System

Abstract

To provide a system that provides support for effectively conveying intentions of people with aphasia.SOLUTION: A system according to the embodiment comprises a selection unit, a prediction unit, and a generation unit. The selection unit selects an image. The prediction unit predicts a next image to be displayed based on the image selected by the selection unit. The generation unit generates words based on the image predicted by the prediction unit and reads the words with voice.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] Previous technology has limited the means of communication available to aphasia patients, resulting in a lack of effective support.

[0005] The system according to the embodiment aims to provide support for aphasia patients to communicate their intentions effectively. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a prediction unit, and a generation unit. The selection unit selects an image. The prediction unit predicts an image to be displayed next based on the image selected by the selection unit. The generation unit generates words based on the image predicted by the prediction unit and speaks the words out loud. Effect of the Invention

[0007] The system according to the embodiment can provide support for aphasia patients to communicate effectively. [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 that supports communication for patients with aphasia. In this system, the patient selects one image with which he or she wants to communicate, and the generation AI predicts and presents the next image based on the image. This allows the patient to easily find the image he or she wants to choose next. Furthermore, the generation AI can also create words based on the selected image and utter them by voice. For example, if the patient wants to eat, the patient selects an image of food. This information is input to the generation AI. Next, the generation AI analyzes the input information and predicts the next image. The generation AI presents the image the patient will want to choose next based on the selected image. For example, if an image of food is selected, an image of a drink is presented next. Furthermore, the generation AI can create words based on the selected image and utter them by voice. For example, the word "I want to eat" is generated and uttered by voice. In this way, the patient's intention can be clearly conveyed. This system allows the patient to communicate his or her intention with fewer options, greatly reducing the burden of communication. By the generation AI predicting the next image, the patient can easily find the next image he or she wants to choose. Furthermore, the function of the generation AI creating words and uttering them by voice allows the patient's intention to be conveyed more clearly. This enables the communication support system to clearly convey the intentions of aphasic patients.

[0029] The communication support system according to the embodiment includes a selection unit, a prediction unit, and a generation unit. The selection unit selects one image with which the patient wants to communicate. The image selected by the patient includes, for example, a meal, a drink, a place, an item, and the like, but is not limited to such examples. The selection unit can select an image using, for example, a touch screen. The selection unit can also select an image using voice input. For example, when the patient says "meal", an image of the meal is displayed. The prediction unit uses a generation AI to predict an image that will come next based on the image selected by the selection unit. For example, when an image of a meal is selected, the prediction unit presents an image of a drink next. The prediction unit can use the generation AI to predict an image that will come next based on the selected image. For example, when an image of a meal is selected, the generation AI presents an image of a drink next. The prediction unit can also use the generation AI to predict an image that will come next based on the selected image. For example, when an image of a meal is selected, the generation AI presents an image of a drink next. The generation unit uses the generation AI to create words based on the image predicted by the prediction unit and utter them by voice. The generating unit generates, for example, the phrase "I want to eat" and issues it by voice. The generating unit can use a generating AI to create words based on a selected image and issue them by voice. For example, the generating AI generates the phrase "I want to eat" and issues it by voice. The generating unit can also use a generating AI to create words based on a selected image and issue them by voice. For example, the generating AI generates the phrase "I want to eat" and issues it by voice. As a result, the communication support system according to the embodiment can clearly communicate the intention of the aphasia patient. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generating unit can generate words using an AI model that takes a selected image as input and outputs words. As a result, the communication support system according to the embodiment can clearly communicate the intention of the aphasia patient.

[0030] The selection unit selects one image with which the patient wants to communicate. The images selected by the patient include, but are not limited to, for example, food, drink, places, and items. The selection unit can select an image using, for example, a touch screen. The touch screen allows intuitive operation, and the patient can easily select an image just by touching it with his / her finger. The selection unit can also select an image using voice input. Voice input is particularly effective for patients with limited hand and finger movement, and uses voice recognition technology to analyze the patient's utterance and display a corresponding image. For example, when the patient says "meal," an image of the meal is displayed. Furthermore, the selection unit can also use gesture recognition technology to analyze the patient's hand and face movements and select an image. This allows the selection unit to provide various input means and enable flexible operation according to the patient's needs and situation. The selection unit also has a function of recording the patient's selection history and learning past selection patterns to assist the patient in making a smoother selection the next time. For example, the selection unit reduces the effort required for selection by preferentially displaying images that the patient frequently selects. The selection unit also provides a function for selecting multiple images at the same time, allowing the patient to convey multiple intentions at once, thereby enabling the selection unit to efficiently and effectively support communication with the patient.

[0031] The prediction unit uses the generation AI to predict the next image based on the image selected by the selection unit. The generation AI learns past data and patterns and can predict the next image based on the selected image with high accuracy. For example, if an image of a meal is selected, it will present an image of a drink next. The generation AI has learned the association between meals and drinks and predicts that a drink will be needed after the meal. The prediction unit can also predict the next image by taking into account the context of the selected image. For example, if the selected image of the meal is breakfast, it will present an image of coffee or juice next. The prediction unit can predict the next image by using the generation AI. Furthermore, the prediction unit can predict the next image by taking into account the patient's past selection history. For example, if the patient has a tendency to select a drink after a meal in the past, the prediction unit learns that pattern and presents an image of a drink next. This allows the prediction unit to present an appropriate image according to the patient's needs and facilitate smooth communication. The prediction unit can improve the accuracy of prediction by regularly updating the learning model of the generation AI and reflecting the latest data and trends. This enables the prediction unit to always provide highly accurate predictions and support communication with patients.

