System

Abstract

To provide a system capable of allowing people with intellectual and developmental disabilities to perform verbal communication.SOLUTION: A system in an embodiment includes an analysis unit, a generation unit, a reception unit, and a conversion unit. The analysis unit analyzes an utterance of a user. The generation unit generates an appropriate sentence based on the speech content analyzed by the analysis unit. The reception unit receives an illustration or icon operation. The conversion unit converts the content received by the reception unit into text. The conversion unit converts the contents input by a healthy person into an illustration or animation.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] Conventional technology has the problem that it is difficult for people with intellectual disabilities or developmental disabilities to communicate smoothly with language.

[0005] The system according to the embodiment aims to enable people with intellectual disabilities or developmental disabilities to smoothly carry out verbal communication. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, a reception unit, and a conversion unit. The analysis unit analyzes the user's speech. The generation unit generates appropriate sentences based on the speech content analyzed by the analysis unit. The reception unit receives operations on illustrations or icons. The conversion unit converts the content received by the reception unit into sentences. The conversion unit converts the content input by a healthy person into an illustration or animation. Effect of the Invention

[0007] The system according to the embodiment enables people with intellectual disabilities or developmental disabilities to smoothly carry out verbal communication. [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 a 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 communication assistance system according to the embodiment of the present invention is a system that complements incomplete speech of people with immature language skills due to intellectual disabilities or developmental disabilities, and converts content input by operating illustrations or icons into text. This system can also make content input by healthy people easier to understand by illustrating or animating it. For example, when a user with intellectual disabilities or developmental disabilities makes an incomplete speech, the system analyzes the speech and complements it into an appropriate text. For example, if the user says "I'm hungry," the system converts it into a complete text such as "I'm hungry, so I want to eat." Next, when the user operates illustrations or icons to input, the system analyzes the content and converts it into text. For example, if the user selects a food icon and then selects the "I'm hungry" icon, the system generates a text such as "I'm hungry, so I want to eat." Furthermore, the system analyzes text input by healthy people and converts it into illustrations or animations. For example, if the user inputs "Let's go to the park today," the system generates an illustration or animation of a park, making it easier for the intellectually disabled to understand the content. This system is a tool for facilitating communication between people with intellectual disabilities or developmental disabilities and their families, and deepening their understanding. For example, if a family member inputs "I'm going to the hospital today," the system will generate illustrations and animations of the hospital, making it easier for the intellectually disabled to understand the content. The system is installed on a smartphone and is designed to be easy for users to operate. For example, input is completed by simply tapping an icon, and text and illustrations are generated. Voice input is also possible, and the system automatically analyzes what the user says and produces appropriate output. It is expected that this system will improve communication between people with intellectual and developmental disabilities and their families, making daily life smoother. As a result, the communication assistance system can facilitate communication between people with intellectual and developmental disabilities and their families, and deepen understanding.

[0029] The communication assistance system according to the embodiment includes an analysis unit, a generation unit, a reception unit, a conversion unit, and a conversion unit. The analysis unit analyzes the user's speech. The user's speech includes, for example, incomplete speech and fragmentary words, but is not limited to such examples. The analysis unit converts the speech into text data using, for example, a voice recognition technology, and analyzes the content. The analysis unit can also use natural language processing technology to understand the context and meaning of the speech. For example, the analysis unit converts the user's speech into text data using a voice recognition technology, and analyzes the text data using a natural language processing technology. The generation unit generates appropriate sentences based on the speech content analyzed by the analysis unit. The generation unit generates grammatically correct sentences based on the analyzed speech content using, for example, a text generation AI (for example, LLM). The generation unit can also use semantic analysis technology to confirm that the generated sentences are semantically appropriate. For example, the generation unit generates sentences based on the speech content analyzed using a text generation AI, and confirms that the sentences are semantically appropriate. The reception unit receives an operation of an illustration or an icon. The reception unit receives an operation of selecting an illustration or an icon by a user using, for example, a touch screen. The reception unit can also use image recognition technology to analyze the content of the illustration or icon selected by the user. For example, the reception unit analyzes the illustration or icon selected by the user using the touch screen using image recognition technology. The conversion unit converts the content received by the reception unit into text. The conversion unit converts the content of the illustration or icon analyzed using, for example, image recognition technology into text data, and generates a text based on the text data. The conversion unit can also use grammar analysis technology to confirm that the generated text is grammatically correct. For example, the conversion unit converts the content of the illustration or icon analyzed using image recognition technology into text data, generates a text based on the text data, and confirms that the text is grammatically correct using grammar analysis technology. The conversion unit converts the content input by the healthy person into an illustration or animation. The conversion unit uses, for example, image generation technology or animation generation technology to generate an illustration or animation based on text data.The conversion unit can also use visual analysis technology to confirm that the generated illustrations and animations are visually easy to understand. For example, the conversion unit generates illustrations and animations based on text data, and uses visual analysis technology to confirm that they are visually easy to understand. As a result, the communication assistance system according to the embodiment can analyze the user's utterances and operation contents and convert them into appropriate sentences and illustrations to assist communication.

[0030] The analysis unit analyzes the user's speech. The user's speech includes, for example, incomplete speech and fragmentary words, but is not limited to such examples. The analysis unit converts the speech into text data using, for example, a speech recognition technology, and analyzes the content. Specifically, a speech recognition model using deep learning is used as the speech recognition technology. This model realizes highly accurate speech recognition by learning a large amount of speech data. When the user's speech is input, the analysis unit first converts the voice signal into digital data, and then converts it into text data using the speech recognition model. This text data accurately reflects the content of the user's speech, and the analysis unit then analyzes this text data. The analysis unit can also use natural language processing technology to understand the context and meaning of the speech. For example, natural language processing technology includes topic modeling, sentiment analysis, and context analysis. As a result, the analysis unit understands the intention and emotion of the user's speech and provides basic information for generating an appropriate response. Furthermore, the analysis unit extracts keywords and important phrases contained in the user's speech, and grasps the overall meaning of the speech based on them. This enables the analysis unit to understand the situation and purpose of the user's speech and provide the information necessary for the next processing step.

[0031] The generation unit generates appropriate sentences based on the speech content analyzed by the analysis unit. The generation unit generates grammatically correct sentences based on the analyzed speech content, for example, using a text generation AI (for example, LLM). Specifically, the generation unit receives text data provided by the analysis unit as an input, and generates natural sentences using the text generation AI. This text generation AI is based on a large-scale language model and can generate sentences that correspond to various contexts and situations. The generation unit can also use semantic analysis technology to confirm that the generated sentences are semantically appropriate. For example, the generation unit uses semantic analysis technology to confirm whether the generated sentences match the user's speech content and are appropriate for the context. This allows the generation unit to generate appropriate sentences that reflect the user's intentions and emotions. Furthermore, the generation unit can also use grammar analysis technology to confirm that the generated sentences are grammatically correct. This allows the generation unit to provide the user with natural and easy-to-understand sentences. The generation unit can also provide a feedback loop to confirm whether the generated sentences are appropriate for the user's needs and situation. For example, the generated sentences are presented to the user, and the sentences can be modified based on feedback from the user. This enables the generation unit to provide a more appropriate response to the user.

