system

Abstract

The system according to the embodiment aims to provide predictive conversion and emoticons according to the user's situation and feelings. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a predictive conversion unit, and an emoji recommendation unit. The reception unit receives input content from a user. The analysis unit analyzes the input content received by the reception unit and analyzes the user's situation or mood. The predictive conversion unit displays appropriate predictive conversion candidates based on the situation or mood analyzed by the analysis unit. The emoji recommendation unit recommends emojis based on the situation or mood analyzed by the analysis unit.

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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide predictive conversion or emoji recommendations that are tailored to the user's situation and mood, and there is room for improvement.

[0005] The system according to the embodiment aims to provide predictive conversion and emoticons according to the user's situation and feelings. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a predictive conversion unit, and an emoji recommendation unit. The reception unit receives input content from a user. The analysis unit analyzes the input content received by the reception unit and analyzes the user's situation or mood. The predictive conversion unit displays appropriate predictive conversion candidates based on the situation or mood analyzed by the analysis unit. The emoji recommendation unit recommends emojis based on the situation or mood analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide predictive conversion and emoticons according to the user's situation and feelings. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. 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 process 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A keyboard app according to an embodiment of the present invention is a system that provides a predictive text function when a user inputs text. This keyboard app receives the user's input and, by understanding the user's current situation and feelings, recommends optimal predictive text candidates and emojis. For example, if a user inputs "thank you," the keyboard app displays predictive text candidates such as "you're welcome" and "I'm grateful," and also recommends emojis that express gratitude. This allows users to communicate more smoothly. First, when a user inputs text, the keyboard app inputs text. Next, the keyboard app receives the input text and incorporates an algorithm that uses natural language processing (NLP) technology to estimate the user's situation and feelings. Regarding data protection, the system anonymizes the input content and immediately deletes the data after analysis. Furthermore, to improve the accuracy of predictive text and emoji recommendations, a function that learns the user's past input history is added. By adding the ability for users to explicitly input specific situations or feelings, more accurate predictions and emoji recommendations will become possible. This will enable keyboard apps to analyze the user's input and provide the most appropriate predictions and emojis based on the situation or feeling, supporting smoother communication.

[0029] A keyboard app according to an embodiment includes a reception unit, an analysis unit, a predictive conversion unit, and an emoji recommendation unit. The reception unit receives user input. Examples of user input include, but are not limited to, text messages, emails, and social media posts. The reception unit receives characters entered by the user using a smartphone keyboard in real time. The reception unit can also receive voice input. For example, when a user inputs a message by voice, the voice input is converted into text using voice recognition technology. The analysis unit analyzes the input received by the reception unit and infers the user's situation and feelings. The analysis unit analyzes the context of the input using, for example, natural language processing (NLP) technology and infers the user's intentions and feelings. The analysis unit can also use a machine learning algorithm to learn the user's past input history and perform more accurate analysis. The predictive conversion unit displays appropriate predictive conversion candidates based on the situation and feelings inferred by the analysis unit. For example, the predictive conversion unit presents candidates that are most relevant to the character string entered by the user. The predictive conversion unit can also refer to the user's past input history and preferentially display frequently used words and phrases. The emoji recommendation unit recommends appropriate emojis based on the situation and feelings estimated by the analysis unit. For example, if the user inputs "thank you," the emoji recommendation unit recommends an emoji that expresses gratitude. The emoji recommendation unit can also refer to the user's past emoji usage history and preferentially display frequently used emojis. As a result, the keyboard app according to the embodiment can analyze the user's input content and provide optimal predictive conversion candidates and emojis based on the situation and feelings, thereby supporting smooth communication.

[0030] The keyboard app includes a learning unit that learns the user's past input history. The learning unit learns the user's past input history. Past input history includes, but is not limited to, previously entered text messages, emails, and social media posts. The learning unit studies, for example, input history from the past year to learn frequently used words and phrases. The learning unit can also analyze the user's input patterns and learn input content related to specific situations or feelings. For example, if a user frequently uses certain words during a specific time period, the learning unit learns that pattern and reflects it in predictive text conversion and emoji recommendations. By learning the past input history, the accuracy of predictive text conversion and emoji recommendations is improved.