[0032] The generation unit uses the generation AI to create words based on the image predicted by the prediction unit and recites them by voice. The generation unit generates, for example, the word "I want to eat" and recites it by voice. The generation AI understands the context of the selected image and the predicted image, and generates appropriate words based on that. For example, if an image of a meal is selected and an image of a drink is predicted, the generation AI can generate the words "I want to eat, so I also want a drink." The generation unit recites the generated words by voice using speech synthesis technology. The speech synthesis technology generates voice with natural pronunciation and intonation, and can clearly convey the patient's intentions. The generation unit can use the generation AI to create words based on the selected image and recite them by voice. For example, the generation AI generates the word "I want to eat" and recites it by voice. The generation unit can also use the generation AI to create words based on the selected image and recite them by voice. For example, the generation AI generates the word "I want to eat" and recites it by voice. The generation unit can use the generation AI to create words based on the selected image and recite them by voice. For example, the generation AI generates the words "I want to eat" and speaks them out loud. The generation unit can also use the generation AI to create words based on a selected image and speak them out loud. For example, the generation AI generates the words "I want to eat" and speaks them out loud. This allows the communication support system according to the embodiment to clearly communicate the intention of the aphasia patient. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate words using an AI model that takes a selected image as input and outputs words. This allows the communication support system according to the embodiment to clearly communicate the intention of the aphasia patient.

[0033] The prediction unit can predict the next image to be displayed using the generation AI. The prediction unit can predict the next image based on the selected image using the generation AI, for example. For example, if an image of a meal is selected, an image of a drink is presented next. The prediction unit can predict the next image based on the selected image using the generation AI. For example, if an image of a meal is selected, an image of a drink is presented next. The prediction unit can also predict the next image based on the selected image using the generation AI. For example, if an image of a meal is selected, an image of a drink is presented next. As a result, by using the generation AI, the prediction accuracy of the next image is improved. Part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can predict the next image using an AI model that inputs the selected image and outputs the next image.

[0034] The generation unit can generate words using a generation AI. The generation unit, for example, uses a generation AI to create words based on a selected image. For example, the generation unit generates the word "I want to eat". The generation unit can create words based on a selected image using a generation AI. For example, the generation AI generates the word "I want to eat". The generation unit can also create words based on a selected image using a generation AI. For example, the generation AI generates the word "I want to eat". As a result, the use of the generation AI improves the accuracy of word generation. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can generate words using an AI model that takes a selected image as input and outputs words.

[0035] The generation unit can output the speech using the generation AI. The generation unit can use the generation AI to create words based on the selected image and output them by speech. For example, the generation AI generates the word "I want to eat" and outputs it by speech. The generation unit can use the generation AI to create words based on the selected image and output them by speech. For example, the generation AI generates the word "I want to eat" and outputs it by speech. The generation unit can also use the generation AI to create words based on the selected image and output them by speech. For example, the generation AI generates the word "I want to eat" and outputs it by speech. As a result, the speech accuracy of the speech is improved by using the generation AI. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate words and output them by speech using an AI model that takes a selected image as input and outputs words.

[0036] The selection unit can select one image with which the patient wants to communicate. The selection unit, for example, selects one image with which the patient wants to communicate. Images selected by the patient include, for example, meals, drinks, places, objects, etc., but are not limited to such examples. The selection unit can select an image using, for example, a touch screen. The selection unit can also select an image using voice input. For example, when the patient says "meal," an image of a meal is displayed. This makes it easier for the patient to select an image with which he or she wants to communicate. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can predict the next image using an AI model that takes the image selected by the patient as input and outputs the image that will come next.

[0037] The selection unit can analyze the patient's past selection history and present the optimal image with priority. For example, the selection unit automatically displays images that the patient has frequently selected in the past as candidates. The selection unit can also preferentially suggest a selection method (such as voice or touch) that the patient has used in the past. Furthermore, the selection unit can predict and suggest an image to be used in a specific time period from the patient's past selection history. This improves the efficiency of selection by presenting the optimal image based on the past selection history. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can present the optimal image using an AI model that inputs the patient's past selection history and outputs the optimal image.

[0038] The selection unit can filter images based on the patient's current health condition and environment. For example, when the patient is tired, the selection unit preferentially displays images that are simple and highly visible. In addition, when the patient is relaxed, the selection unit can also provide images containing detailed information. Furthermore, when the patient is in a specific environment, the selection unit can also suggest images suitable for that environment. This reduces the burden of selection by providing images according to the health condition and environment. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can filter images using an AI model that inputs the patient's health condition and environmental data and filters images.

[0039] The selection unit can preferentially present images with high relevance based on the geographical location information of the patient. For example, when the patient is in a specific location, the selection unit preferentially displays images related to the location. In addition, when the patient is moving, the selection unit can also suggest images with high relevance based on the current location. Furthermore, when the patient is in a specific area, the selection unit can also preferentially display images related to the area. This improves the efficiency of selection by providing images with high relevance based on geographical location information. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can present images with high relevance using an AI model that inputs geographical location information of the patient and outputs images with high relevance.

[0040] The selection unit can analyze the patient's social media activity and present relevant images. The selection unit can, for example, suggest relevant images based on the content frequently posted by the patient on social media. The selection unit can also preferentially display images shared by the patient's social media friends. Furthermore, the selection unit can analyze the patient's social media activity history and suggest highly relevant images. This improves the efficiency of selection by providing highly relevant images based on social media activity. Some or all of the above-mentioned processing in the selection unit can be performed, for example, using AI or without AI. For example, the selection unit can present relevant images using an AI model that inputs the patient's social media activity data and outputs related images.

[0041] The prediction unit can adjust the prediction algorithm by referring to past prediction results. The prediction unit adjusts the prediction algorithm based on, for example, past prediction results. The prediction unit can also extract a pattern for performing highly accurate prediction from the past prediction results. Furthermore, the prediction unit can analyze past prediction results and improve the accuracy of the prediction algorithm. As a result, the prediction algorithm is optimized based on the past prediction results, thereby improving the accuracy of prediction. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can adjust the prediction algorithm using an AI model that uses past prediction result data as input and adjusts the prediction algorithm.