[0032] The reception unit receives operations for selecting illustrations and icons. The reception unit receives operations for selecting illustrations and icons by the user using, for example, a touch screen. Specifically, the touch screen is equipped with a highly sensitive touch sensor and can accurately detect operations using the user's finger or stylus pen. When the user selects an illustration or icon on the touch screen, the selection information is transmitted to the reception unit. The reception unit can also use image recognition technology to analyze the contents of the illustration or icon selected by the user. For example, the reception unit uses image recognition technology to analyze the illustration or icon selected by the user using the touch screen. As the image recognition technology, a deep learning model such as a convolutional neural network (CNN) is often used. This allows the reception unit to accurately recognize the contents of the illustration or icon selected by the user and provide the information to the next processing step. Furthermore, the reception unit can learn the user's operation history and selection patterns and grasp the user's preferences and tendencies. This allows the reception unit to suggest more appropriate illustrations and icons to the user. For example, the efficiency of operation can be improved by preferentially displaying illustrations and icons that the user frequently selects based on the past operation history.

[0033] The conversion unit converts the content received by the reception unit into text. The conversion unit converts the content of an illustration or icon analyzed using, for example, image recognition technology into text data, and generates a text based on the text data. Specifically, the conversion unit analyzes the content of the illustration or icon using image recognition technology, and outputs the result as text data. This text data accurately reflects the meaning and content of the illustration or icon, and the conversion unit then generates a text based on this text data. The conversion unit can also use grammar analysis technology to confirm that the generated text is grammatically correct. For example, the conversion unit uses grammar analysis technology to confirm that the generated text is grammatically correct. This allows the conversion unit to provide the user with natural and easy-to-understand text. Furthermore, the conversion unit converts the content input by the able-bodied person into illustrations or animations. The conversion unit uses, for example, image generation technology or animation generation technology to generate illustrations or animations based on text data. Specifically, the conversion unit receives text data as input, and generates a corresponding illustration using image generation technology. By using animation generation technology, dynamic animations based on text data can also be generated. The conversion unit may also use visual analysis techniques to confirm that the generated illustrations and animations are visually easy to understand. For example, the conversion unit uses visual analysis techniques to confirm that the generated illustrations and animations are visually easy to understand. This allows the conversion unit to provide the user with visually easy-to-understand illustrations and animations.

[0034] The analysis unit can improve the analysis accuracy by referring to the user's past speech history when analyzing the utterance. The analysis unit can improve the analysis accuracy by referring to, for example, specific phrases or expressions used by the user in the past. The analysis unit can also perform analysis according to a specific context or situation from the user's past speech history. Furthermore, the analysis unit can also refer to utterances that the user has misunderstood in the past and adjust the analysis to avoid similar misunderstandings. As a result, the analysis accuracy is improved by referring to the past speech history. 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 past speech history data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0035] The analysis unit can perform the analysis while taking into consideration the user's speech speed and volume when analyzing the speech. For example, when the user speaks quickly, the analysis unit performs the analysis while taking into consideration the speech speed, and extracts accurate content. In addition, when the user speaks softly, the analysis unit can perform the analysis while taking into consideration the volume, and can also complement the parts that are difficult to hear. Furthermore, when the user speaks slowly, the analysis unit can perform the analysis according to the speech speed and generate natural sentences. As a result, accurate analysis is possible by taking into consideration the speech speed and volume. 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 user's speech speed and volume data to the generation AI, and cause the generation AI to execute the analysis.

[0036] When analyzing an utterance, the analysis unit can reflect a language or dialect specific to a region in the analysis by taking into account the geographical location information of the user. For example, when the user is in a specific region, the analysis unit performs the analysis by taking into account the dialect specific to that region. In addition, when the user is traveling, the analysis unit can also reflect the language specific to the region visited in the analysis. Furthermore, when the user makes an utterance from a different region, the analysis unit can also perform the analysis by taking into account the language characteristics of each region. This makes it possible to perform a more natural analysis by taking into account the language or dialect specific to the region. 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 geographical location information of the user to the generation AI and cause the generation AI to execute the analysis of the language or dialect specific to the region.

[0037] The analysis unit can analyze the user's social media activities and analyze the related speech content when analyzing the speech. For example, the analysis unit reflects phrases frequently used by the user on social media in the analysis. The analysis unit can also reflect specific interests and concerns from the user's social media activities in the analysis. Furthermore, the analysis unit can analyze the speech content based on information shared by the user on social media. In this way, the analysis of social media activities improves the analysis accuracy of the related speech content. 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 social media data to the generation AI and cause the generation AI to execute the analysis of the related speech content.

[0038] The generation unit can adjust the level of detail of the sentence based on the importance of the spoken content when generating the sentence. For example, in the case of important spoken content, the generation unit generates a sentence including a detailed explanation. In addition, in the case of general spoken content, the generation unit can also generate a concise sentence. Furthermore, in the case of urgent spoken content, the generation unit can also generate a sentence that focuses on the main points so that it can be understood quickly. In this way, by adjusting the level of detail of the sentence based on the importance of the spoken content, a sentence with an appropriate amount of information is generated. 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 spoken content to the generation AI and cause the generation AI to adjust the level of detail of the sentence.

[0039] The generation unit can apply different generation algorithms depending on the category of the utterance content when generating the sentence. For example, in the case of daily conversation, the generation unit applies an algorithm that generates natural conversational sentences. In addition, in the case of specialized content, the generation unit can also apply an algorithm that generates sentences including technical terms. Furthermore, in the case of emotional content, the generation unit can also apply an algorithm for expressing emotions. In this way, by applying an appropriate generation algorithm depending on the category of the utterance content, more natural sentences are generated. 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 category data of the utterance content to the generation AI and cause the generation AI to apply the generation algorithm.

[0040] The generation unit can determine the priority of generation based on the submission time of the speech content when generating the sentence. For example, in the case of urgent speech content, the generation unit generates the sentence with the highest priority. In addition, in the case of regular speech content, the generation unit can also generate the sentence with normal priority. Furthermore, in the case of speech content related to future plans, the generation unit can generate the sentence later. In this way, by determining the priority of generation based on the submission time of the speech content, it is possible to quickly process content with high urgency. 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 submission time data of the speech content to the generation AI and cause the generation AI to execute the determination of the priority of generation.

[0041] The generation unit can adjust the order of the generated sentences based on the relevance of the spoken contents when generating the sentences. For example, the generation unit arranges important spoken contents first, followed by related information. The generation unit can also arrange the spoken contents in chronological order to create a natural flow. Furthermore, the generation unit can also arrange highly relevant information preferentially based on the user's interest. In this way, the order of the sentences is adjusted based on the relevance of the spoken contents, thereby generating sentences with a natural flow. 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 relevance data of the spoken contents to the generation AI and cause the generation AI to adjust the order of the sentences.

