System

Abstract

To provide a system for supporting a speech-impaired person to conduct a conversation smoothly.SOLUTION: A system includes a collection part, a prediction part, and a presentation part. The collection part collects a user's knowledge and language, and factors that are sources of conversation. The prediction part predicts a content of a next conversation on the basis of the data collected by the collection part. The presentation part presents the user with a conversation content predicted by the prediction part, and receives a selection.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional techniques do not provide sufficient support for people with speech disorders to converse smoothly, and there is room for improvement.

[0005] The system according to the embodiment aims to support people with speech impediments in carrying out smooth conversations. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a prediction unit, and a presentation unit. The collection unit collects a user's knowledge or language, and elements that are the basis of a conversation. The prediction unit predicts the content of the next conversation based on the data collected by the collection unit. The presentation unit presents the conversation content predicted by the prediction unit to the user and accepts a selection. Effect of the Invention

[0007] The system according to the embodiment can assist people with speech impediments in having smooth conversations. [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 new mechanism for people with language disabilities to communicate in conversation. This communication support system allows AI to learn the knowledge, language, and conversational elements of people with language disabilities. Next, the AI ​​predicts and suggests what the person wants to talk about next. The user can smoothly proceed with the conversation by simply selecting an appropriate option from the proposed options. This mechanism enables more natural and smooth communication than the conventional method using a dial or PC. In addition, since the AI ​​learns the person's conversation patterns, it becomes possible to make more accurate predictions over time. For example, when a person with a language disability talks with a family member, the AI ​​suggests what to talk about next based on the past conversation history and the current situation. The user can smoothly proceed with the conversation by simply selecting an appropriate option from the proposed options. In this way, by utilizing AI, communication of people with language disabilities can be significantly improved. As a result, the communication support system can significantly improve communication of people with language disabilities.

[0029] The communication support system according to the embodiment includes a collection unit, a prediction unit, and a presentation unit. The collection unit collects the user's knowledge, language, and elements that are the source of conversation. The user's knowledge includes, for example, specialized knowledge and general knowledge, but is not limited to such examples. The collection unit can collect, for example, the user's past conversation history and topics of interest. For example, the collection unit can collect topics and search history that the user frequently talks about. The prediction unit predicts the next conversation content based on the data collected by the collection unit. The prediction unit can predict the next conversation content based on, for example, data learned by the AI. The data learned by the AI ​​includes, for example, text data, voice data, image data, etc., but is not limited to such examples. The prediction unit can predict the next conversation content by learning, for example, the AI's past conversation patterns. The presentation unit presents the conversation content predicted by the prediction unit to the user and accepts selection. The presentation unit can provide, for example, an interface that makes it easy for the user to select. The interface includes, for example, the size, color, and layout of buttons, but is not limited to such examples. As a result, the communication support system according to the embodiment can support communication for people with language disorders. Some or all of the above-mentioned processes in the collection unit, prediction unit, and presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past conversation history to the AI ​​and have the AI ​​select topics of interest. The prediction unit can predict the next conversation content based on data learned by the AI. The presentation unit can present the conversation content predicted by the AI ​​to the user and accept a selection.

[0030] The collection unit collects the user's knowledge, language, and elements that are the source of conversation. Specifically, the collection unit collects the user's specialized knowledge, general knowledge, past conversation history, topics of interest, search history, etc. For example, the collection unit can collect topics and search history that the user frequently talks about. This allows the collection unit to grasp the user's individual characteristics and interests in detail. The collection unit analyzes the user's past conversation history to identify what topics the user is interested in and what kind of language the user uses. In addition, the collection unit also collects keywords that the user has searched for on the Internet and the history of web pages that the user has viewed, to gain a deeper understanding of the user's interests and concerns. This allows the collection unit to comprehensively grasp the user's knowledge and interests and provide information that is useful for the next conversation. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and can be accessed by the prediction unit and the presentation unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The prediction unit predicts the next conversation content based on the data collected by the collection unit. Specifically, the prediction unit can predict the next conversation content based on the data learned by the AI. The data learned by the AI ​​includes, but is not limited to, text data, voice data, image data, etc. The prediction unit can, for example, learn the past conversation patterns of the AI ​​and predict the next content to be discussed. The AI ​​uses natural language processing technology to analyze the user's past conversation history and topics of interest and predict the next content to be discussed. For example, the AI ​​can predict the next conversation theme based on topics that the user has frequently talked about in the past and keywords that the user has recently searched. Furthermore, the AI ​​learns the user's language patterns and expression methods and generates conversation content that the user feels is natural. This allows the prediction unit to provide conversation content based on the user's interests and concerns, and to smoothly advance communication. The prediction unit can continuously revise the prediction result based on data updated in real time to respond to the latest situation. For example, if the user shows interest in a new topic, the prediction unit immediately takes in new data and updates the prediction result. In addition, the prediction unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data and issue an early warning, allowing the prediction unit to handle not only real-time situation assessment but also long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The presentation unit presents the conversation contents predicted by the prediction unit to the user and accepts the selection. Specifically, an interface that allows the user to easily make a selection can be provided. The interface includes, but is not limited to, the size, color, and layout of buttons. For example, large buttons and bright colors are adopted to make it easier for the user to make a visual selection, and a layout that allows intuitive operation is provided. The presentation unit presents the predicted conversation contents to the user visually or audibly, and advances the next conversation based on the contents selected by the user. For example, the content of the next conversation is generated based on the topic selected by the user and presented to the user. Furthermore, the presentation unit can collect user feedback and continuously improve the accuracy and effectiveness of the interface. For example, the interface layout and button arrangement are optimized based on the contents selected by the user and the operation history. In addition, the presentation unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only visual presentation but also audio guidance and vibration notification. As a result, the presentation unit can quickly and reliably present the conversation contents to the user, and smoothly advance communication.