[0042] The prediction unit can set a prediction priority based on the patient's selection history. For example, the prediction unit preferentially predicts images that the patient has frequently selected in the past. The prediction unit can also predict images to be selected in a specific time period from the patient's selection history. Furthermore, the prediction unit can analyze the patient's selection history and preferentially predict the most relevant image. This improves the accuracy of prediction by determining the prediction priority based on the selection history. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can set the prediction priority using an AI model that inputs the patient's selection history data and sets the prediction priority.

[0043] The prediction unit can customize the prediction result based on the geographic location information of the patient. For example, when the patient is in a specific location, the prediction unit provides a prediction result related to the location. In addition, when the patient is moving, the prediction unit can also customize the prediction result based on the current location. Furthermore, when the patient is in a specific area, the prediction unit can also provide a prediction result related to the area. This improves the accuracy of the prediction by customizing the prediction result based on the geographic location information. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can customize the prediction result using an AI model that uses the geographic location information of the patient as an input and customizes the prediction result.

[0044] The prediction unit can analyze the patient's social media activity and present a relevant prediction result. The prediction unit can provide a relevant prediction result based on, for example, the content frequently posted by the patient on social media. The prediction unit can also present a prediction result based on the content shared by the patient's social media friends. Furthermore, the prediction unit can analyze the patient's social media activity history and provide a highly relevant prediction result. This improves the accuracy of prediction by providing a highly relevant prediction result based on social media activity. Some or all of the above-mentioned processing in the prediction unit can be performed, for example, using AI or can be performed without using AI. For example, the prediction unit can present a relevant prediction result using an AI model that inputs the patient's social media activity data and outputs a related prediction result.

[0045] The generation unit can adjust the generation algorithm by referring to past generation results. The generation unit adjusts the generation algorithm based on, for example, past generation results. The generation unit can also extract a pattern for performing highly accurate generation from the past generation results. Furthermore, the generation unit can analyze past generation results and improve the accuracy of the generation algorithm. As a result, the generation algorithm is optimized based on the past generation results, thereby improving the accuracy of generation. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can adjust the generation algorithm using an AI model that uses past generation result data as input and adjusts the generation algorithm.

[0046] The generating unit can set the priority of the words to be generated based on the selection history of the patient. For example, the generating unit preferentially generates words that the patient has frequently used in the past. The generating unit can also predict and generate words to be used in a specific time period from the selection history of the patient. Furthermore, the generating unit can analyze the selection history of the patient and preferentially generate the most relevant words. This improves the accuracy of generation by determining the priority of words based on the selection history. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can set the priority of words using an AI model that inputs the selection history data of the patient and sets the priority of words.

[0047] The generating unit can customize the words to be generated based on the geographical location information of the patient. For example, when the patient is in a specific location, the generating unit generates words related to the location. In addition, when the patient is moving, the generating unit can also generate highly relevant words based on the current location. Furthermore, when the patient is in a specific area, the generating unit can also generate words related to the area. This improves the quality of communication by customizing the words based on the geographical location information. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI or may be performed without using AI. For example, the generating unit can customize words using an AI model that uses the geographical location information of the patient as an input and customizes words.

[0048] The generating unit can analyze the social media activity of the patient and generate related words. The generating unit can generate related words based on, for example, the content frequently posted by the patient on social media. The generating unit can also generate words based on the content shared by the patient's social media friends. Furthermore, the generating unit can analyze the social media activity history of the patient and generate highly relevant words. This improves the quality of communication by providing highly relevant words based on social media activity. Some or all of the above-mentioned processing in the generating unit can be performed, for example, using AI or can be performed without using AI. For example, the generating unit can generate related words using an AI model that inputs the social media activity data of the patient and outputs related words.

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

[0050] The selection unit can analyze the patient's past selection history and present the optimal image with priority. For example, images that the patient frequently selected in the past are automatically displayed as candidates. The selection unit can also preferentially suggest a selection method (such as voice or touch) that the patient used in the past. Furthermore, the selection unit can predict and suggest an image to be used in a specific time period from the patient's past selection history. This improves the efficiency of selection by presenting the optimal image based on the past selection history. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can present the optimal image using an AI model that inputs the patient's past selection history and outputs the optimal image.

[0051] The selection unit can filter images based on the patient's current health condition and environment. For example, if the patient is tired, a simple image with high visibility is preferentially displayed. The selection unit can also provide an image containing detailed information if the patient is relaxed. Furthermore, if the patient is in a specific environment, the selection unit can also suggest an image suitable for that environment. This reduces the burden of selection by providing images according to the health condition and environment. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can filter images using an AI model that inputs the patient's health condition and environmental data and filters images.

[0052] The prediction unit can adjust the prediction algorithm by referring to past prediction results. For example, the prediction algorithm is adjusted based on past prediction results. The prediction unit can also extract a pattern for performing highly accurate prediction from past prediction results. Furthermore, the prediction unit can analyze past prediction results and improve the accuracy of the prediction algorithm. As a result, the prediction algorithm is optimized based on past prediction results, thereby improving the accuracy of prediction. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can adjust the prediction algorithm using an AI model that uses past prediction result data as input and adjusts the prediction algorithm.

[0053] The selection unit can preferentially present images with high relevance based on the geographical location information of the patient. For example, when the patient is in a specific location, images related to the location are preferentially displayed. In addition, when the patient is moving, the selection unit can also suggest images with high relevance based on the current location. Furthermore, when the patient is in a specific area, the selection unit can also preferentially display images related to the area. This improves the efficiency of selection by providing images with high relevance based on geographical location information. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can present images with high relevance using an AI model that inputs geographical location information of the patient and outputs images with high relevance.

[0054] The prediction unit can set a prediction priority based on the patient's selection history. For example, the prediction unit preferentially predicts images that the patient has frequently selected in the past. The prediction unit can also predict images to be selected in a specific time period from the patient's selection history. Furthermore, the prediction unit can analyze the patient's selection history and preferentially predict the most relevant image. This improves the accuracy of prediction by determining the prediction priority based on the selection history. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can set the prediction priority using an AI model that inputs the patient's selection history data and sets the prediction priority.