[0042] The reception unit can select the optimal operation method by referring to the user's past operation history when receiving an operation of an illustration or an icon. For example, the reception unit preferentially suggests an operation method that the user has frequently used in the past. The reception unit can also select the optimal operation method according to a specific situation from the user's past operation history. Furthermore, the reception unit can also suggest an operation method to avoid a similar operation error by referring to the content of a user's past operation error. In this way, the optimal operation method is selected by referring to the past operation history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's past operation history data to the generation AI and cause the generation AI to select the optimal operation method.

[0043] The reception unit can filter the operation contents based on the user's current field of interest when receiving the operation of the illustration or icon. The reception unit, for example, preferentially displays operation contents related to a theme in which the user is currently interested. The reception unit can also suggest highly relevant operation contents based on the user's current field of interest. Furthermore, the reception unit can filter and not display operation contents related to a theme in which the user is not currently interested. This makes it possible to perform highly relevant operations by filtering the operation contents based on the current field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current field of interest data to the generation AI and cause the generation AI to execute filtering of the operation contents.

[0044] The reception unit can preferentially receive highly relevant operation contents in consideration of the geographical location information of the user when receiving an operation of an illustration or an icon. For example, when the user is in a specific area, the reception unit preferentially receives operation contents related to that area. In addition, when the user is traveling, the reception unit can also preferentially receive operation contents related to the area of ​​the visit. Furthermore, when the user performs an operation from a different area, the reception unit can also preferentially receive operation contents with high relevance to each area. In this way, highly relevant operation contents are preferentially received by considering the geographical location information. 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 execute the reception of highly relevant operation contents.

[0045] The reception unit can analyze the user's social media activity and receive related operation contents when receiving the operation of the illustration or icon. For example, the reception unit preferentially receives operation contents that the user frequently uses on social media. The reception unit can also preferentially receive operation contents related to specific interests and concerns from the user's social media activity. Furthermore, the reception unit can also preferentially receive related operation contents based on information shared by the user on social media. In this way, related operation contents are preferentially received by analyzing the social media activity. 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 social media data to the generation AI and cause the generation AI to execute the reception of related operation contents.

[0046] The conversion unit can adjust the level of detail of the conversion based on the importance of the received content at the time of conversion. For example, in the case of important received content, the conversion unit performs conversion including detailed explanation. In addition, in the case of general received content, the conversion unit can also perform concise conversion. Furthermore, in the case of urgent received content, the conversion unit can also perform conversion that focuses on the main points so that it can be understood quickly. As a result, by adjusting the level of detail of the conversion based on the importance of the received content, it is possible to convert an appropriate amount of information. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the importance data of the received content to the generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0047] The conversion unit can apply different conversion algorithms depending on the category of the received content at the time of conversion. For example, in the case of daily conversation, the conversion unit applies an algorithm that converts the received content into a natural conversation sentence. In addition, in the case of specialized content, the conversion unit can also apply an algorithm that performs conversion including technical terms. Furthermore, in the case of emotional content, the conversion unit can also apply a conversion algorithm for expressing emotions. This allows for more natural conversion by applying an appropriate conversion algorithm depending on the category of the received content. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input category data of the received content to the generation AI and cause the generation AI to apply the conversion algorithm.

[0048] The conversion unit can determine the priority of conversion based on the submission time of the reception contents at the time of conversion. For example, in the case of urgent reception contents, the conversion unit performs conversion with the highest priority. In addition, in the case of regular reception contents, the conversion unit can also perform conversion with normal priority. Furthermore, in the case of reception contents related to future plans, the conversion unit can postpone conversion. In this way, by determining the priority of conversion based on the submission time of the reception contents, contents with high urgency can be processed quickly. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the submission time data of the reception contents to the generation AI and have the generation AI execute the determination of the priority of conversion.

[0049] The conversion unit can adjust the order of the contents to be converted based on the relevance of the reception contents at the time of conversion. For example, the conversion unit arranges important reception contents first, followed by related information. The conversion unit can also arrange reception contents in chronological order to create a natural flow. Furthermore, the conversion unit can also arrange highly related information preferentially based on the user's interest. This allows for a natural flow of conversion by adjusting the order of the converted contents based on the relevance of the reception contents. A part or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can input the relevance data of the reception contents to the generation AI and cause the generation AI to adjust the order of the converted contents.

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

[0051] When analyzing the contents of a user's speech, the analysis unit can adjust the analysis method taking into account the user's health condition. For example, if the user has a cold, the speech may become unclear, so the analysis unit can increase the sensitivity of the voice recognition. Also, if the user is tired, the speech speed may become slow, so the analysis unit can perform analysis according to the speech speed. Furthermore, if the user is stressed, the speech content may become fragmented, so the analysis unit can perform analysis with emphasis on the context. This makes it possible to perform analysis according to the user's health condition, and more accurate analysis results can be obtained.

[0052] When analyzing the contents of a user's speech, the analysis unit can improve the accuracy of the analysis by referring to the user's past speech history. For example, the analysis unit can improve the accuracy of the analysis by referring to specific phrases or expressions used by the user in the past. In addition, the analysis unit can perform analysis according to a specific context or situation from the user's past speech history. Furthermore, the analysis unit can adjust the analysis to avoid similar misunderstandings by referring to utterances that the user has misunderstood in the past. In this way, the analysis accuracy is improved by referring to the past speech history.

[0053] When analyzing the contents of a user's speech, the analysis unit can perform the analysis taking into account the user's speech speed and volume. For example, if the user speaks quickly, the analysis is performed taking into account the speech speed, and accurate content is extracted. Also, if the user speaks softly, the analysis can be performed taking into account the volume, and difficult-to-hear parts can be supplemented. Furthermore, if the user speaks slowly, the analysis can be performed according to the speech speed, and natural sentences can be generated. In this way, accurate analysis is possible by taking into account the speech speed and volume.

[0054] When analyzing the contents of a user's speech, the analysis unit can take into account the user's geographical location information and reflect the language or dialect specific to the region in the analysis. For example, when a user is in a specific region, the analysis is performed taking into account the dialect specific to that region. Also, when a user is traveling, the language specific to the region visited can be reflected in the analysis. Furthermore, when a user speaks from different regions, the analysis can be performed taking into account the linguistic characteristics of each region. This allows for a more natural analysis by taking into account the language or dialect specific to the region.

[0055] When analyzing the user's speech content, the analysis unit can analyze the user's social media activity and analyze the related speech content. For example, phrases frequently used by the user on social media can be reflected in the analysis. In addition, specific interests and concerns from the user's social media activity can be reflected in the analysis. Furthermore, the speech content can be analyzed based on information shared by the user on social media. In this way, the analysis of social media activity can improve the accuracy of the analysis of related speech content.

[0056] When generating a sentence, the generation unit can adjust the level of detail of the sentence based on the importance of the spoken content. For example, in the case of important spoken content, a sentence including a detailed explanation is generated. In addition, in the case of general spoken content, a concise sentence can be generated. Furthermore, in the case of urgent spoken content, a sentence that focuses on the main points so that it can be quickly understood can be generated. In this way, by adjusting the level of detail of the sentence based on the importance of the spoken content, a sentence with an appropriate amount of information is generated.

