System

Abstract

To provide a system that supports effective communication for those with language disorder by utilizing gestures and objects.SOLUTION: A system comprises a capturing unit, an analysis unit, a presentation unit, and a conversion unit. The capturing unit captures the user's gestures or objects. The analysis unit analyzes the video captured by the capturing unit. The presentation unit presents candidates based on the analyzed result. The conversion unit converts the candidates presented by the presentation unit into images, illustrations, or comics.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 speech disorders to communicate effectively using gestures and objects.

[0005] The system according to the embodiment aims to enable people with speech disorders to communicate effectively using gestures and objects. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, a presentation unit, and a conversion unit. The photographing unit photographs a user's gesture or an object. The analysis unit analyzes the video photographed by the photographing unit. The presentation unit presents candidates based on the analysis results. The conversion unit converts the candidates presented by the presentation unit into images, illustrations, or cartoons. Effect of the Invention

[0007] The system according to the embodiment enables people with speech impediments to communicate effectively using gestures and objects. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The communication support system according to the embodiment of the present invention is a system used as an auxiliary tool for people with language disorders such as aphasia. In this system, a user takes a picture of a gesture or object that he or she makes in daily life with a camera, and a generation AI analyzes the taken image, and even if the words and objects do not match, several candidates are presented to support communication. In addition, the generation AI can convert it into an image, illustration, or cartoon to communicate with the other party. For example, an object that the user is pointing at or an action that the user is showing with his or her hand is taken with a camera. This image is input to the generation AI. Next, the generation AI analyzes the input image. The generation AI recognizes the object or action in the image and presents the corresponding word as a candidate. For example, if the object that the user is pointing at is "object A," the generation AI presents candidates such as "object A" and "object B." In addition, the generation AI converts the presented candidates into an image, illustration, or cartoon. For example, for the word "object A," an image, illustration, or cartoon of object A is generated. This allows the user to visually confirm the information. With this mechanism, even if the words and objects do not match, several candidates can be presented to support communication. In addition, by converting information into images, illustrations, and cartoons, it is possible to convey information visually, making it possible to communicate even if one cannot understand the other person's words. For example, if a person with aphasia is pointing to "Object A" but cannot find the words, the generation AI will present candidates such as "Object A" and "Object B," and will then generate an image, illustration, or cartoon of Object A, allowing the other person to understand "Object A." In this way, the communication support system can support communication for people with language disorders.

[0029] The communication support system according to the embodiment includes a photographing unit, an analysis unit, a presentation unit, and a conversion unit. The photographing unit photographs the user's gestures and objects. The user's gestures and objects include, but are not limited to, hand movements, facial expressions, and specific objects. The photographing unit photographs the user's gestures and objects using, for example, a camera. The analysis unit uses a generation AI to analyze the video photographed by the photographing unit. The analysis unit recognizes objects and actions in the video using, for example, an image recognition algorithm or a motion analysis method. The presentation unit presents candidates based on the results of the analysis by the analysis unit. The presentation unit presents, for example, a list of words or image options. The conversion unit converts the candidates presented by the presentation unit into images, illustrations, or cartoons. The conversion unit converts the presented candidates into a visually easy-to-understand form using, for example, image processing technology or an illustration generation algorithm. As a result, the communication support system according to the embodiment can support communication for people with language disorders by photographing the user's gestures and objects, analyzing, presenting, and converting them.

[0030] The photographing unit photographs the user's gestures and objects. The user's gestures and objects include, but are not limited to, hand movements, facial expressions, and specific objects. The photographing unit photographs the user's gestures and objects using, for example, a camera. Specifically, the camera has high resolution and can accurately capture the user's subtle movements and changes in facial expressions. Either a fixed or movable camera can be used, and the camera has a function of automatically adjusting the focus according to the user's movements. Furthermore, the camera is equipped with an infrared sensor and a depth sensor, and can accurately photograph even in dark places or against complex backgrounds. This allows the photographing unit to obtain high-quality images regardless of the user's environment. In addition, the photographing unit can simultaneously obtain images from different angles by linking multiple cameras, enabling three-dimensional analysis. This allows the user's movements and the positional relationship of objects to be more accurately understood.

[0031] The analysis unit uses the generation AI to analyze the video captured by the shooting unit. The analysis unit recognizes objects and actions in the video, for example, using image recognition algorithms and motion analysis methods. Specifically, the generation AI uses a deep learning model and is pre-trained with a large amount of data set. This allows it to recognize hand movements, facial expressions, and specific objects with high accuracy. For example, when analyzing sign language movements, the generation AI analyzes the position, shape, and speed of the hand movements in real time to identify the corresponding sign language words and phrases. In addition, when analyzing facial expressions, the generation AI analyzes the movements of each part of the face to infer emotions and intentions. Furthermore, in object recognition, the generation AI analyzes the shape, color, and texture of the object to identify the specific object. This allows the analysis unit to accurately analyze the user's gestures and object information and provide the data necessary for the next process.

[0032] The presentation unit presents candidates based on the results of the analysis by the analysis unit. The presentation unit presents, for example, a list of words or image options. Specifically, the presentation unit presents the optimal means of communication to the user based on the data obtained from the analysis unit. For example, as a result of analyzing sign language movements, a list of corresponding words is displayed so that the user can select one. In addition, as a result of analyzing facial expressions, images or icons corresponding to emotions are presented so that the user can visually express his or her emotions. Furthermore, as a result of object recognition, information and options related to a specific object are presented so that the user can smoothly communicate about the object. The presentation unit uses a touch screen or a voice assistant as a user interface, providing intuitive and easy-to-use operations. This allows the user to communicate quickly and accurately based on the analysis results.

[0033] The conversion unit converts the candidates presented by the presentation unit into images, illustrations, or cartoons. The conversion unit converts the presented candidates into a visually easy-to-understand form, for example, using image processing technology or illustration generation algorithms. Specifically, the conversion unit uses generation AI to generate corresponding illustrations or cartoons based on the words and images selected by the user. For example, as a result of analyzing sign language movements, the corresponding words are illustrated and presented in a form that is visually easy for the user to understand. In addition, as a result of analyzing facial expressions, the facial expressions of characters corresponding to emotions are drawn in a cartoon style, allowing the user to express their emotions more richly. Furthermore, as a result of object recognition, scenes and stories related to a specific object are drawn as cartoons, allowing the user to enjoyably understand information about the object. The conversion unit performs conversion processing in real time, allowing the user to immediately check the results. As a result, the conversion unit visually supports the user's communication, allowing even those with language disabilities to communicate smoothly.