[0031] The keyboard app includes a data protection unit that anonymizes user input and deletes the data after analysis. The data protection unit anonymizes user input and deletes the data after analysis. Examples of anonymization include, but are not limited to, deleting personal information and masking data. The data protection unit anonymizes the input, for example, by deleting personal information such as the user's name and address. The data protection unit also implements a mechanism for immediately deleting data after analysis. For example, the input is automatically deleted once analysis is complete. The data protection unit can also periodically delete data stored for a certain period of time. For example, data stored every week is deleted. This protects user privacy and ensures data security.

[0032] The keyboard app includes an input assistance unit that allows the user to explicitly input a specific situation or feeling. The input assistance unit supports the user in explicitly inputting the specific situation or feeling. Examples of the specific situation or feeling include, but are not limited to, the user's current activity, location, time of day, and type of emotion (e.g., joy, sadness, anger) and its intensity. The input assistance unit provides, for example, an interface for the user to input "where they are now." The input assistance unit can also provide options for the user to input "how they are feeling now." For example, an interface is provided that allows the user to select emotions such as "joy," "sadness," and "anger." This allows the user to explicitly input the situation or feeling, thereby improving the accuracy of predictive text conversion and emoji recommendations.

[0033] The reception unit can analyze the user's past input patterns and suggest an appropriate input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method that the user will use during a specific time period. Furthermore, the reception unit can also suggest an input method that suits a specific situation based on the user's past input patterns. In this way, the optimal input method can be suggested to the user by analyzing the user's past input patterns.

[0034] The reception unit can automatically switch the input mode based on the user's current activity status when receiving input. For example, when the user is walking, the reception unit can automatically switch to voice input mode. The reception unit can also automatically switch to hands-free mode when the user is driving. Furthermore, the reception unit can return to normal text input mode when the user is stationary. This makes it possible to provide a more appropriate input method by automatically switching the input mode according to the user's activity status.

[0035] When receiving input, the reception unit can prioritize presenting highly relevant input methods in consideration of the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes presenting input methods related to that location. Furthermore, when the user is traveling, the reception unit can also prioritize presenting input methods related to travel. Furthermore, when the user is at home, the reception unit can also prioritize presenting input methods related to daily life. In this way, highly relevant input methods can be provided by taking the user's geographical location information into consideration.

[0036] The reception unit can analyze the user's social media activity when receiving input and suggest a related input method. For example, the reception unit can suggest phrases that the user frequently uses on social media as input methods. The reception unit can also suggest a related input method based on the user's social media activity. Furthermore, the reception unit can also suggest an appropriate input method based on the user's interactions with friends on social media. In this way, it is possible to suggest an input method related to the user by analyzing social media activity.

[0037] During analysis, the analysis unit can improve the analysis accuracy based on the user's past input history. The analysis unit, for example, optimizes the analysis algorithm based on the user's past input history. The analysis unit can also improve the analysis accuracy by referring to the user's past input patterns. Furthermore, the analysis unit can analyze the user's past input content and improve the accuracy of the analysis results. In this way, by referring to the past input history, the analysis accuracy is improved.

[0038] During analysis, the analysis unit can customize the analysis method based on the user's current activity status. For example, when the user is walking, the analysis unit uses a simplified analysis method. When the user is driving, the analysis unit can also use an analysis method that supports voice input. Furthermore, when the user is stationary, the analysis unit can also use a detailed analysis method. In this way, by customizing the analysis method according to the user's activity status, more appropriate analysis results can be provided.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's geographical location information. For example, if the user is in a specific location, the analysis unit uses information related to that location for the analysis. Also, if the user is traveling, the analysis unit can use information related to the user's travel for the analysis. Furthermore, if the user is at home, the analysis unit can use information related to the user's daily life for the analysis. In this way, by taking geographical location information into consideration, the analysis accuracy is improved.

[0040] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's social media activity. For example, the analysis unit uses phrases frequently used by the user on social media for analysis. The analysis unit can also improve the accuracy of the analysis based on the content of the user's activities on social media. Furthermore, the analysis unit can use an appropriate analysis method based on the user's interactions with friends on social media. In this way, the analysis accuracy can be improved by referring to social media activity.

[0041] The predictive conversion unit can improve prediction accuracy based on the user's past input history during predictive conversion. The predictive conversion unit displays predictive conversion candidates based on the user's past input history, for example. The predictive conversion unit can also improve prediction accuracy by referring to the user's past input patterns. Furthermore, the predictive conversion unit can analyze the user's past input content and improve the accuracy of predictive conversion candidates. In this way, prediction accuracy is improved by referring to the past input history.