[0057] When generating sentences, the generation unit can apply different generation algorithms depending on the category of the speech content. For example, in the case of daily conversation, an algorithm that generates natural conversational sentences can be applied. In addition, in the case of specialized content, an algorithm that generates sentences including technical terms can be applied. Furthermore, in the case of emotional content, an algorithm for expressing emotions can be applied. In this way, more natural sentences can be generated by applying an appropriate generation algorithm depending on the category of the speech content.

[0058] The process flow of the first embodiment will be briefly described below.

[0059] Step 1: The analysis unit analyzes the user's speech. The analysis unit converts the speech into text data using speech recognition technology and analyzes its content. It also uses natural language processing technology to understand the context and meaning of the speech. Step 2: The generator generates appropriate sentences based on the speech content analyzed by the analyzer. The generator uses text generation AI to generate grammatically correct sentences and uses semantic analysis technology to confirm that the generated sentences are semantically appropriate. Step 3: The reception unit receives the operation of the illustration or icon. The reception unit receives the operation of the user selecting an illustration or icon using the touch screen, and analyzes the content of the selected illustration or icon using image recognition technology. Step 4: The conversion unit converts the content received by the reception unit into text. The conversion unit converts the content of the illustrations and icons analyzed using image recognition technology into text data, generates text based on the text data, and uses grammar analysis technology to confirm that it is grammatically correct. Step 5: The conversion unit converts the content entered by the able-bodied person into illustrations and animations. To generate illustrations and animations based on text data, the conversion unit uses image generation technology and animation generation technology, and uses visual analysis technology to confirm that they are visually easy to understand.

[0060] (Example 2) The communication assistance system according to the embodiment of the present invention is a system that complements incomplete speech of people with immature language skills due to intellectual disabilities or developmental disabilities, and converts content input by operating illustrations or icons into text. This system can also make content input by healthy people easier to understand by illustrating or animating it. For example, when a user with intellectual disabilities or developmental disabilities makes an incomplete speech, the system analyzes the speech and complements it into an appropriate text. For example, if the user says "I'm hungry," the system converts it into a complete text such as "I'm hungry, so I want to eat." Next, when the user operates illustrations or icons to input, the system analyzes the content and converts it into text. For example, if the user selects a food icon and then selects the "I'm hungry" icon, the system generates a text such as "I'm hungry, so I want to eat." Furthermore, the system analyzes text input by healthy people and converts it into illustrations or animations. For example, if the user inputs "Let's go to the park today," the system generates an illustration or animation of a park, making it easier for the intellectually disabled to understand the content. This system is a tool for facilitating communication between people with intellectual disabilities or developmental disabilities and their families, and deepening their understanding. For example, if a family member inputs "I'm going to the hospital today," the system will generate illustrations and animations of the hospital, making it easier for the intellectually disabled to understand the content. The system is installed on a smartphone and is designed to be easy for users to operate. For example, input is completed by simply tapping an icon, and text and illustrations are generated. Voice input is also possible, and the system automatically analyzes what the user says and produces appropriate output. It is expected that this system will improve communication between people with intellectual and developmental disabilities and their families, making daily life smoother. As a result, the communication assistance system can facilitate communication between people with intellectual and developmental disabilities and their families, and deepen understanding.

[0061] The communication assistance system according to the embodiment includes an analysis unit, a generation unit, a reception unit, a conversion unit, and a conversion unit. The analysis unit analyzes the user's speech. The user's speech includes, for example, incomplete speech and fragmentary words, but is not limited to such examples. The analysis unit converts the speech into text data using, for example, a voice recognition technology, and analyzes the content. The analysis unit can also use natural language processing technology to understand the context and meaning of the speech. For example, the analysis unit converts the user's speech into text data using a voice recognition technology, and analyzes the text data using a natural language processing technology. The generation unit generates appropriate sentences based on the speech content analyzed by the analysis unit. The generation unit generates grammatically correct sentences based on the analyzed speech content using, for example, a text generation AI (for example, LLM). The generation unit can also use semantic analysis technology to confirm that the generated sentences are semantically appropriate. For example, the generation unit generates sentences based on the speech content analyzed using a text generation AI, and confirms that the sentences are semantically appropriate. The reception unit receives an operation of an illustration or an icon. The reception unit receives an operation of selecting an illustration or an icon by a user using, for example, a touch screen. The reception unit can also use image recognition technology to analyze the content of the illustration or icon selected by the user. For example, the reception unit analyzes the illustration or icon selected by the user using the touch screen using image recognition technology. The conversion unit converts the content received by the reception unit into text. The conversion unit converts the content of the illustration or icon analyzed using, for example, image recognition technology into text data, and generates a text based on the text data. The conversion unit can also use grammar analysis technology to confirm that the generated text is grammatically correct. For example, the conversion unit converts the content of the illustration or icon analyzed using image recognition technology into text data, generates a text based on the text data, and confirms that the text is grammatically correct using grammar analysis technology. The conversion unit converts the content input by the healthy person into an illustration or animation. The conversion unit uses, for example, image generation technology or animation generation technology to generate an illustration or animation based on text data.The conversion unit can also use visual analysis technology to confirm that the generated illustrations and animations are visually easy to understand. For example, the conversion unit generates illustrations and animations based on text data, and uses visual analysis technology to confirm that they are visually easy to understand. As a result, the communication assistance system according to the embodiment can analyze the user's utterances and operation contents and convert them into appropriate sentences and illustrations to assist communication.

[0062] The analysis unit analyzes the user's speech. The user's speech includes, for example, incomplete speech and fragmentary words, but is not limited to such examples. The analysis unit converts the speech into text data using, for example, a speech recognition technology, and analyzes the content. Specifically, a speech recognition model using deep learning is used as the speech recognition technology. This model realizes highly accurate speech recognition by learning a large amount of speech data. When the user's speech is input, the analysis unit first converts the voice signal into digital data, and then converts it into text data using the speech recognition model. This text data accurately reflects the content of the user's speech, and the analysis unit then analyzes this text data. The analysis unit can also use natural language processing technology to understand the context and meaning of the speech. For example, natural language processing technology includes topic modeling, sentiment analysis, and context analysis. As a result, the analysis unit understands the intention and emotion of the user's speech and provides basic information for generating an appropriate response. Furthermore, the analysis unit extracts keywords and important phrases contained in the user's speech, and grasps the overall meaning of the speech based on them. This enables the analysis unit to understand the situation and purpose of the user's speech and provide the information necessary for the next processing step.