[0055] The generation unit can customize the words to be generated based on the geographical location information of the patient. For example, when the patient is in a specific location, the generation unit generates words related to the location. In addition, when the patient is moving, the generation unit can also generate highly relevant words based on the current location. Furthermore, when the patient is in a specific area, the generation unit can also generate words related to the area. This improves the quality of communication by customizing words based on geographical location information. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can customize words using an AI model that uses the geographical location information of the patient as an input and customizes words.

[0056] The selection unit can analyze the patient's social media activity and present relevant images. For example, the selection unit can suggest relevant images based on the content frequently posted by the patient on social media. The selection unit can also preferentially display images shared by the patient's social media friends. Furthermore, the selection unit can analyze the patient's social media activity history and suggest highly relevant images. This improves the efficiency of selection by providing highly relevant images based on social media activity. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can present relevant images using an AI model that inputs the patient's social media activity data and outputs related images.

[0057] The process flow of the first embodiment will be briefly described below.

[0058] Step 1: The selection unit selects one image with which the patient wants to communicate. Images selected by the patient include, but are not limited to, images of food, drinks, places, and items. The selection unit can select an image using, for example, a touch screen. The selection unit can also select an image using voice input. For example, when the patient says "food," an image of food is displayed. Step 2: The prediction unit uses a generation AI to predict the next image based on the image selected by the selection unit. For example, if an image of a meal is selected, an image of a drink will be presented next. Step 3: The generator uses the generation AI to create words based on the images predicted by the predictor and speaks them out loud. For example, it generates the phrase "I want to eat" and speaks it out loud.

[0059] (Example 2) The communication support system according to the embodiment of the present invention is a system that supports communication for patients with aphasia. In this system, the patient selects one image with which he or she wants to communicate, and the generation AI predicts and presents the next image based on the image. This allows the patient to easily find the image he or she wants to choose next. Furthermore, the generation AI can also create words based on the selected image and utter them by voice. For example, if the patient wants to eat, the patient selects an image of food. This information is input to the generation AI. Next, the generation AI analyzes the input information and predicts the next image. The generation AI presents the image the patient will want to choose next based on the selected image. For example, if an image of food is selected, an image of a drink is presented next. Furthermore, the generation AI can create words based on the selected image and utter them by voice. For example, the word "I want to eat" is generated and uttered by voice. In this way, the patient's intention can be clearly conveyed. This system allows the patient to communicate his or her intention with fewer options, greatly reducing the burden of communication. By the generation AI predicting the next image, the patient can easily find the next image he or she wants to choose. Furthermore, the function of the generation AI creating words and uttering them by voice allows the patient's intention to be conveyed more clearly. This enables the communication support system to clearly convey the intentions of aphasic patients.

[0060] The communication support system according to the embodiment includes a selection unit, a prediction unit, and a generation unit. The selection unit selects one image with which the patient wants to communicate. The image selected by the patient includes, for example, a meal, a drink, a place, an item, and the like, but is not limited to such examples. The selection unit can select an image using, for example, a touch screen. The selection unit can also select an image using voice input. For example, when the patient says "meal", an image of the meal is displayed. The prediction unit uses a generation AI to predict an image that will come next based on the image selected by the selection unit. For example, when an image of a meal is selected, the prediction unit presents an image of a drink next. The prediction unit can use the generation AI to predict an image that will come next based on the selected image. For example, when an image of a meal is selected, the generation AI presents an image of a drink next. The prediction unit can also use the generation AI to predict an image that will come next based on the selected image. For example, when an image of a meal is selected, the generation AI presents an image of a drink next. The generation unit uses the generation AI to create words based on the image predicted by the prediction unit and utter them by voice. The generating unit generates, for example, the phrase "I want to eat" and issues it by voice. The generating unit can use a generating AI to create words based on a selected image and issue them by voice. For example, the generating AI generates the phrase "I want to eat" and issues it by voice. The generating unit can also use a generating AI to create words based on a selected image and issue them by voice. For example, the generating AI generates the phrase "I want to eat" and issues it by voice. As a result, the communication support system according to the embodiment can clearly communicate the intention of the aphasia patient. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generating unit can generate words using an AI model that takes a selected image as input and outputs words. As a result, the communication support system according to the embodiment can clearly communicate the intention of the aphasia patient.

[0061] The selection unit selects one image with which the patient wants to communicate. The images selected by the patient include, but are not limited to, for example, food, drink, places, and items. The selection unit can select an image using, for example, a touch screen. The touch screen allows intuitive operation, and the patient can easily select an image just by touching it with his / her finger. The selection unit can also select an image using voice input. Voice input is particularly effective for patients with limited hand and finger movement, and uses voice recognition technology to analyze the patient's utterance and display a corresponding image. For example, when the patient says "meal," an image of the meal is displayed. Furthermore, the selection unit can also use gesture recognition technology to analyze the patient's hand and face movements and select an image. This allows the selection unit to provide various input means and enable flexible operation according to the patient's needs and situation. The selection unit also has a function of recording the patient's selection history and learning past selection patterns to assist the patient in making a smoother selection the next time. For example, the selection unit reduces the effort required for selection by preferentially displaying images that the patient frequently selects. The selection unit also provides a function for selecting multiple images at the same time, allowing the patient to convey multiple intentions at once, thereby enabling the selection unit to efficiently and effectively support communication with the patient.

[0062] The prediction unit uses the generation AI to predict the next image based on the image selected by the selection unit. The generation AI learns past data and patterns and can predict the next image based on the selected image with high accuracy. For example, if an image of a meal is selected, it will present an image of a drink next. The generation AI has learned the association between meals and drinks and predicts that a drink will be needed after the meal. The prediction unit can also predict the next image by taking into account the context of the selected image. For example, if the selected image of the meal is breakfast, it will present an image of coffee or juice next. The prediction unit can predict the next image by using the generation AI. Furthermore, the prediction unit can predict the next image by taking into account the patient's past selection history. For example, if the patient has a tendency to select a drink after a meal in the past, the prediction unit learns that pattern and presents an image of a drink next. This allows the prediction unit to present an appropriate image according to the patient's needs and facilitate smooth communication. The prediction unit can improve the accuracy of prediction by regularly updating the learning model of the generation AI and reflecting the latest data and trends. This enables the prediction unit to always provide highly accurate predictions and support communication with patients.

