possible. Some or all of the above-mentioned processing in the translation unit may be performed, for example, using AI, or may be performed without using AI. For example, the translation unit can input text data to be translated into a generation AI and generate a translation result.

[0077] The sensory verbalization support system includes a support unit that supports a person with a disability. The support unit supports a person with a disability. The support includes, for example, visual impairment, hearing impairment, and the like, but is not limited to such examples. The support unit provides, for example, voice support to a person with a visual impairment. The support unit can also provide text support to a person with a hearing impairment. This enables support for a person with a disability to communicate intuitively. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the user's motion data to a generation AI and generate support content.

[0078] The analysis unit can analyze the sensation from the user's words, onomatopoeia, and movements. The analysis unit, for example, analyzes the user's words. For example, the analysis unit converts the words uttered by the user into text data using voice recognition technology and analyzes the contents. The analysis unit can also analyze the user's onomatopoeia. For example, the analysis unit converts the onomatopoeia uttered by the user into text data using voice recognition technology and analyzes the contents. The analysis unit can also analyze the user's movements. For example, the analysis unit digitizes the movements of the user's hands using motion capture technology and analyzes the contents. This makes it possible to understand the sensation from the user's various ways of expression. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's movement data to the generation AI and cause the generation AI to execute the analysis of the sensation.

[0079] The generation unit can generate appropriate words corresponding to the sensation. The generation unit generates appropriate words corresponding to the sensation, for example. For example, the generation unit generates words such as "movements that draw a large circle" from the user's hand movements. The generation unit can also generate words such as "rain pouring" from the user's onomatopoeia. The generation unit can also generate words such as "very delicious" from the user's words. In this way, by generating appropriate words corresponding to the sensation, the sensation can be appropriately conveyed to others. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's motion data to the generation AI and cause the generation AI to generate appropriate words.

[0080] The reception unit can estimate the user's emotion and adjust the expression reception method based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technology. The reception unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the heart rate fluctuation. Next, the reception unit adjusts the expression reception method based on the estimated user's emotion. For example, when the user is nervous, a simple and intuitive interface is provided to facilitate the reception of the expression. When the user is relaxed, detailed input options are provided to accept a variety of expressions. When the user is in a hurry, voice input is prioritized to allow the expression to be quickly received. This makes it possible to receive a more appropriate expression by adjusting the expression reception method according to the user's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI or may be performed without using an AI. For example, the reception unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0081] The reception unit can analyze the user's past expression history and select an appropriate reception method. The reception unit, for example, analyzes the user's past expression history. For example, the reception unit preferentially suggests an expression method (words, onomatopoeia, movement, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an expression method to be used in a specific situation from the user's past expression history. The reception unit can also select the most efficient reception method based on the user's past expression history. For example, the reception unit analyzes an expression method used by the user in the past and suggests an optimal reception method. As a result, the reception of expressions is made more efficient by selecting an optimal reception method based on the user's past expression history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past expression history data to the generation AI and cause the generation AI to select the optimal reception method.

[0082] The reception unit can perform filtering based on the user's current situation and environment when receiving an expression. The reception unit considers, for example, the user's current situation and environment. For example, when the user is in a noisy environment, the reception unit suppresses voice input and prioritizes text input. In addition, when the user is in a quiet environment, the reception unit can also prioritize voice input and quickly receive an expression. In addition, when the user is moving, the reception unit can suggest a simple input method and smoothly receive an expression. This makes it possible to receive a more appropriate expression by adjusting the reception of the expression according to the user's situation and environment. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's current situation data to the generation AI and cause the generation AI to execute filtering.

[0083] The reception unit can estimate the user's emotion and determine the priority of expressions to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technology. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the fluctuation of the heart rate. Next, the reception unit determines the priority of expressions to be received based on the estimated user's emotion. For example, if the user is nervous, important expressions are preferentially received. Also, if the user is relaxed, detailed expressions are preferentially received. Also, if the user is in a hurry, concise expressions are preferentially received. In this way, important expressions can be preferentially received by determining the priority of expressions according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0084] The reception unit can preferentially receive highly relevant expressions in consideration of the geographical location information of the user when receiving an expression. The reception unit, for example, considers the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially receives expressions related to the area. In addition, when the user is traveling, the reception unit can also preferentially receive expressions related to the travel destination. In addition, when the user is at home, the reception unit can also preferentially receive expressions related to daily life. In this way, by considering the geographical location information of the user, highly relevant expressions can be preferentially received. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select highly relevant expressions.