[0042] The predictive conversion unit can customize predictive conversion candidates based on the user's current activity status during predictive conversion. For example, when the user is walking, the predictive conversion unit displays simplified predictive conversion candidates. Furthermore, when the user is driving, the predictive conversion unit can also display predictive conversion candidates corresponding to voice input. Furthermore, when the user is stationary, the predictive conversion unit can also display detailed predictive conversion candidates. In this way, by customizing the predictive conversion candidates according to the user's activity status, more appropriate candidates can be provided.

[0043] The predictive conversion unit can present predictive conversion candidates based on the user's geographical location information during predictive conversion. For example, when the user is in a specific location, the predictive conversion unit displays predictive conversion candidates related to the location. Furthermore, when the user is traveling, the predictive conversion unit can also display predictive conversion candidates related to travel. Furthermore, when the user is at home, the predictive conversion unit can also display predictive conversion candidates related to daily life. In this way, by taking geographical location information into consideration, more relevant predictive conversion candidates can be provided.

[0044] The predictive conversion unit can present predictive conversion candidates based on the user's social media activity during predictive conversion. For example, the predictive conversion unit displays phrases frequently used by the user on social media as predictive conversion candidates. The predictive conversion unit can also display relevant predictive conversion candidates based on the user's social media activities. Furthermore, the predictive conversion unit can also display appropriate predictive conversion candidates based on the user's interactions with friends on social media. This makes it possible to provide more appropriate predictive conversion candidates by referring to social media activities.

[0045] When recommending emojis, the emoji recommendation unit can improve the recommendation accuracy based on the user's past input history. The emoji recommendation unit recommends emojis, for example, based on the user's past input history. The emoji recommendation unit can also improve the recommendation accuracy by referring to the user's past input patterns. Furthermore, the emoji recommendation unit can also analyze the user's past input content and improve the accuracy of the emoji recommendation. As a result, by referring to the past input history, the recommendation accuracy is improved.

[0046] When recommending an emoji, the emoji recommendation unit can customize the recommendation based on the user's current activity status. For example, when the user is walking, the emoji recommendation unit recommends a simplified emoji. Also, when the user is driving, the emoji recommendation unit can recommend an emoji corresponding to a voice input. Furthermore, when the user is standing still, the emoji recommendation unit can recommend a detailed emoji. In this way, by customizing the recommendation according to the user's activity status, more appropriate emoji can be provided.

[0047] When recommending emojis, the emoji recommendation unit can make recommendations based on the user's geographical location information. For example, if the user is in a specific location, the emoji recommendation unit can recommend emojis related to that location. Also, if the user is traveling, the emoji recommendation unit can recommend travel-related emojis. Furthermore, if the user is at home, the emoji recommendation unit can recommend emojis related to daily life. In this way, by taking geographical location information into consideration, more relevant emojis can be provided.

[0048] When recommending emojis, the emoji recommendation unit can make recommendations based on the user's social media activity. For example, the emoji recommendation unit recommends emojis that the user frequently uses on social media. The emoji recommendation unit can also recommend related emojis based on the user's activities on social media. Furthermore, the emoji recommendation unit can also recommend appropriate emojis based on the user's interactions with friends on social media. This makes it possible to provide more appropriate emojis by referring to social media activity.

[0049] During learning, the learning unit can appropriately adjust the learning algorithm based on past learning data. The learning unit, for example, optimizes the learning algorithm based on past learning data. The learning unit can also improve learning accuracy by referring to past learning patterns. Furthermore, the learning unit can analyze past learning content and improve the accuracy of the learning algorithm. In this way, the accuracy of the learning algorithm is improved by referring to past learning data.

[0050] During learning, the learning unit can weight the learning data based on the time when the input history was submitted. For example, the learning unit weights the learning data by prioritizing recent input history. The learning unit can also weight the learning data by prioritizing history that was input intensively during a specific period. Furthermore, the learning unit can weight the learning data based on the frequency of input by the user. Thus, weighting the learning data based on the time when the input history was submitted improves the accuracy of learning.

[0051] When protecting data, the data protection unit can select an appropriate protection method based on the user's past data protection history. The data protection unit selects the optimal protection method, for example, based on the user's past data protection history. The data protection unit can also improve protection accuracy by referring to the user's past data protection patterns. Furthermore, the data protection unit can analyze the user's past data protection details and select the optimal protection method. In this way, the optimal protection method can be selected by referring to the past data protection history.