[0063] The generation unit uses the generation AI to create words based on the image predicted by the prediction unit and recites them by voice. The generation unit generates, for example, the word "I want to eat" and recites it by voice. The generation AI understands the context of the selected image and the predicted image, and generates appropriate words based on that. For example, if an image of a meal is selected and an image of a drink is predicted, the generation AI can generate the words "I want to eat, so I also want a drink." The generation unit recites the generated words by voice using speech synthesis technology. The speech synthesis technology generates voice with natural pronunciation and intonation, and can clearly convey the patient's intentions. The generation unit can use the generation AI to create words based on the selected image and recite them by voice. For example, the generation AI generates the word "I want to eat" and recites it by voice. The generation unit can also use the generation AI to create words based on the selected image and recite them by voice. For example, the generation AI generates the word "I want to eat" and recites it by voice. The generation unit can use the generation AI to create words based on the selected image and recite them by voice. For example, the generation AI generates the words "I want to eat" and speaks them out loud. The generation unit can also use the generation AI to create words based on a selected image and speak them out loud. For example, the generation AI generates the words "I want to eat" and speaks them out loud. This allows the communication support system according to the embodiment to clearly communicate the intention of the aphasia patient. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate words using an AI model that takes a selected image as input and outputs words. This allows the communication support system according to the embodiment to clearly communicate the intention of the aphasia patient.

[0064] The prediction unit can predict the next image to be displayed using the generation AI. The prediction unit can predict the next image based on the selected image using the generation AI, for example. For example, if an image of a meal is selected, an image of a drink is presented next. The prediction unit can predict the next image based on the selected image using the generation AI. For example, if an image of a meal is selected, an image of a drink is presented next. The prediction unit can also predict the next image based on the selected image using the generation AI. For example, if an image of a meal is selected, an image of a drink is presented next. As a result, by using the generation AI, the prediction accuracy of the next image is improved. Part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can predict the next image using an AI model that inputs the selected image and outputs the next image.

[0065] The generation unit can generate words using a generation AI. The generation unit, for example, uses a generation AI to create words based on a selected image. For example, the generation unit generates the word "I want to eat". The generation unit can create words based on a selected image using a generation AI. For example, the generation AI generates the word "I want to eat". The generation unit can also create words based on a selected image using a generation AI. For example, the generation AI generates the word "I want to eat". As a result, the use of the generation AI improves the accuracy of word generation. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can generate words using an AI model that takes a selected image as input and outputs words.

[0066] The generation unit can output the speech using the generation AI. The generation unit can use the generation AI to create words based on the selected image and output them by speech. For example, the generation AI generates the word "I want to eat" and outputs it by speech. The generation unit can use the generation AI to create words based on the selected image and output them by speech. For example, the generation AI generates the word "I want to eat" and outputs it by speech. The generation unit can also use the generation AI to create words based on the selected image and output them by speech. For example, the generation AI generates the word "I want to eat" and outputs it by speech. As a result, the speech accuracy of the speech is improved by using the generation AI. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate words and output them by speech using an AI model that takes a selected image as input and outputs words.

[0067] The selection unit can select one image with which the patient wants to communicate. The selection unit, for example, selects one image with which the patient wants to communicate. Images selected by the patient include, for example, meals, drinks, places, objects, etc., but are not limited to such examples. The selection unit can select an image using, for example, a touch screen. The selection unit can also select an image using voice input. For example, when the patient says "meal," an image of a meal is displayed. This makes it easier for the patient to select an image with which he or she wants to communicate. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can predict the next image using an AI model that takes the image selected by the patient as input and outputs the image that will come next.

[0068] The selection unit can analyze the patient's emotions and adjust the image selection method based on the analyzed patient's emotions. For example, when the patient is stressed, the selection unit provides a simple interface and minimizes the image selection procedure. In addition, when the patient is relaxed, the selection unit can provide detailed selection options and suggest a customizable selection method. Furthermore, when the patient is in a hurry, the selection unit can prioritize voice input and enable quick image selection. This reduces the burden of selection by adjusting the image selection method according to the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can adjust the image selection method using an AI model that receives the patient's emotion data as an input and adjusts the image selection method.

[0069] The selection unit can analyze the patient's past selection history and present the optimal image with priority. For example, the selection unit automatically displays images that the patient has frequently selected in the past as candidates. The selection unit can also preferentially suggest a selection method (such as voice or touch) that the patient has used in the past. Furthermore, the selection unit can predict and suggest an image to be used in a specific time period from the patient's past selection history. This improves the efficiency of selection by presenting the optimal image based on the past selection history. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can present the optimal image using an AI model that inputs the patient's past selection history and outputs the optimal image.

[0070] The selection unit can filter images based on the patient's current health condition and environment. For example, when the patient is tired, the selection unit preferentially displays images that are simple and highly visible. In addition, when the patient is relaxed, the selection unit can also provide images containing detailed information. Furthermore, when the patient is in a specific environment, the selection unit can also suggest images suitable for that environment. This reduces the burden of selection by providing images according to the health condition and environment. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can filter images using an AI model that inputs the patient's health condition and environmental data and filters images.

[0071] The selection unit can analyze the patient's emotions and adjust the number of images based on the analyzed patient's emotions. For example, when the patient is stressed, the selection unit presents a small number of images to reduce the burden of selection. Also, when the patient is relaxed, the selection unit can present many images to increase the options. Furthermore, when the patient is in a hurry, the selection unit can present a small number of the most relevant images. This reduces the burden of selection by adjusting the number of images according to the emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can adjust the number of images using an AI model that inputs the patient's emotion data and adjusts the number of images.