[0085] The reception unit can analyze the social media activity of the user and receive related expressions when receiving the expression. The reception unit, for example, analyzes the social media activity of the user. For example, the reception unit preferentially receives expressions that the user frequently uses on social media. The reception unit can also preferentially receive expressions related to a specific topic from the social media activity of the user. The reception unit can also analyze the social media activity of the user and receive the most relevant expression. In this way, by analyzing the social media activity of the user, it is possible to preferentially receive highly relevant expressions. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the social media activity data of the user to the generation AI and cause the generation AI to select related expressions.

[0086] The analysis unit can estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technology. The analysis unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. Next, the analysis unit adjusts the analysis method based on the estimated user's emotion. For example, if the user is nervous, a simple analysis method is applied and the result is provided quickly. If the user is relaxed, a detailed analysis method is applied and the result is provided with high accuracy. If the user is in a hurry, a simple analysis method is applied and the result is provided quickly. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the expression during the analysis. The analysis unit, for example, evaluates the importance of the expression. For example, the analysis unit performs a detailed analysis on an important expression to provide a highly accurate result. The analysis unit can also perform a simple analysis on a general expression to provide a result quickly. The analysis unit can also perform a detailed analysis on an important expression in a specific situation to provide a highly accurate result. This allows a detailed analysis of an important expression to be performed by adjusting the level of detail of the analysis based on the importance of the expression. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the expression to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the expression during analysis. The analysis unit, for example, identifies the category of the expression. For example, the analysis unit applies an emotion analysis algorithm to an emotion expression. The analysis unit can also apply a motion analysis algorithm to a motion expression. The analysis unit can also apply a sound analysis algorithm to an onomatopoeia expression. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of the expression. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category data of the expression to the generation AI and cause the generation AI to apply the analysis algorithm.

[0089] The analysis unit can estimate the user's emotion and determine the analysis priority based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technology. The analysis unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. Next, the analysis unit determines the analysis priority based on the estimated user's emotion. For example, if the user is nervous, important expressions are analyzed with priority. Also, if the user is relaxed, detailed expressions are analyzed with priority. Also, if the user is in a hurry, concise expressions are analyzed with priority. In this way, important expressions can be analyzed with priority by determining the analysis priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0090] The analysis unit can determine the priority of the analysis based on the submission time of the expression during the analysis. The analysis unit, for example, takes into account the submission time of the expression. For example, the analysis unit prioritizes the analysis of the latest expression and quickly provides the result. The analysis unit can also prioritize the analysis of the latest expression while referring to past expressions. The analysis unit can also prioritize the analysis of expressions submitted in a specific time period. In this way, the analysis priority can be determined based on the submission time of the expression, so that the latest expression can be quickly analyzed. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the expression to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the expressions during analysis. The analysis unit, for example, evaluates the relevance of the expressions. For example, the analysis unit prioritizes analysis of highly relevant expressions and quickly provides results. The analysis unit can also prioritize analysis of important expressions while leaving less relevant expressions for later. The analysis unit can also prioritize analysis of expressions related to a specific topic. As a result, highly relevant expressions can be analyzed preferentially by adjusting the order of analysis based on the relevance of the expressions. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the relevance data of the expressions to the generation AI and cause the generation AI to adjust the order of analysis.

[0092] The generation unit can estimate the user's emotion and adjust the expression method of the words to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technology. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the fluctuation of the heart rate. Next, the generation unit adjusts the expression method of the words to be generated based on the estimated user's emotion. For example, if the user is nervous, simple and intuitive words are generated. Also, if the user is relaxed, words including detailed and rich expressions are generated. Also, if the user is in a hurry, words that are concise and to the point are generated. As a result, more appropriate words can be generated by adjusting the expression method of the words to be generated according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0093] The generation unit can adjust the level of detail of the words to be generated based on the importance of the expression at the time of generation. The generation unit, for example, evaluates the importance of the expression. For example, the generation unit generates detailed and highly accurate words for important expressions. The generation unit can also generate words that are simple and can be generated quickly for general expressions. The generation unit can also generate detailed and highly accurate words for expressions that are important in a specific situation. As a result, detailed words can be generated for important expressions by adjusting the level of detail of the words to be generated based on the importance of the expression. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input importance data of the expression to the generation AI and cause the generation AI to adjust the level of detail of the words.