[0052] The data protection unit can select an appropriate protection method based on the user's geographical location information when protecting data. For example, if the user is in a specific location, the data protection unit selects a data protection method related to that location. If the user is traveling, the data protection unit can also select a data protection method related to travel. Furthermore, if the user is at home, the data protection unit can also select a data protection method related to daily life. This allows for more appropriate data protection by taking geographical location information into consideration.

[0053] The input assistance unit can select an appropriate assistance method based on the user's past input history when providing input assistance. The input assistance unit selects the optimal input assistance method based on the user's past input history, for example. The input assistance unit can also improve assistance accuracy by referring to the user's past input patterns. Furthermore, the input assistance unit can analyze the user's past input contents and select the optimal input assistance method. In this way, the optimal input assistance method can be selected by referring to the past input history.

[0054] The input assistance unit can select an appropriate assistance method based on the user's geographical location information when providing input assistance. For example, when the user is in a specific location, the input assistance unit selects an input assistance method related to that location. Furthermore, when the user is traveling, the input assistance unit can select an input assistance method related to travel. Furthermore, when the user is at home, the input assistance unit can select an input assistance method related to daily life. This allows for more appropriate input assistance by taking the geographical location information into consideration.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When accepting input content from a user, the acceptance unit can automatically switch the input mode taking into account the user's current activity status. For example, if the user is walking, the acceptance unit can automatically switch to voice input mode. Also, if the user is driving, the acceptance unit can automatically switch to hands-free mode. Furthermore, if the user is stationary, the acceptance unit can return to normal text input mode. In this way, by automatically switching the input mode according to the user's activity status, a more appropriate input method can be provided.

[0057] When learning the user's past input history, the learning unit can select learning data taking into account the user's geographical location information. For example, if the user is in a specific location, input history related to that location can be learned preferentially. Also, if the user is traveling, input history related to travel can be learned preferentially. Furthermore, if the user is at home, input history related to daily life can be learned preferentially. In this way, by taking geographical location information into account, more relevant learning data can be selected.

[0058] When assisting a user in explicitly inputting a specific situation or feeling, the input assistance unit can analyze the user's social media activity and suggest a related input assistance method. For example, phrases frequently used by the user on social media can be suggested as input assistance methods. Also, related input assistance methods can be suggested based on the user's social media activities. Furthermore, appropriate input assistance methods can be suggested based on the user's interactions with friends on social media. Thus, by analyzing social media activity, it is possible to suggest input assistance methods relevant to the user.

[0059] The reception unit can analyze the user's past input patterns and suggest an appropriate input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also predict and suggest an input method that the user will use during a specific time period. Furthermore, it can suggest an input method that suits a specific situation based on the user's past input patterns. In this way, it is possible to suggest the optimal input method for the user by analyzing the user's past input patterns.

[0060] The reception unit can automatically switch the input mode based on the user's current activity status when receiving input. For example, if the user is walking, the reception unit can automatically switch to voice input mode. If the user is driving, the reception unit can automatically switch to hands-free mode. Furthermore, if the user is stationary, the reception unit can return to normal text input mode. This makes it possible to provide a more appropriate input method by automatically switching the input mode according to the user's activity status.

[0061] When receiving input, the reception unit can prioritize presenting highly relevant input methods by taking into account the user's geographical location information. For example, if the user is in a specific location, input methods related to that location can be prioritized. Also, if the user is traveling, input methods related to travel can be prioritized. Furthermore, if the user is at home, input methods related to daily life can be prioritized. In this way, highly relevant input methods can be provided by taking into account the user's geographical location information.

[0062] When receiving input, the receiving unit can analyze the user's social media activity and suggest a related input method. For example, it can suggest phrases that the user frequently uses on social media as input methods. It can also suggest a related input method based on the user's social media activity. It can also suggest an appropriate input method based on the user's interactions with friends on social media. In this way, it is possible to suggest an input method related to the user by analyzing social media activity.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives user input. User input includes, for example, text messages, emails, and social media posts. The reception unit receives characters entered by the user using the smartphone keyboard in real time. It can also receive voice input, which is converted into text using voice recognition technology. Step 2: The analysis unit analyzes the input content received by the reception unit and infers the user's situation and feelings. The analysis unit uses natural language processing (NLP) technology to analyze the context of the input content and infer the user's intentions and emotions. It also uses machine learning algorithms to learn the user's past input history and perform more accurate analysis. Step 3: The predictive conversion unit displays appropriate predictive conversion candidates based on the situation and feelings estimated by the analysis unit. The predictive conversion unit presents the most relevant words and phrases to the string of characters entered by the user. It can also refer to the user's past input history to prioritize the display of frequently used words and phrases. Step 4: The emoji recommendation unit recommends appropriate emojis based on the situation and feelings estimated by the analysis unit. For example, if a user types "thank you," the system will recommend emojis that express gratitude. It can also refer to the user's emoji usage history and prioritize frequently used emojis.