[0034] The analysis unit can recognize objects or actions in the video and present corresponding words as candidates. The analysis unit can recognize objects in the video, for example, using an image recognition algorithm. For example, the analysis unit can recognize that an object in the video is "Object A" and present the word "Object A" as a candidate. The analysis unit can also recognize actions in the video using a motion analysis method. For example, the analysis unit can recognize an action of a user waving their hand and present the word "wave" as a candidate. In this way, the analysis unit can support communication by recognizing objects and actions in the video and presenting corresponding words as candidates.

[0035] The presentation unit can convert the presented candidates into an image, illustration, or cartoon. The presentation unit converts the presented candidates into an image, for example, by using an image processing technique. For example, the presentation unit generates an image of object A for the word "object A". The presentation unit can also convert the presented candidates into illustrations by using an illustration generation algorithm. For example, the presentation unit generates an illustration of object A for the word "object A". The presentation unit can also convert the presented candidates into cartoons by using a cartoon generation algorithm. For example, the presentation unit generates a cartoon of object A for the word "object A". In this way, the presentation unit can support communication by converting the presented candidates into a visually easy-to-understand form.

[0036] The shooting unit can analyze the user's past behavioral patterns and select an appropriate shooting angle or distance when shooting. The shooting unit automatically selects an optimal angle based on, for example, a shooting angle that the user has preferred in the past. For example, the shooting unit analyzes a video that the user has shot in the past and selects an optimal shooting angle. The shooting unit can also set an optimal shooting distance from the user's past behavioral patterns. For example, the shooting unit analyzes a video that the user has shot in the past and sets an optimal shooting distance. Furthermore, the shooting unit can analyze a video that the user has shot in the past and propose optimal shooting conditions. Thereby, the shooting unit can automatically set optimal shooting conditions by analyzing the user's past behavioral patterns. A part or all of the above-mentioned processing in the shooting unit may be performed using, for example, AI, or may be performed without using AI.

[0037] The photographing unit can automatically adjust the photographing conditions based on the user's current environmental information when photographing. For example, when the user is outdoors, the photographing unit adjusts the photographing conditions taking natural light into consideration. For example, when the user is outdoors, the photographing unit adjusts the exposure according to the intensity of natural light. In addition, when the user is indoors, the photographing unit can also set the photographing conditions based on the brightness of the lighting. For example, the photographing unit adjusts the white balance according to the brightness of the lighting in the room. Furthermore, when the user is moving, the photographing unit can automatically adjust the photographing conditions according to the movement. For example, when the user is moving, the photographing unit enables image stabilization. This allows the photographing unit to automatically set the optimal photographing conditions by considering the user's current environmental information. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI.

[0038] The photographing unit can take a highly relevant object into consideration when photographing, taking into account the geographical location information of the user. For example, when the user is at a tourist spot, the photographing unit takes a tourist attraction into consideration. For example, when the user is at a tourist spot, the photographing unit automatically detects a tourist attraction and takes a photograph of the tourist attraction with priority. In addition, when the user is at home, the photographing unit can also take a daily object with priority. For example, when the user is at home, the photographing unit automatically detects a daily object and takes a photograph of the tourist attraction with priority. In addition, when the user is at work, the photographing unit can also take a work-related object with priority. For example, when the user is at work, the photographing unit automatically detects a work-related object and takes a photograph of the tourist attraction with priority. As a result, the photographing unit can take a highly relevant object with priority by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI.

[0039] The photographing unit can analyze the user's social media activity and photograph related objects at the time of photographing. The photographing unit photographs related objects based on, for example, the content frequently posted by the user on SNS. For example, the photographing unit analyzes the content frequently posted by the user on SNS, automatically detects related objects, and photographs them. The photographing unit can also preferentially photograph objects that the user's followers are likely to be interested in. For example, the photographing unit automatically detects and photographs objects that the user's followers are likely to be interested in. Furthermore, the photographing unit can also photograph related objects based on the content that the user has "liked" in the past. For example, the photographing unit analyzes the content that the user has "liked" in the past, automatically detects and photographs related objects. As a result, the photographing unit can preferentially photograph related objects by analyzing the user's social media activity. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI.

[0040] During analysis, the analysis unit can optimize the analysis algorithm by referring to past data of objects and actions in the video. The analysis unit optimizes the analysis algorithm, for example, based on past data of objects in the video. For example, the analysis unit optimizes the analysis algorithm by referring to past data of objects in the video. The analysis unit can also optimize the analysis algorithm based on past data of actions in the video. For example, the analysis unit optimizes the analysis algorithm by referring to past data of actions in the video. Furthermore, the analysis unit can optimize the analysis algorithm by comprehensively referring to past data of objects and actions in the video. Thereby, the analysis unit can optimize the analysis algorithm by referring to past data, thereby improving the analysis accuracy.

[0041] During analysis, the analysis unit can apply different analysis methods depending on the category of objects and actions in the video. For example, when an object in the video is a still object, the analysis unit applies an analysis method specialized for still objects. For example, the analysis unit recognizes that an object in the video is a still object and applies an analysis method specialized for still objects. In addition, when an action in the video is dynamic, the analysis unit can also apply an analysis method specialized for dynamic actions. For example, the analysis unit recognizes that an action in the video is dynamic and applies an analysis method specialized for dynamic actions. Furthermore, the analysis unit can select an optimal analysis method depending on the category of objects and actions in the video. In this way, the analysis unit can improve the analysis accuracy by applying an analysis method according to the category.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time when the video was shot. The analysis unit, for example, prioritizes analysis of the latest video. For example, the analysis unit automatically detects the latest video and prioritizes analysis. The analysis unit can also prioritize analysis of video shot within a specific period. For example, the analysis unit automatically detects video shot within a specific period and prioritizes analysis. Furthermore, the analysis unit can also prioritize analysis of video shot within a period specified by the user. For example, the analysis unit automatically detects video shot within a period specified by the user and prioritizes analysis. In this way, the analysis unit can prioritize analysis of the latest information by determining the priority of analysis based on the time when the video was shot.

[0043] The analysis unit can improve the accuracy of the analysis by referring to literature related to the video during analysis. The analysis unit, for example, refers to literature related to objects in the video to improve the accuracy of the analysis. For example, the analysis unit refers to literature related to objects in the video to improve the accuracy of the analysis. The analysis unit can also improve the accuracy of the analysis by referring to literature related to actions in the video. For example, the analysis unit can improve the accuracy of the analysis. Furthermore, the analysis unit can also improve the accuracy of the analysis by comprehensively referring to literature related to objects and actions in the video. Thereby, the analysis unit can improve the accuracy of the analysis by referring to related literature.