[0094] The generation unit can apply different generation algorithms depending on the category of the expression at the time of generation. The generation unit, for example, identifies the category of the expression. For example, the generation unit applies an emotion generation algorithm to an emotion expression. The generation unit can also apply a motion generation algorithm to a motion expression. The generation unit can also apply a voice generation algorithm to an onomatopoeia expression. Thereby, by applying an appropriate generation algorithm depending on the category of the expression, the accuracy of the generated words is improved. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the category data of the expression to the generation AI and cause the generation AI to apply the generation algorithm.

[0095] The generation unit can estimate the user's emotion and adjust the length of the words to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technology. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the fluctuation of the heart rate. Next, the generation unit adjusts the length of the words to be generated based on the estimated user's emotion. For example, if the user is nervous, short words that are to the point are generated. Also, if the user is relaxed, detailed and longer words are generated. Also, if the user is in a hurry, concise and short words are generated. In this way, more appropriate words can be generated by adjusting the length of the words to be generated according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0096] The generation unit can determine the priority of the words to be generated based on the submission time of the expression at the time of generation. The generation unit considers, for example, the submission time of the expression. For example, the generation unit quickly generates words for the latest expression. The generation unit can also generate words preferentially for the latest expression while referring to past expressions. The generation unit can also generate words preferentially for expressions submitted in a specific time period. In this way, by determining the priority of the words to be generated based on the submission time of the expression, it is possible to quickly generate words for the latest expression. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the submission time data of the expression to the generation AI and cause the generation AI to execute the determination of the priority of the words.

[0097] The generation unit can adjust the order of words to be generated based on the relevance of the expressions at the time of generation. The generation unit, for example, evaluates the relevance of the expressions. For example, the generation unit quickly generates words for expressions with high relevance. The generation unit can also generate words preferentially for important expressions, leaving expressions with low relevance to be generated later. The generation unit can also generate words preferentially for expressions related to a specific topic. In this way, by adjusting the order of words to be generated based on the relevance of the expressions, words can be quickly generated for expressions with high relevance. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance data of the expressions to the generation AI and cause the generation AI to adjust the order of the words.

[0098] The providing unit can estimate the user's emotion and adjust the method of providing based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. The providing unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the fluctuation of the heart rate. Next, the providing unit adjusts the method of providing based on the estimated user's emotion. For example, if the user is nervous, words are provided in a simple and intuitive manner. Also, if the user is relaxed, words are provided in a manner including detailed information. Also, if the user is in a hurry, words are provided in a quick and concise manner. As a result, by adjusting the method of providing according to the user's emotion, words can be provided in a more appropriate manner. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0099] The providing unit can select the optimal providing method by referring to the user's past reception history at the time of providing. The providing unit, for example, refers to the user's past reception history. For example, the providing unit preferentially suggests a providing method (text, voice, etc.) that the user has used in the past. The providing unit can also predict and suggest a providing method to be used in a specific situation from the user's past reception history. The providing unit can also select the most efficient providing method based on the user's past reception history. As a result, the efficiency of provision is improved by selecting the optimal providing method based on the user's past reception history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's past reception history data to the generation AI and cause the generation AI to select the optimal providing method.

[0100] The providing unit can customize the means of providing based on the user's current situation at the time of providing. The providing unit, for example, takes into account the user's current situation. For example, when the user is moving, the providing unit prioritizes providing by voice to reduce visual burden. In addition, when the user is in a quiet environment, the providing unit can also prioritize providing by text to reduce the impact on the surroundings. In addition, when the user is in a meeting, the providing unit can also prioritize providing by concise text to quickly convey information. In this way, by customizing the means of providing according to the user's current situation, words can be provided in a more appropriate manner. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data to the generating AI and cause the generating AI to execute customization of the means of providing.