[0033] The collection unit can collect past conversation history or topics of interest. The collection unit, for example, collects past conversation history. The past conversation history includes, for example, a conversation history for a certain period of time and a history related to a specific topic, but is not limited to such examples. The collection unit can collect, for example, topics that the user frequently talks about. The topics of interest include, for example, a user's search history and activities on social media, but are not limited to such examples. By collecting the past conversation history and topics of interest, more appropriate conversation content can be predicted. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs the past conversation history into AI and allows the AI ​​to select a topic of interest.

[0034] The prediction unit can predict the next conversation content based on the data learned by the AI. The prediction unit, for example, predicts the next conversation content based on the data learned by the AI. The data learned by the AI ​​includes, for example, text data, voice data, image data, etc., but is not limited to such examples. The prediction unit, for example, can learn the past conversation patterns of the AI ​​and predict the next content to be spoken. For example, the AI ​​uses an algorithm to predict the next content to be spoken based on the past conversation history. The AI ​​uses, for example, natural language processing technology to understand the flow and context of the conversation and predict the next content to be spoken. As a result, by predicting the next conversation content based on the data learned by the AI, more accurate conversation support is possible. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, the AI, or may be performed without using the AI. For example, the prediction unit can predict the next conversation content based on the data learned by the AI.

[0035] The presentation unit can provide an interface that makes it easy for the user to select. The presentation unit provides, for example, an interface that makes it easy for the user to select. The interface includes, for example, the size, color, and layout of the button, but is not limited to such examples. The presentation unit can, for example, devise a design for the interface so that the user can easily select visually. For example, the size of the button can be adjusted to improve visibility. In addition, the color can be devised so that the user can intuitively select. Furthermore, the layout can be devised so that the user can select without hesitation. As a result, by providing an interface that makes it easy for the user to select, smooth conversation support is possible. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can present the conversation content predicted by AI to the user and accept the selection.

[0036] The collection unit can analyze the user's past conversation history and select an appropriate collection method. The collection unit, for example, analyzes the user's past conversation history and selects the optimal collection method. The collection unit, for example, preferentially collects topics that the user has frequently talked about in the past. The collection unit can also exclude topics that the user has avoided in the past from collection. Furthermore, the collection unit can analyze the user's past conversation patterns and select the most effective collection method. In this way, the optimal collection method can be selected by analyzing the user's past conversation history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past conversation history into AI and have the AI ​​select the optimal collection method.

[0037] The collection unit can perform filtering based on the user's current interests or activity status at the time of collection. The collection unit, for example, performs filtering based on the user's current interests and activity status at the time of collection. The collection unit, for example, preferentially collects topics in which the user is currently interested. The collection unit can also collect data related to the activity currently being performed by the user. Furthermore, the collection unit can filter unnecessary data based on the user's current interests. This makes it possible to eliminate unnecessary data by filtering based on the user's current interests and activity status. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current interests and activity status to AI and have the AI ​​perform filtering.

[0038] The collection unit can preferentially collect highly relevant data based on the geographical location information of the user at the time of collection. For example, the collection unit preferentially collects highly relevant data taking into account the geographical location information of the user at the time of collection. For example, the collection unit preferentially collects topics related to the location where the user is currently located. The collection unit can also collect area-specific data based on the geographical location information of the user. Furthermore, when the user is traveling, the collection unit can also preferentially collect data related to the travel destination. In this way, by taking into account the geographical location information of the user, highly relevant data can be preferentially collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and have the AI ​​select highly relevant data.

[0039] The collection unit can analyze the user's social media activity at the time of collection and collect related data. The collection unit, for example, analyzes the user's social media activity at the time of collection and collects related data. The collection unit, for example, collects topics that the user frequently mentions on social media. The collection unit can also collect topics that the user's social media friends are interested in. Furthermore, the collection unit can identify current interests from the user's social media activity and collect related data. In this way, related data can be collected by analyzing the user's social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI and have the AI ​​collect related data.

[0040] The prediction unit can predict the next conversation content by referring to the past conversation pattern at the time of prediction. The prediction unit, for example, predicts the next conversation content by referring to the past conversation pattern at the time of prediction. The prediction unit predicts the next conversation content based on, for example, phrases that the user has frequently used in the past. The prediction unit can also analyze the user's past conversation patterns and predict the next content to be discussed. Furthermore, the prediction unit can also make predictions taking into account topics that the user has avoided in the past. This makes it possible to more accurately predict the next conversation content by referring to the past conversation pattern. 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 input the past conversation pattern into the AI ​​and have the AI ​​predict the next conversation content.

[0041] The prediction unit can appropriately adjust the prediction algorithm based on the user's current situation and environment at the time of prediction. The prediction unit, for example, optimizes the prediction algorithm based on the user's current situation and environment at the time of prediction. The prediction unit, for example, predicts the next conversation content based on the user's current location. The prediction unit can also make predictions taking into account the user's current activity situation. Furthermore, the prediction unit can adjust the prediction algorithm based on the user's current emotional state. This enables more appropriate predictions by optimizing the prediction algorithm based on the user's current situation and environment. 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 input the user's current situation and environment into AI and have the AI ​​adjust the prediction algorithm.