[0063] The generation unit generates appropriate sentences based on the speech content analyzed by the analysis unit. The generation unit generates grammatically correct sentences based on the analyzed speech content, for example, using a text generation AI (for example, LLM). Specifically, the generation unit receives text data provided by the analysis unit as an input, and generates natural sentences using the text generation AI. This text generation AI is based on a large-scale language model and can generate sentences that correspond to various contexts and situations. The generation unit can also use semantic analysis technology to confirm that the generated sentences are semantically appropriate. For example, the generation unit uses semantic analysis technology to confirm whether the generated sentences match the user's speech content and are appropriate for the context. This allows the generation unit to generate appropriate sentences that reflect the user's intentions and emotions. Furthermore, the generation unit can also use grammar analysis technology to confirm that the generated sentences are grammatically correct. This allows the generation unit to provide the user with natural and easy-to-understand sentences. The generation unit can also provide a feedback loop to confirm whether the generated sentences are appropriate for the user's needs and situation. For example, the generated sentences are presented to the user, and the sentences can be modified based on feedback from the user. This enables the generation unit to provide a more appropriate response to the user.

[0064] The reception unit receives operations for selecting illustrations and icons. The reception unit receives operations for selecting illustrations and icons by the user using, for example, a touch screen. Specifically, the touch screen is equipped with a highly sensitive touch sensor and can accurately detect operations using the user's finger or stylus pen. When the user selects an illustration or icon on the touch screen, the selection information is transmitted to the reception unit. The reception unit can also use image recognition technology to analyze the contents of the illustration or icon selected by the user. For example, the reception unit uses image recognition technology to analyze the illustration or icon selected by the user using the touch screen. As the image recognition technology, a deep learning model such as a convolutional neural network (CNN) is often used. This allows the reception unit to accurately recognize the contents of the illustration or icon selected by the user and provide the information to the next processing step. Furthermore, the reception unit can learn the user's operation history and selection patterns and grasp the user's preferences and tendencies. This allows the reception unit to suggest more appropriate illustrations and icons to the user. For example, the efficiency of operation can be improved by preferentially displaying illustrations and icons that the user frequently selects based on the past operation history.

[0065] The conversion unit converts the content received by the reception unit into text. The conversion unit converts the content of an illustration or icon analyzed using, for example, image recognition technology into text data, and generates a text based on the text data. Specifically, the conversion unit analyzes the content of the illustration or icon using image recognition technology, and outputs the result as text data. This text data accurately reflects the meaning and content of the illustration or icon, and the conversion unit then generates a text based on this text data. The conversion unit can also use grammar analysis technology to confirm that the generated text is grammatically correct. For example, the conversion unit uses grammar analysis technology to confirm that the generated text is grammatically correct. This allows the conversion unit to provide the user with natural and easy-to-understand text. Furthermore, the conversion unit converts the content input by the able-bodied person into illustrations or animations. The conversion unit uses, for example, image generation technology or animation generation technology to generate illustrations or animations based on text data. Specifically, the conversion unit receives text data as input, and generates a corresponding illustration using image generation technology. By using animation generation technology, dynamic animations based on text data can also be generated. The conversion unit may also use visual analysis techniques to confirm that the generated illustrations and animations are visually easy to understand. For example, the conversion unit uses visual analysis techniques to confirm that the generated illustrations and animations are visually easy to understand. This allows the conversion unit to provide the user with visually easy-to-understand illustrations and animations.

[0066] The analysis unit can estimate the user's emotion and adjust the analysis method of the speech based on the estimated user's emotion. For example, when the user is nervous, the analysis unit performs the analysis of the speech more carefully to avoid misunderstanding. In addition, when the user is relaxed, the analysis unit can also perform the analysis of the speech quickly to promote smooth communication. Furthermore, when the user is excited, the analysis unit can perform the analysis at a slow pace to calm the user down. This allows for more appropriate analysis by adjusting the analysis method of the speech according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. 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 user's voice data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0067] The analysis unit can improve the analysis accuracy by referring to the user's past speech history when analyzing the utterance. The analysis unit can improve the analysis accuracy by referring to, for example, specific phrases or expressions used by the user in the past. The analysis unit can also perform analysis according to a specific context or situation from the user's past speech history. Furthermore, the analysis unit can also refer to utterances that the user has misunderstood in the past and adjust the analysis to avoid similar misunderstandings. As a result, the analysis accuracy is improved by referring to the past speech history. 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 past speech history data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0068] The analysis unit can perform the analysis while taking into consideration the user's speech speed and volume when analyzing the speech. For example, when the user speaks quickly, the analysis unit performs the analysis while taking into consideration the speech speed, and extracts accurate content. In addition, when the user speaks softly, the analysis unit can perform the analysis while taking into consideration the volume, and can also complement the parts that are difficult to hear. Furthermore, when the user speaks slowly, the analysis unit can perform the analysis according to the speech speed and generate natural sentences. As a result, accurate analysis is possible by taking into consideration the speech speed and volume. 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 user's speech speed and volume data to the generation AI, and cause the generation AI to execute the analysis.

[0069] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. For example, when the user is nervous, the analysis unit prioritizes analysis of important information and provides it quickly. In addition, when the user is relaxed, the analysis unit can also prioritize analysis of detailed information and provide comprehensive results. Furthermore, when the user is excited, the analysis unit can also prioritize analysis of calm information in order to keep the user calm. In this way, the priority of the analysis results is determined according to the user's emotion, so that important information can be provided quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. 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 user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the analysis results.

[0070] When analyzing an utterance, the analysis unit can reflect a language or dialect specific to a region in the analysis by taking into account the geographical location information of the user. For example, when the user is in a specific region, the analysis unit performs the analysis by taking into account the dialect specific to that region. In addition, when the user is traveling, the analysis unit can also reflect the language specific to the region visited in the analysis. Furthermore, when the user makes an utterance from a different region, the analysis unit can also perform the analysis by taking into account the language characteristics of each region. This makes it possible to perform a more natural analysis by taking into account the language or dialect specific to the region. 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 geographical location information of the user to the generation AI and cause the generation AI to execute the analysis of the language or dialect specific to the region.

[0071] The analysis unit can analyze the user's social media activities and analyze the related speech content when analyzing the speech. For example, the analysis unit reflects phrases frequently used by the user on social media in the analysis. The analysis unit can also reflect specific interests and concerns from the user's social media activities in the analysis. Furthermore, the analysis unit can analyze the speech content based on information shared by the user on social media. In this way, the analysis of social media activities improves the analysis accuracy of the related speech content. 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 social media data to the generation AI and cause the generation AI to execute the analysis of the related speech content.

[0072] The generation unit can estimate the user's emotion and adjust the expression method of the generated sentence based on the estimated user's emotion. For example, when the user is nervous, the generation unit generates the sentence with a calm expression method. Also, when the user is relaxed, the generation unit can generate the sentence with a friendly expression method. Furthermore, when the user is excited, the generation unit can generate the sentence with a gentle expression method to keep the user calm. In this way, by adjusting the expression method of the sentence according to the user's emotion, more appropriate sentences are generated. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the sentence.

[0073] The generation unit can adjust the level of detail of the sentence based on the importance of the spoken content when generating the sentence. For example, in the case of important spoken content, the generation unit generates a sentence including a detailed explanation. In addition, in the case of general spoken content, the generation unit can also generate a concise sentence. Furthermore, in the case of urgent spoken content, the generation unit can also generate a sentence that focuses on the main points so that it can be understood quickly. In this way, by adjusting the level of detail of the sentence based on the importance of the spoken content, a sentence with an appropriate amount of information is generated. 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 spoken content to the generation AI and cause the generation AI to adjust the level of detail of the sentence.