[0101] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. The providing unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the fluctuation of the heart rate. Next, the providing unit determines the priority of information to be provided based on the estimated user's emotion. For example, if the user is nervous, important information is provided preferentially. Also, if the user is relaxed, detailed information is provided preferentially. Also, if the user is in a hurry, brief information is provided preferentially. As a result, important information can be provided preferentially by determining the priority of information to be provided according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0102] The providing unit can select the optimal providing method in consideration of the geographical location information of the user at the time of providing. The providing unit, for example, considers the geographical location information of the user. For example, when the user is in a specific area, the providing unit can provide information related to the area preferentially. In addition, when the user is traveling, the providing unit can also provide information related to the travel destination preferentially. In addition, when the user is at home, the providing unit can also provide information related to daily life preferentially. In this way, by considering the geographical location information of the user, highly relevant information can be provided preferentially. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0103] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of provision. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit preferentially suggests a provision method that the user frequently uses on social media. The providing unit can also preferentially provide information related to a specific topic from the user's social media activity. The providing unit can also analyze the user's social media activity and suggest the most relevant provision method. In this way, a highly relevant provision method can be suggested by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's social media activity data to the generating AI and cause the generating AI to execute the suggestion of a means of provision.

[0104] The sign language analysis unit can estimate the user's emotion and adjust the sign language analysis method based on the estimated user's emotion. The sign language analysis unit, for example, estimates the user's emotion. For example, the sign language analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The sign language analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. The sign language analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the sign language analysis unit calculates an emotion score based on the fluctuation of the heart rate. Next, the sign language analysis unit adjusts the sign language analysis method based on the estimated user's emotion. For example, when the user is nervous, a simple sign language analysis method is applied to quickly provide a result. When the user is relaxed, a detailed sign language analysis method is applied to quickly provide a result with high accuracy. When the user is in a hurry, a simple sign language analysis method is applied to quickly provide a result. This allows for more appropriate sign language analysis by adjusting the sign language analysis method according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sign language analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sign language analysis unit can input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0105] The sign language analysis unit can adjust the level of detail of the analysis based on the importance of the sign language when analyzing the sign language. The sign language analysis unit, for example, evaluates the importance of the sign language. For example, the sign language analysis unit performs a detailed analysis on important sign languages ​​to provide highly accurate results. The sign language analysis unit can also perform a simple analysis on general sign languages ​​to provide quick results. The sign language analysis unit can also perform a detailed analysis on important sign languages ​​in a specific situation to provide highly accurate results. As a result, by adjusting the level of detail of the analysis based on the importance of the sign language, it becomes possible to perform a detailed analysis on important sign languages. A part or all of the above-mentioned processing in the sign language analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the sign language analysis unit can input the importance data of the sign language to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0106] The sign language analysis unit can estimate the user's emotion and determine the analysis priority of the sign language based on the estimated user's emotion. The sign language analysis unit, for example, estimates the user's emotion. For example, the sign language analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The sign language analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. The sign language analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the sign language analysis unit calculates an emotion score based on the fluctuation of the heart rate. Next, the sign language analysis unit determines the analysis priority of the sign language based on the estimated user's emotion. For example, if the user is nervous, important sign language is analyzed with priority. Also, if the user is relaxed, detailed sign language is analyzed with priority. Also, if the user is in a hurry, concise sign language is analyzed with priority. In this way, important sign language can be analyzed with priority by determining the analysis priority of the sign language according to the user's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the sign language analysis unit may be performed, for example, using an AI or may be performed without using an AI. For example, the sign language analysis unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0107] The sign language analysis unit can determine the priority of analysis based on the time of submission of the sign language when analyzing the sign language. The sign language analysis unit, for example, takes into consideration the time of submission of the sign language. For example, the sign language analysis unit prioritizes analysis of the latest sign language and quickly provides the result. The sign language analysis unit can also prioritize analysis of the latest sign language while referring to past sign languages. The sign language analysis unit can also prioritize analysis of sign language submitted in a specific time period. In this way, the latest sign language can be quickly analyzed by determining the priority of analysis based on the time of submission of the sign language. A part or all of the above-mentioned processing in the sign language analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the sign language analysis unit can input data on the time of submission of the sign language to the generation AI and cause the generation AI to execute the determination of the priority of analysis.