[0044] The presentation unit can adjust the level of detail of the presentation based on the importance of the candidate at the time of presentation. For example, the presentation unit presents candidates with high importance in detail. For example, the presentation unit presents candidates with high importance in detail. The presentation unit can also present candidates with low importance briefly. For example, the presentation unit presents candidates with low importance briefly. Furthermore, the presentation unit can also adjust the level of detail of the presentation in stages according to the importance. Thereby, the presentation unit can provide more appropriate information by adjusting the level of detail of the presentation based on the importance of the candidate.

[0045] The presentation unit can apply different presentation algorithms depending on the category of the candidate at the time of presentation. For example, when presenting object candidates, the presentation unit applies a presentation algorithm specialized for the object. For example, when presenting object candidates, the presentation unit applies a presentation algorithm specialized for the object. Furthermore, when presenting action candidates, the presentation unit can also apply a presentation algorithm specialized for the action. For example, when presenting action candidates, the presentation unit applies a presentation algorithm specialized for the action. Furthermore, the presentation unit can also select an optimal presentation algorithm depending on the category. Thereby, the presentation unit can improve the accuracy of presentation by applying a presentation algorithm according to the category.

[0046] The presentation unit can determine the priority of presentation based on the submission time of the candidate at the time of presentation. The presentation unit, for example, preferentially presents the latest candidate. For example, the presentation unit automatically detects the latest candidate and presents it preferentially. The presentation unit can also preferentially present candidates submitted within a specific period. For example, the presentation unit automatically detects candidates submitted within a specific period and presents them preferentially. Furthermore, the presentation unit can also preferentially present candidates within a period specified by the user. For example, the presentation unit automatically detects candidates within a period specified by the user and presents them preferentially. In this way, the presentation unit can preferentially present the latest information by determining the priority of presentation based on the submission time.

[0047] The presentation unit can adjust the order of presentation based on the relevance of the candidates at the time of presentation. For example, the presentation unit presents candidates with high relevance with priority. For example, the presentation unit automatically detects candidates with high relevance and presents them with priority. The presentation unit can also postpone candidates with low relevance. For example, the presentation unit automatically detects candidates with low relevance and presents them with priority. Furthermore, the presentation unit can also adjust the order of presentation in stages according to the relevance. Thereby, the presentation unit can provide more appropriate information by adjusting the order of presentation based on the relevance.

[0048] During conversion, the conversion unit can optimize the conversion algorithm by referring to past conversion data of candidates. The conversion unit, for example, selects an optimal conversion algorithm based on past conversion data. For example, the conversion unit selects an optimal conversion algorithm by referring to past conversion data. The conversion unit can also analyze past conversion data and optimize the conversion algorithm. For example, the conversion unit analyzes past conversion data and optimizes the conversion algorithm. Furthermore, the conversion unit can also optimize the conversion algorithm by comprehensively referring to past conversion data. Thereby, the conversion unit can optimize the conversion algorithm by referring to past conversion data, thereby improving conversion accuracy.

[0049] The conversion unit can apply different conversion methods depending on the category of the candidate at the time of conversion. For example, when converting an object candidate, the conversion unit applies a conversion method specialized for the object. For example, when converting an object candidate, the conversion unit applies a conversion method specialized for the object. Furthermore, when converting an action candidate, the conversion unit can also apply a conversion method specialized for the action. For example, when converting an action candidate, the conversion unit applies a conversion method specialized for the action. Furthermore, the conversion unit can also select an optimal conversion method depending on the category. Thereby, the conversion unit can improve the conversion accuracy by applying a conversion method according to the category.

[0050] The conversion unit can determine the priority of conversion based on the submission time of the candidates at the time of conversion. The conversion unit, for example, converts the latest candidates with priority. For example, the conversion unit automatically detects the latest candidates and converts them with priority. The conversion unit can also convert candidates submitted within a specific period with priority. For example, the conversion unit automatically detects candidates submitted within a specific period and converts them with priority. The conversion unit can also convert candidates within a period designated by the user with priority. For example, the conversion unit automatically detects candidates within a period designated by the user and converts them with priority. In this way, the conversion unit can convert the latest information with priority by determining the priority of conversion based on the submission time.

[0051] The conversion unit can improve the accuracy of the conversion by referring to the related literature of the candidate during conversion. The conversion unit, for example, refers to literature related to the candidate and improves the accuracy of the conversion. For example, the conversion unit refers to literature related to the candidate and improves the accuracy of the conversion. The conversion unit can also improve the accuracy of the conversion by referring to past conversion data of the candidate. For example, the conversion unit refers to past conversion data of the candidate and improves the accuracy of the conversion. Furthermore, the conversion unit can also improve the accuracy of the conversion by comprehensively referring to the related literature of the candidate and past conversion data. Thereby, the conversion unit can improve the accuracy of the conversion by referring to the related literature.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral data. For example, the analysis unit can more accurately recognize objects and actions in the current video based on data of objects and actions that the user has specified in the past. Furthermore, the analysis unit can learn the user's past behavioral patterns and build a prediction model. For example, objects and actions that the user has frequently specified in the past can be presented as candidates with priority. As a result, the analysis unit can improve the accuracy of the analysis and present more appropriate candidates by referring to the user's past behavioral data.

[0054] The presentation unit can customize the presentation format based on the user's visual preferences. For example, if the user prefers illustrations, the presentation unit can present candidates in an illustration format. If the user prefers photographs, the presentation unit can also present candidates in a photograph format. Furthermore, if the user prefers cartoons, the presentation unit can also present candidates in a cartoon format. In this way, the presentation unit can support more effective communication by customizing the presentation format based on the user's visual preferences.

[0055] The shooting unit can automatically select optimal shooting settings based on the user's past shooting data. For example, it can learn shooting settings that the user has preferred in the past and apply them to the current shooting. The shooting unit can also analyze the user's past shooting data and automatically set optimal shooting angles and distances. Furthermore, the shooting unit can also suggest optimal shooting conditions based on the user's past shooting data. In this way, the shooting unit can automatically select optimal shooting settings by referring to the user's past shooting data, and obtain more effective images.

[0056] The image capturing unit can automatically adjust the exposure setting for image capturing based on the user's current environmental information. For example, when the user is outdoors, the exposure can be adjusted according to the intensity of natural light. When the user is indoors, the exposure can also be adjusted according to the brightness of the lighting. Furthermore, when the user is moving, the exposure can also be automatically adjusted according to the user's movement. In this way, the image capturing unit can automatically adjust the optimal exposure setting by taking into account the user's current environmental information, and obtain more appropriate images.