[0072] The selection unit can preferentially present images with high relevance based on the geographical location information of the patient. For example, when the patient is in a specific location, the selection unit preferentially displays images related to the location. In addition, when the patient is moving, the selection unit can also suggest images with high relevance based on the current location. Furthermore, when the patient is in a specific area, the selection unit can also preferentially display images related to the area. This improves the efficiency of selection by providing images with high relevance based on geographical location information. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can present images with high relevance using an AI model that inputs geographical location information of the patient and outputs images with high relevance.

[0073] The selection unit can analyze the patient's social media activity and present relevant images. The selection unit can, for example, suggest relevant images based on the content frequently posted by the patient on social media. The selection unit can also preferentially display images shared by the patient's social media friends. Furthermore, the selection unit can analyze the patient's social media activity history and suggest highly relevant images. This improves the efficiency of selection by providing highly relevant images based on social media activity. Some or all of the above-mentioned processing in the selection unit can be performed, for example, using AI or without AI. For example, the selection unit can present relevant images using an AI model that inputs the patient's social media activity data and outputs related images.

[0074] The prediction unit can analyze the patient's emotions and adjust the accuracy of the prediction based on the analyzed patient's emotions. For example, when the patient is relaxed, the prediction unit performs detailed prediction. Also, when the patient is in a hurry, the prediction unit can perform quick prediction. Furthermore, when the patient is stressed, the prediction unit can perform simple prediction. This improves the accuracy of the prediction by adjusting the accuracy of the prediction according to the emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can adjust the accuracy of the prediction using an AI model that inputs the patient's emotion data and adjusts the accuracy of the prediction.

[0075] The prediction unit can adjust the prediction algorithm by referring to past prediction results. The prediction unit adjusts the prediction algorithm based on, for example, past prediction results. The prediction unit can also extract a pattern for performing highly accurate prediction from the past prediction results. Furthermore, the prediction unit can analyze past prediction results and improve the accuracy of the prediction algorithm. As a result, the prediction algorithm is optimized based on the past prediction results, thereby improving the accuracy of prediction. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can adjust the prediction algorithm using an AI model that uses past prediction result data as input and adjusts the prediction algorithm.

[0076] The prediction unit can set a prediction priority based on the patient's selection history. For example, the prediction unit preferentially predicts images that the patient has frequently selected in the past. The prediction unit can also predict images to be selected in a specific time period from the patient's selection history. Furthermore, the prediction unit can analyze the patient's selection history and preferentially predict the most relevant image. This improves the accuracy of prediction by determining the prediction priority based on the selection history. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can set the prediction priority using an AI model that inputs the patient's selection history data and sets the prediction priority.

[0077] The prediction unit can analyze the patient's emotions and adjust the display method of the prediction result based on the analyzed patient's emotions. For example, when the patient is nervous, the prediction unit provides a simple and highly visible display method. Furthermore, when the patient is relaxed, the prediction unit can also provide a display method including detailed information. Furthermore, when the patient is in a hurry, the prediction unit can also provide a display method that focuses on the main points. This improves the visibility of the prediction result by adjusting the display method according to the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can adjust the display method of the prediction result using an AI model that inputs the patient's emotion data and adjusts the display method of the prediction result.

[0078] The prediction unit can customize the prediction result based on the geographic location information of the patient. For example, when the patient is in a specific location, the prediction unit provides a prediction result related to the location. In addition, when the patient is moving, the prediction unit can also customize the prediction result based on the current location. Furthermore, when the patient is in a specific area, the prediction unit can also provide a prediction result related to the area. This improves the accuracy of the prediction by customizing the prediction result based on the geographic location information. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can customize the prediction result using an AI model that uses the geographic location information of the patient as an input and customizes the prediction result.

[0079] The prediction unit can analyze the patient's social media activity and present a relevant prediction result. The prediction unit can provide a relevant prediction result based on, for example, the content frequently posted by the patient on social media. The prediction unit can also present a prediction result based on the content shared by the patient's social media friends. Furthermore, the prediction unit can analyze the patient's social media activity history and provide a highly relevant prediction result. This improves the accuracy of prediction by providing a highly relevant prediction result based on social media activity. Some or all of the above-mentioned processing in the prediction unit can be performed, for example, using AI or can be performed without using AI. For example, the prediction unit can present a relevant prediction result using an AI model that inputs the patient's social media activity data and outputs a related prediction result.

[0080] The generation unit can analyze the patient's emotions and adjust the expression method of words to be generated based on the analyzed emotion of the patient. For example, when the patient is relaxed, the generation unit uses a relaxed expression method. In addition, when the patient is in a hurry, the generation unit can also use a concise and quick expression method. Furthermore, when the patient is excited, the generation unit can also use a visually stimulating expression method. Thereby, the quality of communication is improved by adjusting the expression method of words according to emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can adjust the expression method of words using an AI model that inputs the emotion data of the patient and adjusts the expression method of words.

[0081] The generation unit can adjust the generation algorithm by referring to past generation results. The generation unit adjusts the generation algorithm based on, for example, past generation results. The generation unit can also extract a pattern for performing highly accurate generation from the past generation results. Furthermore, the generation unit can analyze past generation results and improve the accuracy of the generation algorithm. As a result, the generation algorithm is optimized based on the past generation results, thereby improving the accuracy of generation. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can adjust the generation algorithm using an AI model that uses past generation result data as input and adjusts the generation algorithm.

[0082] The generating unit can set the priority of the words to be generated based on the selection history of the patient. For example, the generating unit preferentially generates words that the patient has frequently used in the past. The generating unit can also predict and generate words to be used in a specific time period from the selection history of the patient. Furthermore, the generating unit can analyze the selection history of the patient and preferentially generate the most relevant words. This improves the accuracy of generation by determining the priority of words based on the selection history. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can set the priority of words using an AI model that inputs the selection history data of the patient and sets the priority of words.