[0065] (Example 2) A keyboard app according to an embodiment of the present invention is a system that provides a predictive text function when a user inputs text. This keyboard app receives the user's input and, by understanding the user's current situation and feelings, recommends optimal predictive text candidates and emojis. For example, if a user inputs "thank you," the keyboard app displays predictive text candidates such as "you're welcome" and "I'm grateful," and also recommends emojis that express gratitude. This allows users to communicate more smoothly. First, when a user inputs text, the keyboard app inputs text. Next, the keyboard app receives the input text and incorporates an algorithm that uses natural language processing (NLP) technology to estimate the user's situation and feelings. Regarding data protection, the system anonymizes the input content and immediately deletes the data after analysis. Furthermore, to improve the accuracy of predictive text and emoji recommendations, a function that learns the user's past input history is added. By adding the ability for users to explicitly input specific situations or feelings, more accurate predictions and emoji recommendations will become possible. This will enable keyboard apps to analyze the user's input and provide the most appropriate predictions and emojis based on the situation or feeling, supporting smoother communication.

[0066] A keyboard app according to an embodiment includes a reception unit, an analysis unit, a predictive conversion unit, and an emoji recommendation unit. The reception unit receives user input. Examples of user input include, but are not limited to, text messages, emails, and social media posts. The reception unit receives characters entered by the user using a smartphone keyboard in real time. The reception unit can also receive voice input. For example, when a user inputs a message by voice, the voice input is converted into text using voice recognition technology. The analysis unit analyzes the input received by the reception unit and infers the user's situation and feelings. The analysis unit analyzes the context of the input using, for example, natural language processing (NLP) technology and infers the user's intentions and feelings. The analysis unit can also use a machine learning algorithm to learn the user's past input history and perform more accurate analysis. The predictive conversion unit displays appropriate predictive conversion candidates based on the situation and feelings inferred by the analysis unit. For example, the predictive conversion unit presents candidates that are most relevant to the character string entered by the user. The predictive conversion unit can also refer to the user's past input history and preferentially display frequently used words and phrases. The emoji recommendation unit recommends appropriate emojis based on the situation and feelings estimated by the analysis unit. For example, if the user inputs "thank you," the emoji recommendation unit recommends an emoji that expresses gratitude. The emoji recommendation unit can also refer to the user's past emoji usage history and preferentially display frequently used emojis. As a result, the keyboard app according to the embodiment can analyze the user's input content and provide optimal predictive conversion candidates and emojis based on the situation and feelings, thereby supporting smooth communication.

[0067] The keyboard app includes a learning unit that learns the user's past input history. The learning unit learns the user's past input history. Past input history includes, but is not limited to, previously entered text messages, emails, and social media posts. The learning unit studies, for example, input history from the past year to learn frequently used words and phrases. The learning unit can also analyze the user's input patterns and learn input content related to specific situations or feelings. For example, if a user frequently uses certain words during a specific time period, the learning unit learns that pattern and reflects it in predictive text conversion and emoji recommendations. By learning the past input history, the accuracy of predictive text conversion and emoji recommendations is improved.

[0068] The keyboard app includes a data protection unit that anonymizes user input and deletes the data after analysis. The data protection unit anonymizes user input and deletes the data after analysis. Examples of anonymization include, but are not limited to, deleting personal information and masking data. The data protection unit anonymizes the input, for example, by deleting personal information such as the user's name and address. The data protection unit also implements a mechanism for immediately deleting data after analysis. For example, the input is automatically deleted once analysis is complete. The data protection unit can also periodically delete data stored for a certain period of time. For example, data stored every week is deleted. This protects user privacy and ensures data security.