[0108] The translation unit can estimate the user's emotion and adjust the translation method based on the estimated user's emotion. The translation unit, for example, estimates the user's emotion. For example, the translation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The translation unit can also record the user's voice and estimate the emotion using a voice analysis technology. The translation unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the translation unit calculates an emotion score based on the fluctuation of the heart rate. Next, the translation unit adjusts the translation method based on the estimated user's emotion. For example, if the user is nervous, a simple and intuitive translation method is applied. Also, if the user is relaxed, a translation method including detailed and rich expressions is applied. Also, if the user is in a hurry, a translation method that is concise and to the point is applied. This allows for more appropriate translation by adjusting the translation method according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the translation unit may be performed, for example, using AI, or may be performed without using AI. For example, the translation unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0109] The translation unit can adjust the level of detail of the translation based on the importance of the expression during translation. The translation unit, for example, evaluates the importance of the expression. For example, the translation unit performs a detailed and accurate translation for an important expression. The translation unit can also apply a method that can translate general expressions simply and quickly. The translation unit can also perform a detailed and accurate translation for an important expression in a specific situation. As a result, by adjusting the level of detail of the translation based on the importance of the expression, detailed translation is possible for an important expression. A part or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the importance data of the expression to the generation AI and cause the generation AI to adjust the level of detail of the translation.

[0110] The translation unit can estimate the user's emotion and determine the priority of translation based on the estimated user's emotion. The translation unit, for example, estimates the user's emotion. For example, the translation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The translation unit can also record the user's voice and estimate the emotion using a voice analysis technology. The translation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the translation unit calculates an emotion score based on the fluctuation of the heart rate. Next, the translation unit determines the priority of translation based on the estimated user's emotion. For example, if the user is nervous, important expressions are translated with priority. Also, if the user is relaxed, detailed expressions are translated with priority. Also, if the user is in a hurry, concise expressions are translated with priority. In this way, important expressions can be translated with priority by determining the priority of translation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the translation unit may be performed, for example, using AI, or may be performed without using AI. For example, the translation unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0111] The translation unit can determine the priority of translation based on the submission time of the expression during translation. The translation unit, for example, takes into account the submission time of the expression. For example, the translation unit translates the latest expression with priority and quickly provides the result. The translation unit can also translate the latest expression with priority while referring to past expressions. The translation unit can also translate expressions submitted in a specific time period with priority. In this way, the latest expression can be quickly translated by determining the priority of translation based on the submission time of the expression. A part or all of the above-mentioned processing in the translation unit may be performed, for example, using AI, or may be performed without using AI. For example, the translation unit can input the submission time data of the expression to the generation AI and cause the generation AI to execute the determination of the translation priority.

[0112] The support unit can estimate the user's emotion and adjust the method of support based on the estimated user's emotion. The support unit, for example, estimates the user's emotion. For example, the support unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The support unit can also record the user's voice and estimate the emotion using a voice analysis technology. The support unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the support unit calculates an emotion score based on the fluctuation of the heart rate. Next, the support unit adjusts the method of support based on the estimated user's emotion. For example, when the user is nervous, a simple and intuitive support method is provided. When the user is relaxed, a support method including detailed information is provided. When the user is in a hurry, a quick and concise support method is provided. This enables more appropriate support by adjusting the method of support according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0113] The support unit can select the optimal support method by referring to the user's past support history at the time of support. The support unit, for example, refers to the user's past support history. For example, the support unit preferentially suggests a support method that the user has used in the past. The support unit can also predict and suggest a support method to be used in a specific situation from the user's past support history. The support unit can also select the most efficient support method based on the user's past support history. As a result, the efficiency of support is improved by selecting the optimal support method based on the user's past support history. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI or may be performed without using AI. For example, the support unit can input the user's past support history data to the generation AI and cause the generation AI to select the optimal support method.

[0114] The support unit can estimate the user's emotion and determine the priority of the support based on the estimated user's emotion. The support unit, for example, estimates the user's emotion. For example, the support unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The support unit can also record the user's voice and estimate the emotion using a voice analysis technology. The support unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the support unit calculates an emotion score based on the fluctuation of the heart rate. Next, the support unit determines the priority of the support based on the estimated user's emotion. For example, if the user is nervous, important support is provided preferentially. Also, if the user is relaxed, detailed support is provided preferentially. Also, if the user is in a hurry, brief support is provided preferentially. In this way, important support can be provided preferentially by determining the priority of the support according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0115] The support unit can select the optimal support method in consideration of the geographical location information of the user at the time of support. The support unit considers, for example, the geographical location information of the user. For example, when the user is in a specific area, the support unit preferentially provides a support method related to the area. In addition, when the user is traveling, the support unit can also preferentially provide a support method related to the travel destination. In addition, when the user is at home, the support unit can also preferentially provide a support method related to daily life. In this way, by considering the geographical location information of the user, it is possible to preferentially provide a highly relevant support method. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select the optimal support method.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] When accepting an expression from a user, the sensory verbalization support system can analyze the user's past expression history and suggest an optimal acceptance method. For example, the acceptance unit preferentially suggests an expression method (words, onomatopoeia, movement, etc.) that the user has used frequently in the past. The acceptance unit can also predict and suggest an expression method to be used in a specific situation from the user's past expression history. This allows the acceptance of expressions to be made more efficient by selecting an optimal acceptance method based on the user's past expression history. A part or all of the above-mentioned processing in the acceptance unit may be performed, for example, using AI or may be performed without using AI. For example, the acceptance unit can input the user's past expression history data to the generation AI and cause the generation AI to select an optimal acceptance method.