[0083] The generation unit can analyze the patient's emotions and adjust the length of the words to be generated based on the analyzed patient's emotions. For example, when the patient is in a hurry, the generation unit generates short words that are to the point. In addition, when the patient is relaxed, the generation unit can also generate longer words that include detailed explanations. Furthermore, when the patient is excited, the generation unit can also generate words that include visually stimulating expressions. This improves the quality of communication by adjusting the length of words according to emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can adjust the length of words using an AI model that inputs the patient's emotion data and adjusts the length of words.

[0084] The generating unit can customize the words to be generated based on the geographical location information of the patient. For example, when the patient is in a specific location, the generating unit generates words related to the location. In addition, when the patient is moving, the generating unit can also generate highly relevant words based on the current location. Furthermore, when the patient is in a specific area, the generating unit can also generate words related to the area. This improves the quality of communication by customizing the words based on the geographical location information. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI or may be performed without using AI. For example, the generating unit can customize words using an AI model that uses the geographical location information of the patient as an input and customizes words.

[0085] The generating unit can analyze the social media activity of the patient and generate related words. The generating unit can generate related words based on, for example, the content frequently posted by the patient on social media. The generating unit can also generate words based on the content shared by the patient's social media friends. Furthermore, the generating unit can analyze the social media activity history of the patient and generate highly relevant words. This improves the quality of communication by providing highly relevant words based on social media activity. Some or all of the above-mentioned processing in the generating unit can be performed, for example, using AI or can be performed without using AI. For example, the generating unit can generate related words using an AI model that inputs the social media activity data of the patient and outputs related words.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The selection unit can analyze the patient's past selection history and present the optimal image with priority. For example, images that the patient frequently selected in the past are automatically displayed as candidates. The selection unit can also preferentially suggest a selection method (such as voice or touch) that the patient used in the past. Furthermore, the selection unit can predict and suggest an image to be used in a specific time period from the patient's past selection history. This improves the efficiency of selection by presenting the optimal image based on the past selection history. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can present the optimal image using an AI model that inputs the patient's past selection history and outputs the optimal image.

[0088] The prediction unit can analyze the patient's emotions and adjust the accuracy of the prediction based on the analyzed patient's emotions. For example, when the patient is relaxed, a detailed prediction is made. In addition, when the patient is in a hurry, the prediction unit can also make a quick prediction. Furthermore, when the patient is stressed, the prediction unit can also make a simple prediction. Thereby, the accuracy of the prediction is improved by adjusting the accuracy of the prediction according to the emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the prediction unit can adjust the accuracy of the prediction using an AI model that inputs the patient's emotion data and adjusts the accuracy of the prediction.

[0089] The generation unit can analyze the patient's emotions and adjust the expression method of words to be generated based on the analyzed emotion of the patient. For example, when the patient is relaxed, a relaxed expression method is used. In addition, when the patient is in a hurry, the generation unit can also use a concise and quick expression method. Furthermore, when the patient is excited, the generation unit can also use a visually stimulating expression method. Thereby, the quality of communication is improved by adjusting the expression method of words according to emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can adjust the expression method of words using an AI model that inputs the emotion data of the patient and adjusts the expression method of words.

[0090] The selection unit can filter images based on the patient's current health condition and environment. For example, if the patient is tired, a simple image with high visibility is preferentially displayed. The selection unit can also provide an image containing detailed information if the patient is relaxed. Furthermore, if the patient is in a specific environment, the selection unit can also suggest an image suitable for that environment. This reduces the burden of selection by providing images according to the health condition and environment. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can filter images using an AI model that inputs the patient's health condition and environmental data and filters images.

[0091] The prediction unit can adjust the prediction algorithm by referring to past prediction results. For example, the prediction algorithm is adjusted based on past prediction results. The prediction unit can also extract a pattern for performing highly accurate prediction from past prediction results. Furthermore, the prediction unit can analyze past prediction results and improve the accuracy of the prediction algorithm. As a result, the prediction algorithm is optimized based on past prediction results, thereby improving the accuracy of prediction. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can adjust the prediction algorithm using an AI model that uses past prediction result data as input and adjusts the prediction algorithm.

[0092] The generation unit can analyze the patient's emotions and adjust the length of the words to be generated based on the analyzed patient's emotions. For example, if the patient is in a hurry, the generation unit generates short words that are to the point. In addition, if the patient is relaxed, the generation unit can also generate longer words that include detailed explanations. Furthermore, if the patient is excited, the generation unit can also generate words that include visually stimulating expressions. This improves the quality of communication by adjusting the length of words according to emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can adjust the length of words using an AI model that inputs the patient's emotion data and adjusts the length of words.

[0093] The selection unit can preferentially present images with high relevance based on the geographical location information of the patient. For example, when the patient is in a specific location, images related to the location are preferentially displayed. In addition, when the patient is moving, the selection unit can also suggest images with high relevance based on the current location. Furthermore, when the patient is in a specific area, the selection unit can also preferentially display images related to the area. This improves the efficiency of selection by providing images with high relevance based on geographical location information. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can present images with high relevance using an AI model that inputs geographical location information of the patient and outputs images with high relevance.

[0094] The prediction unit can set a prediction priority based on the patient's selection history. For example, the prediction unit preferentially predicts images that the patient has frequently selected in the past. The prediction unit can also predict images to be selected in a specific time period from the patient's selection history. Furthermore, the prediction unit can analyze the patient's selection history and preferentially predict the most relevant image. This improves the accuracy of prediction by determining the prediction priority based on the selection history. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can set the prediction priority using an AI model that inputs the patient's selection history data and sets the prediction priority.

[0095] The generation unit can customize the words to be generated based on the geographical location information of the patient. For example, when the patient is in a specific location, the generation unit generates words related to the location. In addition, when the patient is moving, the generation unit can also generate highly relevant words based on the current location. Furthermore, when the patient is in a specific area, the generation unit can also generate words related to the area. This improves the quality of communication by customizing words based on geographical location information. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can customize words using an AI model that uses the geographical location information of the patient as an input and customizes words.