[0057] The photographing unit can overlay related information based on the geographical location information of the user. For example, when the user is at a tourist spot, information about tourist attractions can be overlaid. When the user is at home, information about nearby stores and facilities can be overlaid. When the user is at work, work-related information can be overlaid. This allows the photographing unit to provide richer information by overlaying related information based on the geographical location information of the user.

[0058] The photography unit can automatically generate related hashtags based on the user's social media activities. For example, related hashtags can be automatically generated based on hashtags that the user frequently uses on SNS. It can also automatically generate hashtags that are likely to interest the user's followers. Furthermore, it can automatically generate related hashtags based on hashtags of posts that the user has "liked" in the past. In this way, the photography unit can support more effective SNS posting by automatically generating related hashtags based on the user's social media activities.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The image capturing unit captures the user's gestures and objects. The user's gestures and objects include, but are not limited to, hand movements, facial expressions, and specific objects. The image capturing unit captures the user's gestures and objects using, for example, a camera. Step 2: The analysis unit uses the generation AI to analyze the video captured by the shooting unit. The analysis unit recognizes objects and actions in the video, for example, using an image recognition algorithm or a motion analysis method. Step 3: The presentation unit presents candidates based on the results of the analysis by the analysis unit. For example, the presentation unit presents a list of words or image options. Step 4: The conversion unit converts the candidates presented by the presentation unit into images, illustrations, or cartoons. The conversion unit converts the presented candidates into a visually easy-to-understand form, for example, by using image processing technology or an illustration generation algorithm.

[0061] (Example 2) The communication support system according to the embodiment of the present invention is a system used as an auxiliary tool for people with language disorders such as aphasia. In this system, a user takes a picture of a gesture or object that he or she makes in daily life with a camera, and a generation AI analyzes the taken image, and even if the words and objects do not match, several candidates are presented to support communication. In addition, the generation AI can convert it into an image, illustration, or cartoon to communicate with the other party. For example, an object that the user is pointing at or an action that the user is showing with his or her hand is taken with a camera. This image is input to the generation AI. Next, the generation AI analyzes the input image. The generation AI recognizes the object or action in the image and presents the corresponding word as a candidate. For example, if the object that the user is pointing at is "object A," the generation AI presents candidates such as "object A" and "object B." In addition, the generation AI converts the presented candidates into an image, illustration, or cartoon. For example, for the word "object A," an image, illustration, or cartoon of object A is generated. This allows the user to visually confirm the information. With this mechanism, even if the words and objects do not match, several candidates can be presented to support communication. In addition, by converting information into images, illustrations, and cartoons, it is possible to convey information visually, making it possible to communicate even if one cannot understand the other person's words. For example, if a person with aphasia is pointing to "Object A" but cannot find the words, the generation AI will present candidates such as "Object A" and "Object B," and will then generate an image, illustration, or cartoon of Object A, allowing the other person to understand "Object A." In this way, the communication support system can support communication for people with language disorders.

[0062] The communication support system according to the embodiment includes a photographing unit, an analysis unit, a presentation unit, and a conversion unit. The photographing unit photographs the user's gestures and objects. The user's gestures and objects include, but are not limited to, hand movements, facial expressions, and specific objects. The photographing unit photographs the user's gestures and objects using, for example, a camera. The analysis unit uses a generation AI to analyze the video photographed by the photographing unit. The analysis unit recognizes objects and actions in the video using, for example, an image recognition algorithm or a motion analysis method. The presentation unit presents candidates based on the results of the analysis by the analysis unit. The presentation unit presents, for example, a list of words or image options. The conversion unit converts the candidates presented by the presentation unit into images, illustrations, or cartoons. The conversion unit converts the presented candidates into a visually easy-to-understand form using, for example, image processing technology or an illustration generation algorithm. As a result, the communication support system according to the embodiment can support communication for people with language disorders by photographing the user's gestures and objects, analyzing, presenting, and converting them.

[0063] The photographing unit photographs the user's gestures and objects. The user's gestures and objects include, but are not limited to, hand movements, facial expressions, and specific objects. The photographing unit photographs the user's gestures and objects using, for example, a camera. Specifically, the camera has high resolution and can accurately capture the user's subtle movements and changes in facial expressions. Either a fixed or movable camera can be used, and the camera has a function of automatically adjusting the focus according to the user's movements. Furthermore, the camera is equipped with an infrared sensor and a depth sensor, and can accurately photograph even in dark places or against complex backgrounds. This allows the photographing unit to obtain high-quality images regardless of the user's environment. In addition, the photographing unit can simultaneously obtain images from different angles by linking multiple cameras, enabling three-dimensional analysis. This allows the user's movements and the positional relationship of objects to be more accurately understood.

[0064] The analysis unit uses the generation AI to analyze the video captured by the shooting unit. The analysis unit recognizes objects and actions in the video, for example, using image recognition algorithms and motion analysis methods. Specifically, the generation AI uses a deep learning model and is pre-trained with a large amount of data set. This allows it to recognize hand movements, facial expressions, and specific objects with high accuracy. For example, when analyzing sign language movements, the generation AI analyzes the position, shape, and speed of the hand movements in real time to identify the corresponding sign language words and phrases. In addition, when analyzing facial expressions, the generation AI analyzes the movements of each part of the face to infer emotions and intentions. Furthermore, in object recognition, the generation AI analyzes the shape, color, and texture of the object to identify the specific object. This allows the analysis unit to accurately analyze the user's gestures and object information and provide the data necessary for the next process.

[0065] The presentation unit presents candidates based on the results of the analysis by the analysis unit. The presentation unit presents, for example, a list of words or image options. Specifically, the presentation unit presents the optimal means of communication to the user based on the data obtained from the analysis unit. For example, as a result of analyzing sign language movements, a list of corresponding words is displayed so that the user can select one. In addition, as a result of analyzing facial expressions, images or icons corresponding to emotions are presented so that the user can visually express his or her emotions. Furthermore, as a result of object recognition, information and options related to a specific object are presented so that the user can smoothly communicate about the object. The presentation unit uses a touch screen or a voice assistant as a user interface, providing intuitive and easy-to-use operations. This allows the user to communicate quickly and accurately based on the analysis results.