[0042] The prediction unit can determine the priority of prediction by referring to the user's past behavior history at the time of prediction. The prediction unit, for example, determines the priority of prediction by referring to the user's past behavior history at the time of prediction. The prediction unit determines the priority of prediction based on, for example, behavior frequently performed by the user in the past. The prediction unit can also predict important behavior preferentially from the user's past behavior history. Furthermore, the prediction unit can analyze the user's past behavior pattern and make the most appropriate prediction. As a result, the priority of prediction can be determined by referring to the user's past behavior 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 input the user's past behavior history into AI and have the AI ​​determine the priority of prediction.

[0043] The prediction unit can improve the accuracy of prediction by referring to the user's related literature and materials at the time of prediction. The prediction unit can improve the accuracy of prediction by referring to the user's related literature and materials at the time of prediction, for example. The prediction unit can improve the accuracy of prediction based on the literature the user has referred to in the past, for example. The prediction unit can also analyze the user's related materials and predict the next conversation content. Furthermore, the prediction unit can also improve the accuracy of prediction based on the materials the user has used in the past. Thereby, the accuracy of prediction is improved by referring to the user's related literature and materials. 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 input the user's related literature and materials into AI to improve the accuracy of prediction in AI.

[0044] The presentation unit can select the optimal presentation method by referring to the user's past selection history at the time of presentation. For example, the presentation unit selects the optimal presentation method by referring to the user's past selection history at the time of presentation. The presentation unit selects the optimal presentation method based on, for example, the content selected by the user in the past. The presentation unit can also suggest an effective presentation method from the user's past selection history. Furthermore, the presentation unit can also select the optimal presentation method by taking into account the presentation method that the user avoided in the past. In this way, the optimal presentation method can be selected by referring to the user's past selection history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the user's past selection history into AI and have the AI ​​select the optimal presentation method.

[0045] The presentation unit can appropriately adjust the presentation content based on the user's current situation and environment at the time of presentation. For example, the presentation unit customizes the presentation content based on the user's current situation and environment at the time of presentation. The presentation unit presents appropriate content, for example, based on the user's current location. The presentation unit can also present optimal content in consideration of the user's current activity status. Furthermore, the presentation unit can present customized content based on the user's current emotional state. This makes it possible to present more appropriate content by customizing the presentation content based on the user's current situation and environment. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the user's current situation and environment to the AI ​​and cause the AI ​​to adjust the presentation content.

[0046] The presentation unit can select the optimal display method in consideration of the device information of the user at the time of presentation. For example, the presentation unit selects the optimal display method in consideration of the device information of the user at the time of presentation. For example, when the user is using a smartphone, the presentation unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the presentation unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smart watch, the presentation unit can also provide a simple and highly visible display method. This makes it possible to select the optimal display method by considering the device information of the user. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit inputs the device information of the user into AI and allows AI to select the optimal display method.

[0047] The presentation unit can analyze the user's social media activity and present related content at the time of presentation. For example, the presentation unit analyzes the user's social media activity and presents related content at the time of presentation. The presentation unit, for example, presents topics that the user frequently mentions on social media. The presentation unit can also present topics that the user's social media friends are interested in. Furthermore, the presentation unit can identify current interests from the user's social media activity and present related content. In this way, related content can be presented by analyzing the user's social media activity. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the user's social media activity into AI and have the AI ​​present related content.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The communication support system may further include a translation unit. The translation unit may translate the conversation content selected by the user into another language and display it through the presentation unit. For example, if the user wants to have a conversation in English, the translation unit may translate the conversation content in Japanese into English and display it on the presentation unit. The translation unit may also function as support for the user when learning a foreign language. Furthermore, the translation unit may perform translation in real time to facilitate communication between users who speak different languages. This enables communication across language barriers.

[0050] The prediction unit can predict the next conversation content by referring to the user's past purchase history. For example, it predicts topics related to products the user has purchased in the past. It can also predict the next conversation content based on the product categories the user frequently purchases. It can also predict the next topic to talk about based on reviews and ratings of products the user has purchased in the past. In this way, it is possible to more accurately predict the next conversation content by referring to the user's purchase history.

[0051] The presentation unit can present the conversation content by voice, taking into consideration the visual impairment of the user. For example, the presentation unit reads out the conversation content selected by the user by voice. The presentation unit can also provide an interface that allows the user to make selections by voice. Furthermore, the presentation unit can present the conversation content in sign language or subtitles, taking into consideration the hearing impairment of the user. This allows even users with visual or hearing impairments to smoothly proceed with the conversation.

[0052] The collection unit can customize the type of data to be collected based on the user's hobbies and preferences. For example, if the user is interested in music, topics related to music are preferentially collected. If the user is interested in sports, data related to sports can also be collected. Furthermore, if the user is interested in cooking, topics related to cooking can also be collected. This allows for more appropriate data collection by customizing the type of data to be collected based on the user's hobbies and preferences.

[0053] The prediction unit can predict the next conversation content by referring to the user's learning history. For example, the next conversation content is predicted based on the topics the user has learned in the past. The prediction unit can also predict the next content to be discussed taking into account the topic the user is currently learning. Furthermore, the prediction unit can predict related topics as a review of the content the user has learned in the past. In this way, the next conversation content can be predicted more accurately by referring to the user's learning history.