[0118] The sensory verbalization support system can analyze sign language movement data in real time in the sign language analysis unit and instantly convert the user's intention into text. For example, the sign language analysis unit digitizes the user's hand movements using motion capture technology and analyzes the contents in real time. The sign language analysis unit can also input the user's sign language movement data into a generation AI and instantly convert it into text data. This allows expressions using sign language to be appropriately converted into words in real time. Some or all of the above-mentioned processing in the sign language analysis unit may be performed, for example, using AI or may be performed without using AI.

[0119] The sensory verbalization support system can estimate the user's emotions in the translation unit and adjust the translation method based on the estimated emotions. For example, the translation unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. The translation unit can also record the user's voice and estimate the emotions using voice analysis technology. Next, the translation unit adjusts the translation method based on the estimated emotions. For example, if the user is nervous, a simple and intuitive translation method is applied, and if the user is relaxed, a translation method including detailed and rich expressions is applied. This allows for more appropriate translation by adjusting the translation method according to the user's emotions.

[0120] The sensory verbalization support system can select the optimal support method in the support unit by taking into account the geographical location information of the user. For example, when the user is in a specific area, the support unit can provide a support method related to that area with priority. In addition, when the user is traveling, the support unit can also provide a support method related to the travel destination with priority. In this way, by taking into account the geographical location information of the user, it is possible to provide a highly relevant support method with priority. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select the optimal support method.

[0121] The sensory verbalization support system can estimate the user's emotion in the analysis unit and adjust the analysis method based on the estimated emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. Next, the analysis unit adjusts the analysis method based on the estimated emotion. For example, if the user is nervous, a simple analysis method is applied to provide a quick result. Also, if the user is relaxed, a detailed analysis method is applied to provide a highly accurate result. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotion.

[0122] The sensory verbalization support system can adjust the expression method of the generated words based on the user's emotions in the generation unit. For example, the generation unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using a voice analysis technology. Next, the generation unit adjusts the expression method of the generated words based on the estimated emotions. For example, when the user is nervous, simple and intuitive words are generated, and when the user is relaxed, words including detailed and rich expressions are generated. In this way, more appropriate words can be generated by adjusting the expression method of the generated words according to the user's emotions.

[0123] The sensory verbalization support system can perform filtering in the reception unit based on the user's current situation and environment. For example, when the user is in a noisy environment, the reception unit suppresses voice input and prioritizes text input. Also, when the user is in a quiet environment, the reception unit can prioritize voice input and quickly accept expressions. Also, when the user is on the move, the reception unit can suggest a simple input method and smoothly accept expressions. This makes it possible to accept more appropriate expressions by adjusting the acceptance of expressions according to the user's situation and environment.

[0124] The sensory verbalization support system can estimate the user's emotion in the providing unit and adjust the method of providing based on the estimated emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. Next, the providing unit adjusts the method of providing based on the estimated emotion. For example, if the user is nervous, words are provided in a simple and intuitive manner, and if the user is relaxed, words are provided in a manner including detailed information. In this way, by adjusting the method of providing according to the user's emotion, words can be provided in a more appropriate manner.

[0125] The sensory verbalization support system can analyze the social media activity of the user in the providing unit and provide related expressions preferentially. For example, the providing unit can provide expressions frequently used by the user on social media preferentially. The providing unit can also provide expressions related to a specific topic from the social media activity of the user preferentially. In this way, by analyzing the social media activity of the user, highly relevant expressions can be provided preferentially. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the social media activity data of the user to the generating AI and cause the generating AI to select related expressions.