[0066] The conversion unit converts the candidates presented by the presentation unit into images, illustrations, or cartoons. The conversion unit converts the presented candidates into a visually easy-to-understand form, for example, using image processing technology or illustration generation algorithms. Specifically, the conversion unit uses generation AI to generate corresponding illustrations or cartoons based on the words and images selected by the user. For example, as a result of analyzing sign language movements, the corresponding words are illustrated and presented in a form that is visually easy for the user to understand. In addition, as a result of analyzing facial expressions, the facial expressions of characters corresponding to emotions are drawn in a cartoon style, allowing the user to express their emotions more richly. Furthermore, as a result of object recognition, scenes and stories related to a specific object are drawn as cartoons, allowing the user to enjoyably understand information about the object. The conversion unit performs conversion processing in real time, allowing the user to immediately check the results. As a result, the conversion unit visually supports the user's communication, allowing even those with language disabilities to communicate smoothly.

[0067] The analysis unit can recognize objects or actions in the video and present corresponding words as candidates. The analysis unit can recognize objects in the video, for example, using an image recognition algorithm. For example, the analysis unit can recognize that an object in the video is "Object A" and present the word "Object A" as a candidate. The analysis unit can also recognize actions in the video using a motion analysis method. For example, the analysis unit can recognize an action of a user waving their hand and present the word "wave" as a candidate. In this way, the analysis unit can support communication by recognizing objects and actions in the video and presenting corresponding words as candidates.

[0068] The presentation unit can convert the presented candidates into an image, illustration, or cartoon. The presentation unit converts the presented candidates into an image, for example, by using an image processing technique. For example, the presentation unit generates an image of object A for the word "object A". The presentation unit can also convert the presented candidates into illustrations by using an illustration generation algorithm. For example, the presentation unit generates an illustration of object A for the word "object A". The presentation unit can also convert the presented candidates into cartoons by using a cartoon generation algorithm. For example, the presentation unit generates a cartoon of object A for the word "object A". In this way, the presentation unit can support communication by converting the presented candidates into a visually easy-to-understand form.

[0069] The image capturing unit can estimate the user's emotion and adjust the timing of shooting based on the emotion. The image capturing unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the image capturing unit delays shooting until the user relaxes. Also, if the user is excited, the image capturing unit can immediately start shooting. Furthermore, if the user is tired, the image capturing unit can resume shooting after a break. In this way, the image capturing unit can obtain more appropriate images by adjusting the timing of shooting 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.

[0070] The shooting unit can analyze the user's past behavioral patterns and select an appropriate shooting angle or distance when shooting. The shooting unit automatically selects an optimal angle based on, for example, a shooting angle that the user has preferred in the past. For example, the shooting unit analyzes a video that the user has shot in the past and selects an optimal shooting angle. The shooting unit can also set an optimal shooting distance from the user's past behavioral patterns. For example, the shooting unit analyzes a video that the user has shot in the past and sets an optimal shooting distance. Furthermore, the shooting unit can analyze a video that the user has shot in the past and propose optimal shooting conditions. Thereby, the shooting unit can automatically set optimal shooting conditions by analyzing the user's past behavioral patterns. A part or all of the above-mentioned processing in the shooting unit may be performed using, for example, AI, or may be performed without using AI.

[0071] The photographing unit can automatically adjust the photographing conditions based on the user's current environmental information when photographing. For example, when the user is outdoors, the photographing unit adjusts the photographing conditions taking natural light into consideration. For example, when the user is outdoors, the photographing unit adjusts the exposure according to the intensity of natural light. In addition, when the user is indoors, the photographing unit can also set the photographing conditions based on the brightness of the lighting. For example, the photographing unit adjusts the white balance according to the brightness of the lighting in the room. Furthermore, when the user is moving, the photographing unit can automatically adjust the photographing conditions according to the movement. For example, when the user is moving, the photographing unit enables image stabilization. This allows the photographing unit to automatically set the optimal photographing conditions by considering the user's current environmental information. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI.

[0072] The photographing unit can estimate the user's emotion and determine the priority of the object to be photographed based on the estimated emotion of the user. For example, the photographing unit photographs the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, when the user is excited, the photographing unit preferentially photographs an object that attracts the user's interest. In addition, when the user is relaxed, the photographing unit can also preferentially photograph a calm object. Furthermore, when the user is nervous, the photographing unit can also preferentially photograph an object that gives a sense of security. In this way, the photographing unit can obtain a more appropriate image by determining the priority of the object to be photographed 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.

[0073] The photographing unit can take a highly relevant object into consideration when photographing, taking into account the geographical location information of the user. For example, when the user is at a tourist spot, the photographing unit takes a tourist attraction into consideration. For example, when the user is at a tourist spot, the photographing unit automatically detects a tourist attraction and takes a photograph of the tourist attraction with priority. In addition, when the user is at home, the photographing unit can also take a daily object with priority. For example, when the user is at home, the photographing unit automatically detects a daily object and takes a photograph of the tourist attraction with priority. In addition, when the user is at work, the photographing unit can also take a work-related object with priority. For example, when the user is at work, the photographing unit automatically detects a work-related object and takes a photograph of the tourist attraction with priority. As a result, the photographing unit can take a highly relevant object with priority by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI.

[0074] The photographing unit can analyze the user's social media activity and photograph related objects at the time of photographing. The photographing unit photographs related objects based on, for example, the content frequently posted by the user on SNS. For example, the photographing unit analyzes the content frequently posted by the user on SNS, automatically detects related objects, and photographs them. The photographing unit can also preferentially photograph objects that the user's followers are likely to be interested in. For example, the photographing unit automatically detects and photographs objects that the user's followers are likely to be interested in. Furthermore, the photographing unit can also photograph related objects based on the content that the user has "liked" in the past. For example, the photographing unit analyzes the content that the user has "liked" in the past, automatically detects and photographs related objects. As a result, the photographing unit can preferentially photograph related objects by analyzing the user's social media activity. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI.

[0075] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. The analysis unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a quick analysis when the user is in a hurry. Furthermore, the analysis unit can provide a visually stimulating analysis result when the user is excited. Thereby, the analysis unit can provide a more appropriate analysis result by adjusting the accuracy of the analysis 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.

[0076] During analysis, the analysis unit can optimize the analysis algorithm by referring to past data of objects and actions in the video. The analysis unit optimizes the analysis algorithm, for example, based on past data of objects in the video. For example, the analysis unit optimizes the analysis algorithm by referring to past data of objects in the video. The analysis unit can also optimize the analysis algorithm based on past data of actions in the video. For example, the analysis unit optimizes the analysis algorithm by referring to past data of actions in the video. Furthermore, the analysis unit can optimize the analysis algorithm by comprehensively referring to past data of objects and actions in the video. Thereby, the analysis unit can optimize the analysis algorithm by referring to past data, thereby improving the analysis accuracy.