[0054] The presentation unit can adjust the format of the content to be presented by referring to the user's past feedback. For example, the content is presented in a format that the user has previously preferred. The content can also be presented in a different format, taking into account formats that the user has previously avoided. Furthermore, the content can also be presented in an optimal format based on feedback provided by the user in the past. In this way, the format of the content to be presented can be adjusted by referring to the user's past feedback.

[0055] The process flow of the first embodiment will be briefly described below.

[0056] Step 1: The collection unit collects the user's knowledge, language, and conversational elements. For example, the user's past conversation history, topics of interest, search history, etc. can be collected. The user's knowledge includes specialized knowledge and general knowledge. Step 2: The prediction unit predicts the next conversation content based on the data collected by the collection unit. For example, based on the data that the AI ​​has learned (text data, voice data, image data, etc.), it learns past conversation patterns and predicts what should be said next. Step 3: The presentation unit presents the conversation content predicted by the prediction unit to the user and accepts a selection. For example, the presentation unit provides an interface (such as button size, color, layout, etc.) that makes it easy for the user to make selections.

[0057] (Example 2) The communication support system according to the embodiment of the present invention is a new mechanism for people with language disabilities to communicate in conversation. This communication support system allows AI to learn the knowledge, language, and conversational elements of people with language disabilities. Next, the AI ​​predicts and suggests what the person wants to talk about next. The user can smoothly proceed with the conversation by simply selecting an appropriate option from the proposed options. This mechanism enables more natural and smooth communication than the conventional method using a dial or PC. In addition, since the AI ​​learns the person's conversation patterns, it becomes possible to make more accurate predictions over time. For example, when a person with a language disability talks with a family member, the AI ​​suggests what to talk about next based on the past conversation history and the current situation. The user can smoothly proceed with the conversation by simply selecting an appropriate option from the proposed options. In this way, by utilizing AI, communication of people with language disabilities can be significantly improved. As a result, the communication support system can significantly improve communication of people with language disabilities.

[0058] The communication support system according to the embodiment includes a collection unit, a prediction unit, and a presentation unit. The collection unit collects the user's knowledge, language, and elements that are the source of conversation. The user's knowledge includes, for example, specialized knowledge and general knowledge, but is not limited to such examples. The collection unit can collect, for example, the user's past conversation history and topics of interest. For example, the collection unit can collect topics and search history that the user frequently talks about. The prediction unit predicts the next conversation content based on the data collected by the collection unit. The prediction unit can predict the next conversation content based on, for example, data learned by the AI. The data learned by the AI ​​includes, for example, text data, voice data, image data, etc., but is not limited to such examples. The prediction unit can predict the next conversation content by learning, for example, the AI's past conversation patterns. The presentation unit presents the conversation content predicted by the prediction unit to the user and accepts selection. The presentation unit can provide, for example, an interface that makes it easy for the user to select. The interface includes, for example, the size, color, and layout of buttons, but is not limited to such examples. As a result, the communication support system according to the embodiment can support communication for people with language disorders. Some or all of the above-mentioned processes in the collection unit, prediction unit, and presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past conversation history to the AI ​​and have the AI ​​select topics of interest. The prediction unit can predict the next conversation content based on data learned by the AI. The presentation unit can present the conversation content predicted by the AI ​​to the user and accept a selection.

[0059] The collection unit collects the user's knowledge, language, and elements that are the source of conversation. Specifically, the collection unit collects the user's specialized knowledge, general knowledge, past conversation history, topics of interest, search history, etc. For example, the collection unit can collect topics and search history that the user frequently talks about. This allows the collection unit to grasp the user's individual characteristics and interests in detail. The collection unit analyzes the user's past conversation history to identify what topics the user is interested in and what kind of language the user uses. In addition, the collection unit also collects keywords that the user has searched for on the Internet and the history of web pages that the user has viewed, to gain a deeper understanding of the user's interests and concerns. This allows the collection unit to comprehensively grasp the user's knowledge and interests and provide information that is useful for the next conversation. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and can be accessed by the prediction unit and the presentation unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0060] The prediction unit predicts the next conversation content based on the data collected by the collection unit. Specifically, the prediction unit can predict the next conversation content based on the data learned by the AI. The data learned by the AI ​​includes, but is not limited to, text data, voice data, image data, etc. The prediction unit can, for example, learn the past conversation patterns of the AI ​​and predict the next content to be discussed. The AI ​​uses natural language processing technology to analyze the user's past conversation history and topics of interest and predict the next content to be discussed. For example, the AI ​​can predict the next conversation theme based on topics that the user has frequently talked about in the past and keywords that the user has recently searched. Furthermore, the AI ​​learns the user's language patterns and expression methods and generates conversation content that the user feels is natural. This allows the prediction unit to provide conversation content based on the user's interests and concerns, and to smoothly advance communication. The prediction unit can continuously revise the prediction result based on data updated in real time to respond to the latest situation. For example, if the user shows interest in a new topic, the prediction unit immediately takes in new data and updates the prediction result. In addition, the prediction unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data and issue an early warning, allowing the prediction unit to handle not only real-time situation assessment but also long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0061] The presentation unit presents the conversation contents predicted by the prediction unit to the user and accepts the selection. Specifically, an interface that allows the user to easily make a selection can be provided. The interface includes, but is not limited to, the size, color, and layout of buttons. For example, large buttons and bright colors are adopted to make it easier for the user to make a visual selection, and a layout that allows intuitive operation is provided. The presentation unit presents the predicted conversation contents to the user visually or audibly, and advances the next conversation based on the contents selected by the user. For example, the content of the next conversation is generated based on the topic selected by the user and presented to the user. Furthermore, the presentation unit can collect user feedback and continuously improve the accuracy and effectiveness of the interface. For example, the interface layout and button arrangement are optimized based on the contents selected by the user and the operation history. In addition, the presentation unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only visual presentation but also audio guidance and vibration notification. As a result, the presentation unit can quickly and reliably present the conversation contents to the user, and smoothly advance communication.