[0074] The generation unit can apply different generation algorithms depending on the category of the utterance content when generating the sentence. For example, in the case of daily conversation, the generation unit applies an algorithm that generates natural conversational sentences. In addition, in the case of specialized content, the generation unit can also apply an algorithm that generates sentences including technical terms. Furthermore, in the case of emotional content, the generation unit can also apply an algorithm for expressing emotions. In this way, by applying an appropriate generation algorithm depending on the category of the utterance content, more natural sentences are generated. 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 category data of the utterance content to the generation AI and cause the generation AI to apply the generation algorithm.

[0075] The generation unit can estimate the user's emotion and adjust the length of the sentence to be generated based on the estimated user's emotion. For example, when the user is nervous, the generation unit generates a short sentence that is to the point. Also, when the user is relaxed, the generation unit can generate a longer sentence that includes a detailed explanation. Furthermore, when the user is excited, the generation unit can generate a sentence in a gentle expression manner to keep the user calm. In this way, a sentence of an appropriate length is generated by adjusting the length of the sentence 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 is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the sentence.

[0076] The generation unit can determine the priority of generation based on the submission time of the speech content when generating the sentence. For example, in the case of urgent speech content, the generation unit generates the sentence with the highest priority. In addition, in the case of regular speech content, the generation unit can also generate the sentence with normal priority. Furthermore, in the case of speech content related to future plans, the generation unit can generate the sentence later. In this way, by determining the priority of generation based on the submission time of the speech content, it is possible to quickly process content with high urgency. 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 submission time data of the speech content to the generation AI and cause the generation AI to execute the determination of the priority of generation.

[0077] The generation unit can adjust the order of the generated sentences based on the relevance of the spoken contents when generating the sentences. For example, the generation unit arranges important spoken contents first, followed by related information. The generation unit can also arrange the spoken contents in chronological order to create a natural flow. Furthermore, the generation unit can also arrange highly relevant information preferentially based on the user's interest. In this way, the order of the sentences is adjusted based on the relevance of the spoken contents, thereby generating sentences with a natural flow. 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 relevance data of the spoken contents to the generation AI and cause the generation AI to adjust the order of the sentences.

[0078] The reception unit can estimate the user's emotion and adjust the operation method of the illustration or icon based on the estimated user's emotion. For example, when the user is nervous, the reception unit provides a simple operation method to reduce stress. In addition, when the user is relaxed, the reception unit can provide detailed operation options and suggest a customizable operation method. Furthermore, when the user is excited, the reception unit can provide a calm operation method to keep the user calm. This allows for more appropriate operation by adjusting the operation 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 is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the operation method.

[0079] The reception unit can select the optimal operation method by referring to the user's past operation history when receiving an operation of an illustration or an icon. For example, the reception unit preferentially suggests an operation method that the user has frequently used in the past. The reception unit can also select the optimal operation method according to a specific situation from the user's past operation history. Furthermore, the reception unit can also suggest an operation method to avoid a similar operation error by referring to the content of a user's past operation error. In this way, the optimal operation method is selected by referring to the past operation history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's past operation history data to the generation AI and cause the generation AI to select the optimal operation method.

[0080] The reception unit can filter the operation contents based on the user's current field of interest when receiving the operation of the illustration or icon. The reception unit, for example, preferentially displays operation contents related to a theme in which the user is currently interested. The reception unit can also suggest highly relevant operation contents based on the user's current field of interest. Furthermore, the reception unit can filter and not display operation contents related to a theme in which the user is not currently interested. This makes it possible to perform highly relevant operations by filtering the operation contents based on the current field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current field of interest data to the generation AI and cause the generation AI to execute filtering of the operation contents.

[0081] The reception unit can estimate the user's emotion and determine the priority of the operation contents to be received based on the estimated user's emotion. For example, when the user is nervous, the reception unit preferentially receives important operation contents. In addition, when the user is relaxed, the reception unit can also preferentially receive detailed operation contents. Furthermore, when the user is excited, the reception unit can also preferentially receive calm operation contents in order to keep the user calm. In this way, the priority of the operation contents is determined according to the user's emotion, so that important operations can be quickly received. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the operation contents.

[0082] The reception unit can preferentially receive highly relevant operation contents in consideration of the geographical location information of the user when receiving an operation of an illustration or an icon. For example, when the user is in a specific area, the reception unit preferentially receives operation contents related to that area. In addition, when the user is traveling, the reception unit can also preferentially receive operation contents related to the area of ​​the visit. Furthermore, when the user performs an operation from a different area, the reception unit can also preferentially receive operation contents with high relevance to each area. In this way, highly relevant operation contents are preferentially received by considering the geographical location information. 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 execute the reception of highly relevant operation contents.

[0083] The reception unit can analyze the user's social media activity and receive related operation contents when receiving the operation of the illustration or icon. For example, the reception unit preferentially receives operation contents that the user frequently uses on social media. The reception unit can also preferentially receive operation contents related to specific interests and concerns from the user's social media activity. Furthermore, the reception unit can also preferentially receive related operation contents based on information shared by the user on social media. In this way, related operation contents are preferentially received by analyzing the social media activity. 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 social media data to the generation AI and cause the generation AI to execute the reception of related operation contents.

[0084] The conversion unit can estimate the user's emotion and adjust the expression method of the content to be converted based on the estimated user's emotion. For example, when the user is nervous, the conversion unit converts the content with a calm expression method. Also, when the user is relaxed, the conversion unit can convert the content with a friendly expression method. Furthermore, when the user is excited, the conversion unit can convert the content with a gentle expression method to keep the user calm. This allows for more appropriate conversion by adjusting the expression 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 is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversion unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method.

[0085] The conversion unit can adjust the level of detail of the conversion based on the importance of the received content at the time of conversion. For example, in the case of important received content, the conversion unit performs conversion including detailed explanation. In addition, in the case of general received content, the conversion unit can also perform concise conversion. Furthermore, in the case of urgent received content, the conversion unit can also perform conversion that focuses on the main points so that it can be understood quickly. As a result, by adjusting the level of detail of the conversion based on the importance of the received content, it is possible to convert an appropriate amount of information. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the importance data of the received content to the generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0086] The conversion unit can apply different conversion algorithms depending on the category of the received content at the time of conversion. For example, in the case of daily conversation, the conversion unit applies an algorithm that converts the received content into a natural conversation sentence. In addition, in the case of specialized content, the conversion unit can also apply an algorithm that performs conversion including technical terms. Furthermore, in the case of emotional content, the conversion unit can also apply a conversion algorithm for expressing emotions. This allows for more natural conversion by applying an appropriate conversion algorithm depending on the category of the received content. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input category data of the received content to the generation AI and cause the generation AI to apply the conversion algorithm.

[0087] The conversion unit can estimate the user's emotion and determine the priority of the content to be converted based on the estimated user's emotion. For example, when the user is nervous, the conversion unit preferentially converts important content. Furthermore, when the user is relaxed, the conversion unit can also preferentially convert detailed content. Furthermore, when the user is excited, the conversion unit can also preferentially convert calm content in order to keep the user calm. In this way, important content can be quickly converted by determining the priority of the converted content according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversion unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the converted content.