[0077] During analysis, the analysis unit can apply different analysis methods depending on the category of objects and actions in the video. For example, when an object in the video is a still object, the analysis unit applies an analysis method specialized for still objects. For example, the analysis unit recognizes that an object in the video is a still object and applies an analysis method specialized for still objects. In addition, when an action in the video is dynamic, the analysis unit can also apply an analysis method specialized for dynamic actions. For example, the analysis unit recognizes that an action in the video is dynamic and applies an analysis method specialized for dynamic actions. Furthermore, the analysis unit can select an optimal analysis method depending on the category of objects and actions in the video. In this way, the analysis unit can improve the analysis accuracy by applying an analysis method according to the category.

[0078] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit provides a simple and highly visible display method when the user is nervous. In addition, the analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows the analysis unit to provide more appropriate information by adjusting the display method of the analysis result 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.

[0079] During analysis, the analysis unit can determine the priority of analysis based on the time when the video was shot. The analysis unit, for example, prioritizes analysis of the latest video. For example, the analysis unit automatically detects the latest video and prioritizes analysis. The analysis unit can also prioritize analysis of video shot within a specific period. For example, the analysis unit automatically detects video shot within a specific period and prioritizes analysis. Furthermore, the analysis unit can also prioritize analysis of video shot within a period specified by the user. For example, the analysis unit automatically detects video shot within a period specified by the user and prioritizes analysis. In this way, the analysis unit can prioritize analysis of the latest information by determining the priority of analysis based on the time when the video was shot.

[0080] The analysis unit can improve the accuracy of the analysis by referring to literature related to the video during analysis. The analysis unit, for example, refers to literature related to objects in the video to improve the accuracy of the analysis. For example, the analysis unit refers to literature related to objects in the video to improve the accuracy of the analysis. The analysis unit can also improve the accuracy of the analysis by referring to literature related to actions in the video. For example, the analysis unit can improve the accuracy of the analysis. Furthermore, the analysis unit can also improve the accuracy of the analysis by comprehensively referring to literature related to objects and actions in the video. Thereby, the analysis unit can improve the accuracy of the analysis by referring to related literature.

[0081] The presentation unit can estimate the user's emotion and adjust the presentation expression method based on the estimated user's emotion. The presentation unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, when the user is nervous, the presentation unit provides a simple and highly visible expression method. When the user is relaxed, the presentation unit can also provide an expression method including detailed information. Furthermore, when the user is in a hurry, the presentation unit can also provide an expression method that focuses on the main points. This allows the presentation unit to provide more appropriate information by adjusting the presentation 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.

[0082] The presentation unit can adjust the level of detail of the presentation based on the importance of the candidate at the time of presentation. For example, the presentation unit presents candidates with high importance in detail. For example, the presentation unit presents candidates with high importance in detail. The presentation unit can also present candidates with low importance briefly. For example, the presentation unit presents candidates with low importance briefly. Furthermore, the presentation unit can also adjust the level of detail of the presentation in stages according to the importance. Thereby, the presentation unit can provide more appropriate information by adjusting the level of detail of the presentation based on the importance of the candidate.

[0083] The presentation unit can apply different presentation algorithms depending on the category of the candidate at the time of presentation. For example, when presenting object candidates, the presentation unit applies a presentation algorithm specialized for the object. For example, when presenting object candidates, the presentation unit applies a presentation algorithm specialized for the object. Furthermore, when presenting action candidates, the presentation unit can also apply a presentation algorithm specialized for the action. For example, when presenting action candidates, the presentation unit applies a presentation algorithm specialized for the action. Furthermore, the presentation unit can also select an optimal presentation algorithm depending on the category. Thereby, the presentation unit can improve the accuracy of presentation by applying a presentation algorithm according to the category.

[0084] The presentation unit can estimate the user's emotion and adjust the length of the presentation based on the estimated user's emotion. The presentation unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, when the user is in a hurry, the presentation unit provides a short, to-the-point presentation. When the user is relaxed, the presentation unit can also provide a longer presentation including detailed explanations. Furthermore, when the user is excited, the presentation unit can also provide a presentation with a visually stimulating effect. This allows the presentation unit to provide more appropriate information by adjusting the length of the presentation 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 can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0085] The presentation unit can determine the priority of presentation based on the submission time of the candidate at the time of presentation. The presentation unit, for example, preferentially presents the latest candidate. For example, the presentation unit automatically detects the latest candidate and presents it preferentially. The presentation unit can also preferentially present candidates submitted within a specific period. For example, the presentation unit automatically detects candidates submitted within a specific period and presents them preferentially. Furthermore, the presentation unit can also preferentially present candidates within a period specified by the user. For example, the presentation unit automatically detects candidates within a period specified by the user and presents them preferentially. In this way, the presentation unit can preferentially present the latest information by determining the priority of presentation based on the submission time.

[0086] The presentation unit can adjust the order of presentation based on the relevance of the candidates at the time of presentation. For example, the presentation unit presents candidates with high relevance with priority. For example, the presentation unit automatically detects candidates with high relevance and presents them with priority. The presentation unit can also postpone candidates with low relevance. For example, the presentation unit automatically detects candidates with low relevance and presents them with priority. Furthermore, the presentation unit can also adjust the order of presentation in stages according to the relevance. Thereby, the presentation unit can provide more appropriate information by adjusting the order of presentation based on the relevance.

[0087] The conversion unit can estimate the user's emotion and adjust the conversion method based on the estimated user's emotion. The conversion unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the conversion unit performs detailed conversion when the user is relaxed. Also, the conversion unit can perform quick conversion when the user is in a hurry. Furthermore, the conversion unit can perform visually stimulating conversion when the user is excited. Thereby, the conversion unit can provide a more appropriate conversion result by adjusting the conversion 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.

[0088] During conversion, the conversion unit can optimize the conversion algorithm by referring to past conversion data of candidates. The conversion unit, for example, selects an optimal conversion algorithm based on past conversion data. For example, the conversion unit selects an optimal conversion algorithm by referring to past conversion data. The conversion unit can also analyze past conversion data and optimize the conversion algorithm. For example, the conversion unit analyzes past conversion data and optimizes the conversion algorithm. Furthermore, the conversion unit can also optimize the conversion algorithm by comprehensively referring to past conversion data. Thereby, the conversion unit can optimize the conversion algorithm by referring to past conversion data, thereby improving conversion accuracy.