[0126] The sensory verbalization support system can estimate the user's emotion in the sign language analysis unit and adjust the sign language analysis method based on the estimated emotion. For example, the sign language analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The sign language analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. Next, the sign language analysis unit adjusts the sign language analysis method based on the estimated emotion. For example, if the user is nervous, a simple sign language analysis method is applied to provide a quick result. Also, if the user is relaxed, a detailed sign language analysis method is applied to provide a highly accurate result. This allows for more appropriate sign language analysis by adjusting the sign language analysis method according to the user's emotion.

[0127] The process flow of the second embodiment will be briefly described below.

[0128] Step 1: The reception unit receives a user's expression. The user's expression may include, but is not limited to, words, onomatopoeia, and movements. For example, the reception unit may receive an expression in which the user moves his / her hand while saying, "Hmm, it feels like this." Step 2: The analysis unit uses AI to analyze the expression received by the reception unit. The analysis unit understands sensations from the user's words, onomatopoeia, and movements. For example, the analysis unit extracts information from the user's hand movements to generate words such as "movements that draw large circles." Step 3: The generation unit uses AI to generate appropriate words based on the expressions analyzed by the analysis unit. For example, the generation unit generates words such as "a large circular movement" from the user's hand movements. The generation unit can use a generation AI (for example, a text generation AI or a multimodal generation AI) to generate appropriate words corresponding to the sensation. Step 4: The providing unit communicates the words generated by the generating unit to others. For example, the providing unit displays the generated words as text. The providing unit can also output the generated words as audio.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0131] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, sign language analysis unit, translation unit, and support unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a user's expression. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the expression received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate words based on the analyzed expression. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and conveys the generated words to others. The sign language analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes sign language. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a translation function. The support unit is realized, for example, by the control unit 46A of the smart device 14 and provides support for people with disabilities. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0140] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, sign language analysis unit, translation unit, and support unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a user's expression. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the expression received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate words based on the analyzed expression. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and conveys the generated words to others. The sign language analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes sign language. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a translation function. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides support for people with disabilities. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] 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.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, sign language analysis unit, translation unit, and support unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives a user's expression. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the expression received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate words based on the analyzed expression. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and conveys the generated words to others. The sign language analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes sign language. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a translation function. The support unit is realized, for example, by the control unit 46A of the headset type terminal 314, and supports a person with a disability. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0173] 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.

[0174] 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.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0176] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, sign language analysis unit, translation unit, and support unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives a user's expression. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the expression received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate words based on the analyzed expression. The provision unit is realized, for example, by the control unit 46A of the robot 414 and conveys the generated words to others. The sign language analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes sign language. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a translation function. The support unit is realized, for example, by the control unit 46A of the robot 414 and supports people with disabilities. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0183] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0185] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0187] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0189] 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.

[0190] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0191] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0192] 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.