[0088] The conversion unit can determine the priority of conversion based on the submission time of the reception contents at the time of conversion. For example, in the case of urgent reception contents, the conversion unit performs conversion with the highest priority. In addition, in the case of regular reception contents, the conversion unit can also perform conversion with normal priority. Furthermore, in the case of reception contents related to future plans, the conversion unit can postpone conversion. In this way, by determining the priority of conversion based on the submission time of the reception contents, contents with high urgency can be processed quickly. Part or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the submission time data of the reception contents to the generation AI and have the generation AI execute the determination of the priority of conversion.

[0089] The conversion unit can adjust the order of the contents to be converted based on the relevance of the reception contents at the time of conversion. For example, the conversion unit arranges important reception contents first, followed by related information. The conversion unit can also arrange reception contents in chronological order to create a natural flow. Furthermore, the conversion unit can also arrange highly related information preferentially based on the user's interest. This allows for a natural flow of conversion by adjusting the order of the converted contents based on the relevance of the reception contents. A part or all of the above-mentioned processing in the conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversion unit can input the relevance data of the reception contents to the generation AI and cause the generation AI to adjust the order of the converted contents.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When analyzing the contents of a user's speech, the analysis unit can adjust the analysis method taking into account the user's health condition. For example, if the user has a cold, the speech may become unclear, so the analysis unit can increase the sensitivity of the voice recognition. Also, if the user is tired, the speech speed may become slow, so the analysis unit can perform analysis according to the speech speed. Furthermore, if the user is stressed, the speech content may become fragmented, so the analysis unit can perform analysis with emphasis on the context. This makes it possible to perform analysis according to the user's health condition, and more accurate analysis results can be obtained.

[0092] The analysis unit can estimate the user's emotions and adjust the speech analysis method based on the estimated user's emotions. For example, if the user is nervous, the speech analysis can be performed more carefully to avoid misunderstandings. Also, if the user is relaxed, the speech analysis can be performed quickly to promote smooth communication. Furthermore, if the user is excited, the speech analysis can be performed at a slow pace to calm the user down. This allows for more appropriate analysis by adjusting the speech analysis method according to the user's emotions.

[0093] When analyzing the contents of a user's speech, the analysis unit can improve the accuracy of the analysis by referring to the user's past speech history. For example, the analysis unit can improve the accuracy of the analysis by referring to specific phrases or expressions used by the user in the past. In addition, the analysis unit can perform analysis according to a specific context or situation from the user's past speech history. Furthermore, the analysis unit can adjust the analysis to avoid similar misunderstandings by referring to utterances that the user has misunderstood in the past. In this way, the analysis accuracy is improved by referring to the past speech history.

[0094] When analyzing the contents of a user's speech, the analysis unit can perform the analysis taking into account the user's speech speed and volume. For example, if the user speaks quickly, the analysis is performed taking into account the speech speed, and accurate content is extracted. Also, if the user speaks softly, the analysis can be performed taking into account the volume, and difficult-to-hear parts can be supplemented. Furthermore, if the user speaks slowly, the analysis can be performed according to the speech speed, and natural sentences can be generated. In this way, accurate analysis is possible by taking into account the speech speed and volume.

[0095] The analysis unit can estimate the user's emotions and determine the priority order of analysis results based on the estimated user's emotions. For example, if the user is nervous, important information can be analyzed with priority and provided quickly. Also, if the user is relaxed, detailed information can be analyzed with priority and comprehensive results can be provided. Furthermore, if the user is excited, calm information can be analyzed with priority in order to keep the user calm. In this way, by determining the priority order of analysis results according to the user's emotions, important information can be provided quickly.

[0096] When analyzing the contents of a user's speech, the analysis unit can take into account the user's geographical location information and reflect the language or dialect specific to the region in the analysis. For example, when a user is in a specific region, the analysis is performed taking into account the dialect specific to that region. Also, when a user is traveling, the language specific to the region visited can be reflected in the analysis. Furthermore, when a user speaks from different regions, the analysis can be performed taking into account the linguistic characteristics of each region. This allows for a more natural analysis by taking into account the language or dialect specific to the region.

[0097] When analyzing the user's speech content, the analysis unit can analyze the user's social media activity and analyze the related speech content. For example, phrases frequently used by the user on social media can be reflected in the analysis. In addition, specific interests and concerns from the user's social media activity can be reflected in the analysis. Furthermore, the speech content can be analyzed based on information shared by the user on social media. In this way, the analysis of social media activity can improve the accuracy of the analysis of related speech content.

[0098] The generation unit can estimate the user's emotion and adjust the expression method of the generated sentence based on the estimated user's emotion. For example, if the user is nervous, the generation unit can generate sentences in a calm expression method. If the user is relaxed, the generation unit can generate sentences in a friendly expression method. Furthermore, if the user is excited, the generation unit can generate sentences in a calm expression method in order to keep the user calm. In this way, more appropriate sentences can be generated by adjusting the expression method of the sentence according to the user's emotion.

[0099] When generating a sentence, the generation unit can adjust the level of detail of the sentence based on the importance of the spoken content. For example, in the case of important spoken content, a sentence including a detailed explanation is generated. In addition, in the case of general spoken content, a concise sentence can be generated. Furthermore, in the case of urgent spoken content, a sentence that focuses on the main points so that it can be quickly understood can be generated. In this way, by adjusting the level of detail of the sentence based on the importance of the spoken content, a sentence with an appropriate amount of information is generated.

[0100] When generating sentences, the generation unit can apply different generation algorithms depending on the category of the speech content. For example, in the case of daily conversation, an algorithm that generates natural conversational sentences can be applied. In addition, in the case of specialized content, an algorithm that generates sentences including technical terms can be applied. Furthermore, in the case of emotional content, an algorithm for expressing emotions can be applied. In this way, more natural sentences can be generated by applying an appropriate generation algorithm depending on the category of the speech content.

[0101] The process flow of the second embodiment will be briefly described below.