[0089] The conversion unit can apply different conversion methods depending on the category of the candidate at the time of conversion. For example, when converting an object candidate, the conversion unit applies a conversion method specialized for the object. For example, when converting an object candidate, the conversion unit applies a conversion method specialized for the object. Furthermore, when converting an action candidate, the conversion unit can also apply a conversion method specialized for the action. For example, when converting an action candidate, the conversion unit applies a conversion method specialized for the action. Furthermore, the conversion unit can also select an optimal conversion method depending on the category. Thereby, the conversion unit can improve the conversion accuracy by applying a conversion method according to the category.

[0090] The conversion unit can estimate the user's emotion and adjust the display method of the conversion result based on the estimated user's emotion. For example, the conversion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the conversion unit provides a simple and highly visible display method when the user is nervous. In addition, the conversion unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the conversion unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows the conversion unit to provide more appropriate information by adjusting the display method of the conversion result 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.

[0091] The conversion unit can determine the priority of conversion based on the submission time of the candidates at the time of conversion. The conversion unit, for example, converts the latest candidates with priority. For example, the conversion unit automatically detects the latest candidates and converts them with priority. The conversion unit can also convert candidates submitted within a specific period with priority. For example, the conversion unit automatically detects candidates submitted within a specific period and converts them with priority. The conversion unit can also convert candidates within a period designated by the user with priority. For example, the conversion unit automatically detects candidates within a period designated by the user and converts them with priority. In this way, the conversion unit can convert the latest information with priority by determining the priority of conversion based on the submission time.

[0092] The conversion unit can improve the accuracy of the conversion by referring to the related literature of the candidate during conversion. The conversion unit, for example, refers to literature related to the candidate and improves the accuracy of the conversion. For example, the conversion unit refers to literature related to the candidate and improves the accuracy of the conversion. The conversion unit can also improve the accuracy of the conversion by referring to past conversion data of the candidate. For example, the conversion unit refers to past conversion data of the candidate and improves the accuracy of the conversion. Furthermore, the conversion unit can also improve the accuracy of the conversion by comprehensively referring to the related literature of the candidate and past conversion data. Thereby, the conversion unit can improve the accuracy of the conversion by referring to the related literature.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The communication support system may further include a voice recognition unit. The voice recognition unit recognizes the user's speech in real time and transmits it to the analysis unit. For example, when the user utters "Please give me some water," the voice recognition unit converts the speech into text and transmits it to the analysis unit. The analysis unit can analyze the user's intention based on the text transmitted from the voice recognition unit and present appropriate candidates. The voice recognition unit can also analyze the speed and tone of the user's speech and estimate the user's emotions. For example, when the user is excited, the analysis unit can quickly present candidates. In this way, the voice recognition unit can recognize the user's speech in real time and transmit it to the analysis unit, thereby supporting communication.

[0095] The analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral data. For example, the analysis unit can more accurately recognize objects and actions in the current video based on data of objects and actions that the user has specified in the past. Furthermore, the analysis unit can learn the user's past behavioral patterns and build a prediction model. For example, objects and actions that the user has frequently specified in the past can be presented as candidates with priority. As a result, the analysis unit can improve the accuracy of the analysis and present more appropriate candidates by referring to the user's past behavioral data.

[0096] The presentation unit can customize the presentation format based on the user's visual preferences. For example, if the user prefers illustrations, the presentation unit can present candidates in an illustration format. If the user prefers photographs, the presentation unit can also present candidates in a photograph format. Furthermore, if the user prefers cartoons, the presentation unit can also present candidates in a cartoon format. In this way, the presentation unit can support more effective communication by customizing the presentation format based on the user's visual preferences.

[0097] The image capturing unit can estimate the user's emotion and adjust the frame rate of the image capturing based on the estimated emotion. For example, when the user is excited, the image capturing unit can capture images at a high frame rate and smoothly record fast-moving scenes. When the user is relaxed, the image capturing unit can capture images at a low frame rate and record quiet scenes in more detail. Furthermore, when the user is nervous, the image capturing unit can capture images at a medium frame rate and provide a balanced image. This allows the image capturing unit to obtain more appropriate images by adjusting the frame rate of the image capturing according to the user's emotion.

[0098] The shooting unit can automatically select optimal shooting settings based on the user's past shooting data. For example, it can learn shooting settings that the user has preferred in the past and apply them to the current shooting. The shooting unit can also analyze the user's past shooting data and automatically set optimal shooting angles and distances. Furthermore, the shooting unit can also suggest optimal shooting conditions based on the user's past shooting data. In this way, the shooting unit can automatically select optimal shooting settings by referring to the user's past shooting data, and obtain more effective images.

[0099] The image capturing unit can automatically adjust the exposure setting for image capturing based on the user's current environmental information. For example, when the user is outdoors, the exposure can be adjusted according to the intensity of natural light. When the user is indoors, the exposure can also be adjusted according to the brightness of the lighting. Furthermore, when the user is moving, the exposure can also be automatically adjusted according to the user's movement. In this way, the image capturing unit can automatically adjust the optimal exposure setting by taking into account the user's current environmental information, and obtain more appropriate images.

[0100] The image capturing unit can estimate the user's emotion and adjust the zoom level of the image capturing based on the estimated emotion. For example, if the user is excited, the image capturing unit can zoom in to obtain detailed images. If the user is relaxed, the image capturing unit can zoom out to obtain a wide range of images. Furthermore, if the user is nervous, the image capturing unit can capture images at a moderate zoom level to provide a balanced image. This allows the image capturing unit to obtain a more appropriate image by adjusting the zoom level of the image capturing according to the user's emotion.

[0101] The photographing unit can overlay related information based on the geographical location information of the user. For example, when the user is at a tourist spot, information about tourist attractions can be overlaid. When the user is at home, information about nearby stores and facilities can be overlaid. When the user is at work, work-related information can be overlaid. This allows the photographing unit to provide richer information by overlaying related information based on the geographical location information of the user.

[0102] The photography unit can automatically generate related hashtags based on the user's social media activities. For example, related hashtags can be automatically generated based on hashtags that the user frequently uses on SNS. It can also automatically generate hashtags that are likely to interest the user's followers. Furthermore, it can automatically generate related hashtags based on hashtags of posts that the user has "liked" in the past. In this way, the photography unit can support more effective SNS posting by automatically generating related hashtags based on the user's social media activities.

[0103] The analysis unit can estimate the user's emotions and filter the analysis results based on the estimated emotions. For example, if the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, only important information can be filtered and provided. Furthermore, if the user is excited, a visually stimulating analysis result can be provided. In this way, the analysis unit can provide more appropriate information by filtering the analysis result according to the user's emotions.