[0062] The collection unit can collect past conversation history or topics of interest. The collection unit, for example, collects past conversation history. The past conversation history includes, for example, a conversation history for a certain period of time and a history related to a specific topic, but is not limited to such examples. The collection unit can collect, for example, topics that the user frequently talks about. The topics of interest include, for example, a user's search history and activities on social media, but are not limited to such examples. By collecting the past conversation history and topics of interest, more appropriate conversation content can be predicted. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs the past conversation history into AI and allows the AI ​​to select a topic of interest.

[0063] The prediction unit can predict the next conversation content based on the data learned by the AI. The prediction unit, for example, predicts the next conversation content based on the data learned by the AI. The data learned by the AI ​​includes, for example, text data, voice data, image data, etc., but is not limited to such examples. The prediction unit, for example, can learn the past conversation patterns of the AI ​​and predict the next content to be spoken. For example, the AI ​​uses an algorithm to predict the next content to be spoken based on the past conversation history. The AI ​​uses, for example, natural language processing technology to understand the flow and context of the conversation and predict the next content to be spoken. As a result, by predicting the next conversation content based on the data learned by the AI, more accurate conversation support is possible. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, the AI, or may be performed without using the AI. For example, the prediction unit can predict the next conversation content based on the data learned by the AI.

[0064] The presentation unit can provide an interface that makes it easy for the user to select. The presentation unit provides, for example, an interface that makes it easy for the user to select. The interface includes, for example, the size, color, and layout of the button, but is not limited to such examples. The presentation unit can, for example, devise a design for the interface so that the user can easily select visually. For example, the size of the button can be adjusted to improve visibility. In addition, the color can be devised so that the user can intuitively select. Furthermore, the layout can be devised so that the user can select without hesitation. As a result, by providing an interface that makes it easy for the user to select, smooth conversation support is possible. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can present the conversation content predicted by AI to the user and accept the selection.

[0065] The collection unit can estimate the user's emotion and adjust the type of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the type of data to be collected based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. For example, the collection unit preferentially collects topics for relaxing the user when the user is excited. In addition, the collection unit can also collect topics for comforting the user when the user is sad. Furthermore, the collection unit can also collect topics that are even more enjoyable when the user is having fun. This allows more appropriate data collection by adjusting the type of data to be collected based on the user's emotion. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the type of data to be collected.

[0066] The collection unit can analyze the user's past conversation history and select an appropriate collection method. The collection unit, for example, analyzes the user's past conversation history and selects the optimal collection method. The collection unit, for example, preferentially collects topics that the user has frequently talked about in the past. The collection unit can also exclude topics that the user has avoided in the past from collection. Furthermore, the collection unit can analyze the user's past conversation patterns and select the most effective collection method. In this way, the optimal collection method can be selected by analyzing the user's past conversation history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past conversation history into AI and have the AI ​​select the optimal collection method.

[0067] The collection unit can perform filtering based on the user's current interests or activity status at the time of collection. The collection unit, for example, performs filtering based on the user's current interests and activity status at the time of collection. The collection unit, for example, preferentially collects topics in which the user is currently interested. The collection unit can also collect data related to the activity currently being performed by the user. Furthermore, the collection unit can filter unnecessary data based on the user's current interests. This makes it possible to eliminate unnecessary data by filtering based on the user's current interests and activity status. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current interests and activity status to AI and have the AI ​​perform filtering.

[0068] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the collection unit preferentially collects data for relaxation. In addition, when the user is enjoying himself, the collection unit can also preferentially collect data that is more enjoyable. Furthermore, when the user is tired, the collection unit can also preferentially collect data for refreshing. This enables more appropriate data collection by determining the priority of data to be collected based on the user's emotion. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to determine the priority of data to be collected.

[0069] The collection unit can preferentially collect highly relevant data based on the geographical location information of the user at the time of collection. For example, the collection unit preferentially collects highly relevant data taking into account the geographical location information of the user at the time of collection. For example, the collection unit preferentially collects topics related to the location where the user is currently located. The collection unit can also collect area-specific data based on the geographical location information of the user. Furthermore, when the user is traveling, the collection unit can also preferentially collect data related to the travel destination. In this way, by taking into account the geographical location information of the user, highly relevant data can be preferentially collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and have the AI ​​select highly relevant data.

[0070] The collection unit can analyze the user's social media activity at the time of collection and collect related data. The collection unit, for example, analyzes the user's social media activity at the time of collection and collects related data. The collection unit, for example, collects topics that the user frequently mentions on social media. The collection unit can also collect topics that the user's social media friends are interested in. Furthermore, the collection unit can identify current interests from the user's social media activity and collect related data. In this way, related data can be collected by analyzing the user's social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI and have the AI ​​collect related data.