[0102] Step 1: The analysis unit analyzes the user's speech. The analysis unit converts the speech into text data using speech recognition technology and analyzes its content. It also uses natural language processing technology to understand the context and meaning of the speech. Step 2: The generator generates appropriate sentences based on the speech content analyzed by the analyzer. The generator uses text generation AI to generate grammatically correct sentences and uses semantic analysis technology to confirm that the generated sentences are semantically appropriate. Step 3: The reception unit receives the operation of the illustration or icon. The reception unit receives the operation of the user selecting an illustration or icon using the touch screen, and analyzes the content of the selected illustration or icon using image recognition technology. Step 4: The conversion unit converts the content received by the reception unit into text. The conversion unit converts the content of the illustrations and icons analyzed using image recognition technology into text data, generates text based on the text data, and uses grammar analysis technology to confirm that it is grammatically correct. Step 5: The conversion unit converts the content entered by the able-bodied person into illustrations and animations. To generate illustrations and animations based on text data, the conversion unit uses image generation technology and animation generation technology, and uses visual analysis technology to confirm that they are visually easy to understand.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements including the above-mentioned analysis unit, generation unit, reception unit, and conversion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 and analyzes the user's utterance. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate sentences based on the analyzed utterance content. The reception unit receives, for example, an operation in which the user selects an illustration or icon using the touch screen of the smart device 14. The conversion unit is realized, for example, by the control unit 46A of the smart device 14 and converts the content received by the reception unit into sentences. In addition, the conversion unit can use the processor 46 of the smart device 14 to convert the content input by the healthy person into an illustration or animation. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements including the above-mentioned analysis unit, generation unit, reception unit, and conversion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and analyzes the user's speech. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate sentences based on the analyzed speech content. The reception unit receives, for example, an operation in which the user selects an illustration or icon using the touch screen of the smart glasses 214. The conversion unit is realized, for example, by the control unit 46A of the smart glasses 214 and converts the content received by the reception unit into sentences. In addition, the conversion unit can use the processor 46 of the smart glasses 214 to convert the content input by the able-bodied person into an illustration or animation. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements including the above-mentioned analysis unit, generation unit, reception unit, and conversion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 and analyzes the user's utterance. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate sentences based on the analyzed utterance content. The reception unit receives, for example, an operation in which the user selects an illustration or icon using the touch screen of the headset type terminal 314. The conversion unit is realized, for example, by the control unit 46A of the headset type terminal 314 and converts the content received by the reception unit into sentences. In addition, the conversion unit can use the processor 46 of the headset type terminal 314 to convert the content input by the able-bodied person into illustrations or animations. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements including the above-mentioned analysis unit, generation unit, reception unit, and conversion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and analyzes the user's utterance. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates appropriate sentences based on the analyzed utterance content. The reception unit receives, for example, an operation in which the user selects an illustration or icon using the touch screen of the robot 414. The conversion unit is realized, for example, by the control unit 46A of the robot 414 and converts the content received by the reception unit into sentences. In addition, the conversion unit can use the processor 46 of the robot 414 to convert the content input by the able-bodied person into an illustration or animation. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0156] 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.

[0157] 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.

[0158] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Appendix 1) An analysis unit that analyzes a user's utterance; a generation unit that generates an appropriate sentence based on the speech content analyzed by the analysis unit; A reception unit that receives operations on illustrations or icons; a conversion unit that converts the content received by the reception unit into text; A conversion unit that converts the content input by a healthy person into an illustration or animation. A system characterized by: (Appendix 2) The analysis unit is Estimate the user's emotions and adjust the way the speech is analyzed based on the estimated user emotions 2. The system of claim 1, (Appendix 3) The analysis unit is When analyzing speech, the accuracy of the analysis is improved by referring to the user's past speech history. 2. The system of claim 1, (Appendix 4) The analysis unit is When analyzing speech, the analysis is based on the user's speaking rate or volume. 2. The system of claim 1, (Appendix 5) The analysis unit is Infer user emotions and prioritize analysis results based on the inferred user emotions 2. The system of claim 1, (Appendix 6) The analysis unit is When analyzing speech, the geographic location of the user is taken into account to allow for regional language and dialect analysis. 2. The system of claim 1, (Appendix 7) The analysis unit is When analyzing utterances, the user's social media activity is analyzed and related utterance content is analyzed. 2. The system of claim 1, (Appendix 8) The generation unit is Estimate the user's emotions and adjust the way the text is generated based on the estimated user's emotions 2. The system of claim 1, (Appendix 9) The generation unit is When generating sentences, adjust the level of detail of the sentences based on the importance of the spoken content. 2. The system of claim 1, (Appendix 10) The generation unit is When generating text, different generation algorithms are applied depending on the category of the utterance content. 2. The system of claim 1, (Appendix 11) The generation unit is Estimate the user's emotions and adjust the length of the generated sentences based on the estimated user emotions. 2. The system of claim 1, (Appendix 12) The generation unit is When generating text, determine the priority of generation based on the time of submission of the utterance 2. The system of claim 1, (Appendix 13) The generation unit is When generating sentences, the order of the generated sentences is adjusted based on the relevance of the spoken content. 2. The system of claim 1, (Appendix 14) The reception unit is Estimate the user's emotions and adjust the way illustrations and icons are operated based on the estimated user emotions 2. The system of claim 1, (Appendix 15) The reception unit is When accepting operations on illustrations or icons, the system selects the optimal operation method by referring to the user's past operation history. 2. The system of claim 1, (Appendix 16) The reception unit is When accepting operations on illustrations or icons, the operation content is filtered based on the user's current field of interest. 2. The system of claim 1, (Appendix 17) The reception unit is Estimate the user's emotions and determine the priority of operations to be accepted based on the estimated user emotions. 2. The system of claim 1, (Appendix 18) The reception unit is When accepting operations using illustrations or icons, the system takes into account the user's geographic location information and prioritizes operations that are highly relevant 2. The system of claim 1, (Appendix 19) The reception unit is When accepting operations on illustrations or icons, the system analyzes the user's social media activity and accepts related operations. 2. The system of claim 1, (Appendix 20) The conversion unit is Estimate the user's emotions and adjust the way the content is expressed based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The conversion unit is During conversion, adjust conversion detail based on the importance of the received content 2. The system of claim 1, (Appendix 22) The conversion unit is During conversion, different conversion algorithms are applied depending on the category of the received content. 2. The system of claim 1, (Appendix 23) The conversion unit is Estimate the user's emotions and prioritize the content to be converted based on the estimated user emotions. 2. The system of claim 1, (Appendix 24) The conversion unit is During conversion, prioritize conversions based on when the acceptance was submitted 2. The system of claim 1, (Appendix 25) The conversion unit is During conversion, adjust the order of conversions based on the relevance of incoming content 2. The system of claim 1, [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis unit that analyzes a user's utterance; a generation unit that generates an appropriate sentence based on the speech content analyzed by the analysis unit; A reception unit that receives operations on illustrations or icons; a conversion unit that converts the content received by the reception unit into text; A conversion unit that converts the content input by a healthy person into an illustration or animation. A system characterized by:

2. The analysis unit is Estimate the user's emotions and adjust the way the speech is analyzed based on the estimated user emotions 2. The system of claim 1 .

3. The analysis unit is When analyzing speech, the accuracy of the analysis is improved by referring to the user's past speech history.

2. The system of claim 1 .

4. The analysis unit is When analyzing speech, the analysis is based on the user's speaking rate or volume.

2. The system of claim 1 .

5. The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions.

2. The system of claim 1 .

6. The analysis unit is When analyzing speech, the geographic location of the user is taken into account to allow for regional language and dialect analysis.

2. The system of claim 1 .

7. The analysis unit is When analyzing utterances, the user's social media activity is analyzed and related utterance content is analyzed.

2. The system of claim 1 .

8. The generation unit is Estimate the user's emotions and adjust the way the text is generated based on the estimated user's emotions 2. The system of claim 1 .

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