[0104] The process flow of the second embodiment will be briefly described below.

[0105] Step 1: The image capturing unit captures the user's gestures and objects. The user's gestures and objects include, but are not limited to, hand movements, facial expressions, and specific objects. The image capturing unit captures the user's gestures and objects using, for example, a camera. Step 2: The analysis unit uses the generation AI to analyze the video captured by the shooting unit. The analysis unit recognizes objects and actions in the video, for example, using an image recognition algorithm or a motion analysis method. Step 3: The presentation unit presents candidates based on the results of the analysis by the analysis unit. For example, the presentation unit presents a list of words or image options. Step 4: The conversion unit converts the candidates presented by the presentation unit into images, illustrations, or cartoons. The conversion unit converts the presented candidates into a visually easy-to-understand form, for example, by using image processing technology or an illustration generation algorithm.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, presentation 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 photographing unit is realized by the camera 42 of the smart device 14, and photographs the user's gestures and objects. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photographed image. The presentation unit is realized, for example, by the display 40A of the smart device 14, and presents candidates based on the analysis results. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the presented candidates into images, illustrations, or cartoons. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, presentation 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 photographing unit is realized by the camera 42 of the smart glasses 214, and photographs the user's gestures and objects. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photographed image. The presentation unit is realized, for example, by the display of the smart glasses 214, and presents candidates based on the analysis results. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the presented candidates into images, illustrations, or cartoons. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, presentation 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 photographing unit is realized by the camera 42 of the headset type terminal 314, and photographs the user's gestures and objects. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photographed video. The presentation unit is realized, for example, by the display 343 of the headset type terminal 314, and presents candidates based on the analysis results. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the presented candidates into images, illustrations, or cartoons. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, presentation 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 photographing unit is realized by the camera 42 of the robot 414, and photographs the user's gestures and objects. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photographed video. The presentation unit is realized, for example, by the display of the robot 414, and presents candidates based on the analysis results. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the presented candidates into images, illustrations, or cartoons. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Appendix 1) An image capturing unit that captures an image of a user's gesture or an object; An analysis unit that analyzes the image captured by the imaging unit; a presentation unit that presents candidates based on the analyzed results; A conversion unit that converts the candidates presented by the presentation unit into images, illustrations, or cartoons. A system characterized by: (Appendix 2) The analysis unit is Recognizes objects or actions in the video and suggests corresponding words 2. The system of claim 1, (Appendix 3) The presentation unit is Convert the suggested candidates into images, illustrations, or comics 2. The system of claim 1, (Appendix 4) The imaging unit includes: Estimate the user's emotions and adjust the timing of taking photos based on those emotions 2. The system of claim 1, (Appendix 5) The imaging unit includes: When taking a photo, the system analyzes the user's past behavioral patterns and selects the appropriate shooting angle or distance. 2. The system of claim 1, (Appendix 6) The imaging unit includes: When taking a picture, the shooting conditions are automatically adjusted based on the user's current environmental information. 2. The system of claim 1, (Appendix 7) The imaging unit includes: Estimate the user's emotions and prioritize the subjects to be photographed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The imaging unit includes: When taking a photo, the system takes into account the user's geographic location information and prioritizes taking photos of subjects that are highly relevant. 2. The system of claim 1, (Appendix 9) The imaging unit includes: When taking a photo, it analyzes the user's social media activity and takes photos of related subjects. 2. The system of claim 1, (Appendix 10) The analysis unit is Estimate user emotions and adjust analysis accuracy based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The analysis unit is During analysis, the analysis algorithm is optimized by referencing past data on objects and actions in the video. 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, different analysis methods are applied depending on the category of objects or actions in the video. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, prioritize analysis based on when the footage was taken 2. The system of claim 1, (Appendix 15) The analysis unit is When analyzing, refer to related literature on the video to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 16) The presentation unit is Estimate the user's emotions and adjust the presentation based on the estimated user's emotions 2. The system of claim 1, (Appendix 17) The presentation unit is At presentation time, adjust the detail of the suggestions based on the importance of the suggestions 2. The system of claim 1, (Appendix 18) The presentation unit is During presentation, different presentation algorithms are applied depending on the category of the candidate. 2. The system of claim 1, (Appendix 19) The presentation unit is Estimate the user's emotion and adjust the presentation length based on the estimated user emotion. 2. The system of claim 1, (Appendix 20) The presentation unit is During submission, prioritize submissions based on when candidates were submitted 2. The system of claim 1, (Appendix 21) The presentation unit is At suggestion time, adjust the order of suggestions based on the relevance of the suggestions 2. The system of claim 1, (Appendix 22) The conversion unit is Infer user emotions and adjust the conversion method based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The conversion unit is During conversion, the conversion algorithm is optimized by referencing the past conversion data of the candidate. 2. The system of claim 1, (Appendix 24) The conversion unit is During conversion, different conversion methods are applied depending on the category of the candidate. 2. The system of claim 1, (Appendix 25) The conversion unit is Inferring user sentiment and adjusting the way conversion results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 26) The conversion unit is During conversion, prioritize conversions based on when candidates were submitted 2. The system of claim 1, (Appendix 27) The conversion unit is During conversion, the accuracy of the conversion is improved by referring to related literature of the candidates. 2. The system of claim 1, [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An image capturing unit that captures an image of a user's gesture or an object; An analysis unit that analyzes the image captured by the imaging unit; a presentation unit that presents candidates based on the analyzed results; A conversion unit that converts the candidates presented by the presentation unit into images, illustrations, or cartoons. A system characterized by:

2. The analysis unit is Recognizes objects or actions in the video and suggests corresponding words 2. The system of claim 1 .

3. The imaging unit includes: Estimate the user's emotions and adjust the timing of taking photos based on those emotions 2. The system of claim 1 .

4. The imaging unit includes: When taking a photo, the system analyzes the user's past behavioral patterns and selects the appropriate shooting angle or distance.

2. The system of claim 1 .

5. The imaging unit includes: When taking a picture, the shooting conditions are automatically adjusted based on the user's current environmental information.

2. The system of claim 1 .

6. The imaging unit includes: Estimate the user's emotions and prioritize the subjects to be photographed based on the estimated user's emotions.

2. The system of claim 1 .

7. The imaging unit includes: When taking a photo, the system takes into account the user's geographic location information and prioritizes taking photos of subjects that are highly relevant.

2. The system of claim 1 .

Images