[0071] The prediction unit can estimate the user's emotion and adjust the accuracy of prediction based on the estimated user's emotion. The prediction unit, for example, estimates the user's emotion and adjusts the accuracy of prediction based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the prediction unit makes a detailed prediction when the user is relaxed. In addition, the prediction unit can also make a quick prediction when the user is in a hurry. Furthermore, the prediction unit can make a prediction that takes the emotion into consideration when the user is excited. This makes it possible to make a more appropriate prediction by adjusting the accuracy of prediction based on the user's emotion. 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 input the user's emotion data to the generation AI and cause the generation AI to adjust the accuracy of the prediction.

[0072] The prediction unit can predict the next conversation content by referring to the past conversation pattern at the time of prediction. The prediction unit, for example, predicts the next conversation content by referring to the past conversation pattern at the time of prediction. The prediction unit predicts the next conversation content based on, for example, phrases that the user has frequently used in the past. The prediction unit can also analyze the user's past conversation patterns and predict the next content to be discussed. Furthermore, the prediction unit can also make predictions taking into account topics that the user has avoided in the past. This makes it possible to more accurately predict the next conversation content by referring to the past conversation pattern. 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 input the past conversation pattern into the AI ​​and have the AI ​​predict the next conversation content.

[0073] The prediction unit can appropriately adjust the prediction algorithm based on the user's current situation and environment at the time of prediction. The prediction unit, for example, optimizes the prediction algorithm based on the user's current situation and environment at the time of prediction. The prediction unit, for example, predicts the next conversation content based on the user's current location. The prediction unit can also make predictions taking into account the user's current activity situation. Furthermore, the prediction unit can adjust the prediction algorithm based on the user's current emotional state. This enables more appropriate predictions by optimizing the prediction algorithm based on the user's current situation and environment. 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 input the user's current situation and environment into AI and have the AI ​​adjust the prediction algorithm.

[0074] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated user's emotion. The prediction unit, for example, estimates the user's emotion and adjusts the display method of the prediction result based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is nervous, the prediction unit provides a display method that is simple and highly visible. In addition, when the user is relaxed, the prediction unit can also provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the prediction unit can also provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the prediction result based on the user's emotion. 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 input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the prediction result.

[0075] The prediction unit can determine the priority of prediction by referring to the user's past behavior history at the time of prediction. The prediction unit, for example, determines the priority of prediction by referring to the user's past behavior history at the time of prediction. The prediction unit determines the priority of prediction based on, for example, behavior frequently performed by the user in the past. The prediction unit can also predict important behavior preferentially from the user's past behavior history. Furthermore, the prediction unit can analyze the user's past behavior pattern and make the most appropriate prediction. As a result, the priority of prediction can be determined by referring to the user's past behavior 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 input the user's past behavior history into AI and have the AI ​​determine the priority of prediction.

[0076] The prediction unit can improve the accuracy of prediction by referring to the user's related literature and materials at the time of prediction. The prediction unit can improve the accuracy of prediction by referring to the user's related literature and materials at the time of prediction, for example. The prediction unit can improve the accuracy of prediction based on the literature the user has referred to in the past, for example. The prediction unit can also analyze the user's related materials and predict the next conversation content. Furthermore, the prediction unit can also improve the accuracy of prediction based on the materials the user has used in the past. Thereby, the accuracy of prediction is improved by referring to the user's related literature and materials. 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 input the user's related literature and materials into AI to improve the accuracy of prediction in AI.

[0077] The presentation unit can estimate the user's emotion and adjust the expression method of the content to be presented based on the estimated user's emotion. The presentation unit, for example, estimates the user's emotion and adjusts the expression method of the content to be presented based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the presentation unit presents the content in a calm expression method when the user is nervous. In addition, the presentation unit can also present the content in a bright expression method when the user is relaxed. Furthermore, the presentation unit can also present the content in a concise and quick expression method when the user is in a hurry. This makes it possible to present the content more appropriately by adjusting the expression method of the content to be presented based on the user's emotion. Part or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the content to be presented.

[0078] The presentation unit can select the optimal presentation method by referring to the user's past selection history at the time of presentation. For example, the presentation unit selects the optimal presentation method by referring to the user's past selection history at the time of presentation. The presentation unit selects the optimal presentation method based on, for example, the content selected by the user in the past. The presentation unit can also suggest an effective presentation method from the user's past selection history. Furthermore, the presentation unit can also select the optimal presentation method by taking into account the presentation method that the user avoided in the past. In this way, the optimal presentation method can be selected by referring to the user's past selection history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the user's past selection history into AI and have the AI ​​select the optimal presentation method.

[0079] The presentation unit can appropriately adjust the presentation content based on the user's current situation and environment at the time of presentation. For example, the presentation unit customizes the presentation content based on the user's current situation and environment at the time of presentation. The presentation unit presents appropriate content, for example, based on the user's current location. The presentation unit can also present optimal content in consideration of the user's current activity status. Furthermore, the presentation unit can present customized content based on the user's current emotional state. This makes it possible to present more appropriate content by customizing the presentation content based on the user's current situation and environment. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the user's current situation and environment to the AI ​​and cause the AI ​​to adjust the presentation content.

[0080] The presentation unit can estimate the user's emotion and determine the priority of the content to be presented based on the estimated user's emotion. The presentation unit, for example, estimates the user's emotion and determines the priority of the content to be presented based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the presentation unit preferentially presents relaxing content. In addition, when the user is enjoying himself, the presentation unit can also preferentially present more enjoyable content. Furthermore, when the user is tired, the presentation unit can also preferentially present refreshing content. This enables more appropriate content presentation by determining the priority of the content to be presented based on the user's emotion. Part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of the content to be presented.