[0096] The selection unit can analyze the patient's social media activity and present relevant images. For example, the selection unit can suggest relevant images based on the content frequently posted by the patient on social media. The selection unit can also preferentially display images shared by the patient's social media friends. Furthermore, the selection unit can analyze the patient's social media activity history and suggest highly relevant images. This improves the efficiency of selection by providing highly relevant images based on social media activity. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can present relevant images using an AI model that inputs the patient's social media activity data and outputs related images.

[0097] The process flow of the second embodiment will be briefly described below.

[0098] Step 1: The selection unit selects one image with which the patient wants to communicate. Images selected by the patient include, but are not limited to, images of food, drinks, places, and items. The selection unit can select an image using, for example, a touch screen. The selection unit can also select an image using voice input. For example, when the patient says "food," an image of food is displayed. Step 2: The prediction unit uses a generation AI to predict the next image based on the image selected by the selection unit. For example, if an image of a meal is selected, an image of a drink will be presented next. Step 3: The generator uses the generation AI to create words based on the images predicted by the predictor and speaks them out loud. For example, it generates the phrase "I want to eat" and speaks it out loud.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements including the above-mentioned selection unit, prediction unit, and generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is realized using a touch screen or a voice input device of the smart device 14. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the next image using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates words based on the predicted image and speaks them out loud. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements including the above-mentioned selection unit, prediction unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized using a voice input device of the smart glasses 214. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the next image using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates words based on the predicted image and speaks them out loud. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements including the above-mentioned selection unit, prediction unit, and generation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the selection unit is realized using a voice input device of the headset type terminal 314. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the next image using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates words based on the predicted image and speaks them out loud. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] 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).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements including the above-mentioned selection unit, prediction unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized using a voice input device of the robot 414. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the next image using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates words based on the predicted image and speaks them out loud. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Appendix 1) A selection unit for selecting an image; a prediction unit that predicts a next image to be displayed based on the image selected by the selection unit; A generation unit that generates words based on the image predicted by the prediction unit and outputs the words by voice. A system characterized by: (Appendix 2) The prediction unit is Using generative AI to predict the next image to be displayed 2. The system of claim 1, (Appendix 3) The generation unit is Generate words using generative AI 2. The system of claim 1, (Appendix 4) The generation unit is Use generative AI to output audio 2. The system of claim 1, (Appendix 5) The selection unit is The patient selects one image they want to communicate 2. The system of claim 1, (Appendix 6) The selection unit is Analyzing patient emotions and adjusting image selection methods based on the analyzed patient emotions 2. The system of claim 1, (Appendix 7) The selection unit is Analyze the patient's past selection history and present appropriate images preferentially 2. The system of claim 1, (Appendix 8) The selection unit is Filtering images based on the patient's current health status and environment 2. The system of claim 1, (Appendix 9) The selection unit is Analyze the patient's emotions and adjust the number of images based on the analyzed patient's emotions. 2. The system of claim 1, (Appendix 10) The selection unit is Prioritize relevant images based on the patient's geographic location 2. The system of claim 1, (Appendix 11) The selection unit is Analyze patients' social media activity and present relevant images 2. The system of claim 1, (Appendix 12) The prediction unit is Analyze the patient's emotions and adjust the accuracy of the prediction based on the analyzed patient's emotions 2. The system of claim 1, (Appendix 13) The prediction unit is Refer to past prediction results to adjust the prediction algorithm 2. The system of claim 1, (Appendix 14) The prediction unit is Prioritize predictions based on patient selection history 2. The system of claim 1, (Appendix 15) The prediction unit is Analyze the patient's emotions and adjust the way the prediction results are displayed based on the analyzed patient's emotions 2. The system of claim 1, (Appendix 16) The prediction unit is Customize predictions based on the patient's geographic location 2. The system of claim 1, (Appendix 17) The prediction unit is Analyze patients' social media activity and provide relevant predictions 2. The system of claim 1, (Appendix 18) The generation unit is Analyze the patient's emotions and adjust the way words are expressed based on the analyzed patient's emotions. 2. The system of claim 1, (Appendix 19) The generation unit is Adjust the generation algorithm by referring to past generation results 2. The system of claim 1, (Appendix 20) The generation unit is Prioritize generated words based on patient selection history 2. The system of claim 1, (Appendix 21) The generation unit is Analyze the patient's emotions and adjust the length of the words to be generated based on the analyzed patient's emotions. 2. The system of claim 1, (Appendix 22) The generation unit is Customize generated messages based on the patient's geographic location 2. The system of claim 1, (Appendix 23) The generation unit is Analyze patients' social media activity and generate relevant words 2. The system of claim 1, [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A selection unit for selecting an image; a prediction unit that predicts a next image to be displayed based on the image selected by the selection unit; A generation unit that generates words based on the image predicted by the prediction unit and outputs the words by voice. A system characterized by:

2. The prediction unit is Using generative AI to predict the next image to be displayed 2. The system of claim 1 .

3. The generation unit is Generate words using generative AI 2. The system of claim 1 .

4. The generation unit is Output as voice using generative AI 2. The system of claim 1 .

5. The selection unit is The patient selects one image they want to communicate 2. The system of claim 1 .

6. The selection unit is Analyzing patient emotions and adjusting image selection methods based on the analyzed patient emotions 2. The system of claim 1 .

7. The selection unit is Analyze the patient's past selection history and present appropriate images preferentially 2. The system of claim 1 .

8. The selection unit is Filtering images based on the patient's current health status and environment 2. The system of claim 1 .

9. The selection unit is Analyze the patient's emotions and adjust the number of images based on the analyzed patient's emotions.

2. The system of claim 1 .

10. The selection unit is Prioritize relevant images based on the patient's geographic location 2. The system of claim 1 .

Images