[0193] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0194] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0195] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0196] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0198] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0200] (Appendix 1) a reception unit for receiving an expression from a user; an analysis unit that analyzes the expression accepted by the acceptance unit; a generation unit that generates appropriate words based on the expressions analyzed by the analysis unit; A provision unit that conveys the words generated by the generation unit to others. A system characterized by: (Appendix 2) Equipped with a sign language analysis unit that analyzes sign language 2. The system of claim 1, (Appendix 3) Equipped with a translation section that provides translation functions 2. The system of claim 1, (Appendix 4) Have a support department to provide support to people with disabilities 2. The system of claim 1, (Appendix 5) The analysis unit is Analyze the user's senses from their words, onomatopoeia, and movements 2. The system of claim 1, (Appendix 6) The generation unit is Generate appropriate words to correspond to the sensations 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and adjust a method of accepting an expression based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past expression history and select the appropriate reception method 2. The system of claim 1, (Appendix 9) The reception unit is As expressions are received, they are filtered based on the user's current situation or environment 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and determine the priority of expressions to be accepted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When accepting expressions, the system takes into account the user's geographic location information and prioritizes expressions that are highly relevant. 2. The system of claim 1, (Appendix 12) The reception unit is When accepting an expression, analyze the user's social media activity and accept related expressions. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust analysis methods based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail of the analysis based on the importance of the expression 2. The system of claim 1, (Appendix 15) The analysis unit is During parsing, apply different parsing algorithms depending on the category of the expression 2. The system of claim 1, (Appendix 16) The analysis unit is Estimate user emotions and determine analysis priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During parsing, determine parsing priorities based on when the expression was submitted 2. The system of claim 1, (Appendix 18) The analysis unit is At parse time, adjust the order of parsing based on the relevance of expressions 2. The system of claim 1, (Appendix 19) The generation unit is Estimate the user's emotions and adjust the way words are expressed based on the estimated user emotions. 2. The system of claim 1, (Appendix 20) The generation unit is During generation, the level of detail of the generated words is adjusted based on the importance of the expression. 2. The system of claim 1, (Appendix 21) The generation unit is During generation, different generation algorithms are applied depending on the category of the expression. 2. The system of claim 1, (Appendix 22) The generation unit is Estimate the user's emotions and adjust the length of generated words based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The generation unit is During generation, prioritize which terms to generate based on when the expressions were submitted 2. The system of claim 1, (Appendix 24) The generation unit is During generation, the order of words is adjusted based on the relevance of the expressions. 2. The system of claim 1, (Appendix 25) The providing unit is Inferring user emotion and adjusting the serving method based on the inferred user emotion 2. The system of claim 1, (Appendix 26) The providing unit is When providing the service, the optimal delivery method is selected by referring to the user's past reception history. 2. The system of claim 1, (Appendix 27) The providing unit is At the time of delivery, customize the delivery vehicle based on the user's current situation 2. The system of claim 1, (Appendix 28) The providing unit is Estimate user emotions and determine priority of services to be provided based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, the optimal method of providing the information is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The providing unit is At the time of delivery, we analyze your social media activity to suggest delivery methods. 2. The system of claim 1, (Appendix 31) The sign language analysis unit is Estimate the user's emotions and adjust the sign language analysis method based on the estimated user's emotions. 3. The system of claim 2, (Appendix 32) The sign language analysis unit is When analyzing sign language, adjust the level of analysis detail based on the importance of the sign language. 3. The system of claim 2, (Appendix 33) The sign language analysis unit is Estimate the user's emotion and determine the analysis priority of sign language based on the estimated user's emotion. 3. The system of claim 2, (Appendix 34) The sign language analysis unit is When analyzing sign language, prioritize analysis based on when the sign language was submitted. 3. The system of claim 2, (Appendix 35) The translation unit is Estimate user emotions and adjust translation methods based on the estimated user emotions 4. The system of claim 3. (Appendix 36) The translation unit is During translation, adjust the level of detail in the translation based on the importance of the expression 4. The system of claim 3. (Appendix 37) The translation unit is Estimate user sentiment and prioritize translations based on the estimated user sentiment 4. The system of claim 3. (Appendix 38) The translation unit is During translation, prioritize translations based on when expressions were submitted 4. The system of claim 3. (Appendix 39) The support unit includes: Estimating a user's emotions and adjusting the method of assistance based on the estimated user's emotions 5. The system of claim 4. (Appendix 40) The support unit includes: When providing support, the system selects the most appropriate support method by referring to the user's past support history. 5. The system of claim 4. (Appendix 41) The support unit includes: Estimating a user's emotions and determining the priority of assistance based on the estimated user's emotions 5. The system of claim 4. (Appendix 42) The support unit includes: When providing assistance, the system selects the optimal assistance method taking into account the user's geographic location information. 5. The system of claim 4. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving an expression from a user; an analysis unit that analyzes the expression accepted by the acceptance unit; a generation unit that generates appropriate words based on the expressions analyzed by the analysis unit; A provision unit that conveys the words generated by the generation unit to others. A system characterized by:

2. Equipped with a sign language analysis unit that analyzes sign language 2. The system of claim 1 .

3. Equipped with a translation section that provides translation functions 2. The system of claim 1 .

4. Have a support department to provide support to people with disabilities 2. The system of claim 1 .

5. The analysis unit is Analyze the user's senses from their words, onomatopoeia, and movements 2. The system of claim 1 .

6. The generation unit is Generate appropriate words to correspond to the sensations 2. The system of claim 1 .

7. The reception unit is To estimate a user's emotion and adjust a method of accepting an expression based on the estimated user's emotion.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past expression history and select the appropriate reception method 2. The system of claim 1 .

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