[0081] The presentation unit can select the optimal display method in consideration of the device information of the user at the time of presentation. For example, the presentation unit selects the optimal display method in consideration of the device information of the user at the time of presentation. For example, when the user is using a smartphone, the presentation unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the presentation unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smart watch, the presentation unit can also provide a simple and highly visible display method. This makes it possible to select the optimal display method by considering the device information of the user. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit inputs the device information of the user into AI and allows AI to select the optimal display method.

[0082] The presentation unit can analyze the user's social media activity and present related content at the time of presentation. For example, the presentation unit analyzes the user's social media activity and presents related content at the time of presentation. The presentation unit, for example, presents topics that the user frequently mentions on social media. The presentation unit can also present topics that the user's social media friends are interested in. Furthermore, the presentation unit can identify current interests from the user's social media activity and present related content. In this way, related content can be presented by analyzing the user's social media activity. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the user's social media activity into AI and have the AI ​​present related content.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The communication support system may further include a translation unit. The translation unit may translate the conversation content selected by the user into another language and display it through the presentation unit. For example, if the user wants to have a conversation in English, the translation unit may translate the conversation content in Japanese into English and display it on the presentation unit. The translation unit may also function as support for the user when learning a foreign language. Furthermore, the translation unit may perform translation in real time to facilitate communication between users who speak different languages. This enables communication across language barriers.

[0085] The collection unit can monitor the user's health condition and adjust the type of data to be collected. For example, if the user is tired, topics that help the user relax are preferentially collected. Also, if the user is stressed, topics that help reduce stress can be collected. Furthermore, if the user is in good health, new topics that interest the user can be collected. This allows for more appropriate data collection by adjusting the type of data to be collected based on the user's health condition.

[0086] The prediction unit can predict the next conversation content by referring to the user's past purchase history. For example, it predicts topics related to products the user has purchased in the past. It can also predict the next conversation content based on the product categories the user frequently purchases. It can also predict the next topic to talk about based on reviews and ratings of products the user has purchased in the past. In this way, it is possible to more accurately predict the next conversation content by referring to the user's purchase history.

[0087] The presentation unit can present the conversation content by voice, taking into consideration the visual impairment of the user. For example, the presentation unit reads out the conversation content selected by the user by voice. The presentation unit can also provide an interface that allows the user to make selections by voice. Furthermore, the presentation unit can present the conversation content in sign language or subtitles, taking into consideration the hearing impairment of the user. This allows even users with visual or hearing impairments to smoothly proceed with the conversation.

[0088] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, when the user is excited, data is collected frequently to track changes in emotions in real time. Also, when the user is relaxed, the frequency of data collection can be reduced to reduce the burden on the user. Furthermore, when the user is stressed, data that is useful for reducing stress can be preferentially collected. This allows more appropriate data collection by adjusting the frequency of data collection based on the user's emotions.

[0089] The collection unit can customize the type of data to be collected based on the user's hobbies and preferences. For example, if the user is interested in music, topics related to music are preferentially collected. If the user is interested in sports, data related to sports can also be collected. Furthermore, if the user is interested in cooking, topics related to cooking can also be collected. This allows for more appropriate data collection by customizing the type of data to be collected based on the user's hobbies and preferences.

[0090] The collection unit can estimate the user's emotions and adjust the filtering criteria for the data to be collected based on the estimated user's emotions. For example, if the user is sad, positive topics can be preferentially collected. If the user is angry, topics to help the user regain their composure can be collected. Furthermore, if the user is having fun, topics that are even more enjoyable can be collected. This allows for more appropriate data collection by adjusting the filtering criteria for the data to be collected based on the user's emotions.

[0091] The prediction unit can predict the next conversation content by referring to the user's learning history. For example, the next conversation content is predicted based on the topics the user has learned in the past. The prediction unit can also predict the next content to be discussed taking into account the topic the user is currently learning. Furthermore, the prediction unit can predict related topics as a review of the content the user has learned in the past. In this way, the next conversation content can be predicted more accurately by referring to the user's learning history.

[0092] The presentation unit can estimate the user's emotion and adjust the tone of the content to be presented based on the estimated user's emotion. For example, if the user is nervous, the content can be presented in a calm tone. If the user is relaxed, the content can be presented in a bright tone. Furthermore, if the user is in a hurry, the content can be presented in a concise and quick tone. This makes it possible to present more appropriate content by adjusting the tone of the content to be presented based on the user's emotion.

[0093] The presentation unit can adjust the format of the content to be presented by referring to the user's past feedback. For example, the content is presented in a format that the user has previously preferred. The content can also be presented in a different format, taking into account formats that the user has previously avoided. Furthermore, the content can also be presented in an optimal format based on feedback provided by the user in the past. In this way, the format of the content to be presented can be adjusted by referring to the user's past feedback.

[0094] The process flow of the second embodiment will be briefly described below.

[0095] Step 1: The collection unit collects the user's knowledge, language, and conversational elements. For example, the user's past conversation history, topics of interest, search history, etc. can be collected. The user's knowledge includes specialized knowledge and general knowledge. Step 2: The prediction unit predicts the next conversation content based on the data collected by the collection unit. For example, based on the data that the AI ​​has learned (text data, voice data, image data, etc.), it learns past conversation patterns and predicts what should be said next. Step 3: The presentation unit presents the conversation content predicted by the prediction unit to the user and accepts a selection. For example, the presentation unit provides an interface (such as button size, color, layout, etc.) that makes it easy for the user to make selections.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements including the collection unit, prediction unit, and presentation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects elements that are the source of knowledge and conversation of the user using the camera 42 and microphone 38B of the smart device 14, and transmits them to the data processing device 12 by the control unit 46A. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the next conversation content based on the collected data. The presentation unit presents the predicted conversation content to the user using, for example, the display 40A and speaker 40B of the smart device 14, and accepts the selection. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements including the collection unit, prediction unit, and presentation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects elements that are the source of the user's knowledge and conversation using the camera 42 and microphone 238 of the smart glasses 214, and transmits them to the data processing device 12 by the control unit 46A. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the next conversation content based on the collected data. The presentation unit presents the predicted conversation content to the user using, for example, the display and speaker 240 of the smart glasses 214, and accepts the selection. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements including the collection unit, prediction unit, and presentation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects elements that are the source of knowledge and conversation of the user using the camera 42 and microphone 238 of the headset type terminal 314, and transmits them to the data processing device 12 by the control unit 46A. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the next conversation content based on the collected data. The presentation unit presents the predicted conversation content to the user using, for example, the display 343 and speaker 240 of the headset type terminal 314, and accepts the selection. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

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

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

[0143] In the 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.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0147] The data processing system 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.

[0148] Each of the multiple elements including the collection unit, prediction unit, and presentation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects elements that are the source of knowledge and conversation of the user using the camera 42 and microphone 238 of the robot 414, and transmits them to the data processing device 12 by the control unit 46A. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the next conversation content based on the collected data. The presentation unit presents the predicted conversation content to the user using, for example, the display and speaker 240 of the robot 414, and accepts the selection. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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 in which a processor is used 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Appendix 1) A collection unit that collects the user's knowledge, language, and conversational elements; A prediction unit that predicts the content of a next conversation based on the data collected by the collection unit; A presentation unit that presents the conversation content predicted by the prediction unit to a user and accepts a selection. A system characterized by: (Appendix 2) The collecting unit includes: Collect past conversation history or topics of interest 2. The system of claim 1, (Appendix 3) The prediction unit is AI predicts the next conversation content based on learned data 2. The system of claim 1, (Appendix 4) The presentation unit is Provide an interface that makes it easy for users to make selections 2. The system of claim 1, (Appendix 5) The collecting unit includes: Inferring user sentiment and adjusting the type of data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 6) The collecting unit includes: Analyze the user's past conversation history and select the appropriate collection method 2. The system of claim 1, (Appendix 7) The collecting unit includes: At the time of collection, filtering based on the user's current interests or activity 2. The system of claim 1, (Appendix 8) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 9) The collecting unit includes: At the time of collection, prioritize collection of relevant data based on the user's geographic location 2. The system of claim 1, (Appendix 10) The collecting unit includes: At the time of collection, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 11) The prediction unit is Infer user sentiment and adjust prediction accuracy based on the inferred user sentiment 2. The system of claim 1, (Appendix 12) The prediction unit is When making predictions, past conversation patterns are referenced to predict the next conversation content. 2. The system of claim 1, (Appendix 13) The prediction unit is At the time of prediction, the prediction algorithm is adjusted appropriately based on the user's current situation and environment. 2. The system of claim 1, (Appendix 14) The prediction unit is Infer user emotions and adjust how prediction results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The prediction unit is When making predictions, the system determines the priority of predictions by referring to the user's past behavior history. 2. The system of claim 1, (Appendix 16) The prediction unit is When making predictions, the accuracy of the predictions is improved by referring to the user's related literature and materials. 2. The system of claim 1, (Appendix 17) The presentation unit is Estimate the user's emotions and adjust the way the content is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 18) The presentation unit is When presenting, the optimal presentation method is selected by referring to the user's past selection history. 2. The system of claim 1, (Appendix 19) The presentation unit is At presentation time, adapt the presentation appropriately based on the user's current situation and environment 2. The system of claim 1, (Appendix 20) The presentation unit is Estimate the user's emotions and prioritize the content to be presented based on the estimated user's emotions. 2. The system of claim 1, (Appendix 21) The presentation unit is When presenting, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 22) The presentation unit is At the time of presentation, the system analyzes the user's social media activity to present relevant content. 2. The system of claim 1, [Explanation of symbols]

[0168] 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 collection unit that collects the user's knowledge, language, and conversational elements; A prediction unit that predicts the content of a next conversation based on the data collected by the collection unit; A presentation unit that presents the conversation content predicted by the prediction unit to a user and accepts a selection. A system characterized by:

2. The collecting unit includes: Collect past conversation history or topics of interest 2. The system of claim 1 .

3. The prediction unit is Predict the next conversation content based on the data that AI has learned 2. The system of claim 1 .

4. The presentation unit is Provide an interface that makes it easy for users to make selections 2. The system of claim 1 .

5. The collecting unit includes: Inferring user sentiment and adjusting the type of data collected based on the inferred user sentiment 2. The system of claim 1 .

6. The collecting unit includes: Analyze the user's past conversation history and select the appropriate collection method 2. The system of claim 1 .

7. The collecting unit includes: At the time of collection, filtering based on the user's current interests or activity 2. The system of claim 1 .

8. The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1 .

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