[0069] The keyboard app includes an input assistance unit that allows the user to explicitly input a specific situation or feeling. The input assistance unit supports the user in explicitly inputting the specific situation or feeling. Examples of the specific situation or feeling include, but are not limited to, the user's current activity, location, time of day, and type of emotion (e.g., joy, sadness, anger) and its intensity. The input assistance unit provides, for example, an interface for the user to input "where they are now." The input assistance unit can also provide options for the user to input "how they are feeling now." For example, an interface is provided that allows the user to select emotions such as "joy," "sadness," and "anger." This allows the user to explicitly input the situation or feeling, thereby improving the accuracy of predictive text conversion and emoji recommendations.

[0070] The reception unit can analyze the user's emotions and dynamically change the input reception interface based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Also, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input. In this way, by changing the interface according to the user's emotions, a more comfortable input experience can be provided.

[0071] The reception unit can analyze the user's past input patterns and suggest an appropriate input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method that the user will use during a specific time period. Furthermore, the reception unit can also suggest an input method that suits a specific situation based on the user's past input patterns. In this way, the optimal input method can be suggested to the user by analyzing the user's past input patterns.

[0072] The reception unit can automatically switch the input mode based on the user's current activity status when receiving input. For example, when the user is walking, the reception unit can automatically switch to voice input mode. The reception unit can also automatically switch to hands-free mode when the user is driving. Furthermore, the reception unit can return to normal text input mode when the user is stationary. This makes it possible to provide a more appropriate input method by automatically switching the input mode according to the user's activity status.

[0073] The reception unit can analyze the user's emotions and determine the priority of input reception based on the analyzed user's emotions. For example, when the user is nervous, the reception unit can preferentially receive important inputs. Furthermore, when the user is relaxed, the reception unit can also preferentially receive inputs in the normal input order. Furthermore, when the user is in a hurry, the reception unit can also preferentially receive urgent inputs. In this way, by determining the priority of input reception according to the user's emotions, important inputs can be processed preferentially.

[0074] When receiving input, the reception unit can prioritize presenting highly relevant input methods in consideration of the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes presenting input methods related to that location. Furthermore, when the user is traveling, the reception unit can also prioritize presenting input methods related to travel. Furthermore, when the user is at home, the reception unit can also prioritize presenting input methods related to daily life. In this way, highly relevant input methods can be provided by taking the user's geographical location information into consideration.

[0075] The reception unit can analyze the user's social media activity when receiving input and suggest a related input method. For example, the reception unit can suggest phrases that the user frequently uses on social media as input methods. The reception unit can also suggest a related input method based on the user's social media activity. Furthermore, the reception unit can also suggest an appropriate input method based on the user's interactions with friends on social media. In this way, it is possible to suggest an input method related to the user by analyzing social media activity.

[0076] The analysis unit can analyze the user's emotions and dynamically adjust the analysis algorithm based on the analyzed user's emotions. For example, if the user is feeling stressed, the analysis unit can simplify the analysis algorithm and provide results quickly. Also, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. Furthermore, if the user is in a hurry, the analysis unit can speed up the analysis algorithm and provide results quickly. In this way, by adjusting the analysis algorithm according to the user's emotions, more appropriate analysis results can be provided.

[0077] During analysis, the analysis unit can improve the analysis accuracy based on the user's past input history. The analysis unit, for example, optimizes the analysis algorithm based on the user's past input history. The analysis unit can also improve the analysis accuracy by referring to the user's past input patterns. Furthermore, the analysis unit can analyze the user's past input content and improve the accuracy of the analysis results. In this way, by referring to the past input history, the analysis accuracy is improved.

[0078] During analysis, the analysis unit can customize the analysis method based on the user's current activity status. For example, when the user is walking, the analysis unit uses a simplified analysis method. When the user is driving, the analysis unit can also use an analysis method that supports voice input. Furthermore, when the user is stationary, the analysis unit can also use a detailed analysis method. In this way, by customizing the analysis method according to the user's activity status, more appropriate analysis results can be provided.

[0079] The analysis unit can analyze the user's emotions and adjust the display method of the analysis results based on the analyzed user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide more visible results.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's geographical location information. For example, if the user is in a specific location, the analysis unit uses information related to that location for the analysis. Also, if the user is traveling, the analysis unit can use information related to the user's travel for the analysis. Furthermore, if the user is at home, the analysis unit can use information related to the user's daily life for the analysis. In this way, by taking geographical location information into consideration, the analysis accuracy is improved.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's social media activity. For example, the analysis unit uses phrases frequently used by the user on social media for analysis. The analysis unit can also improve the accuracy of the analysis based on the content of the user's activities on social media. Furthermore, the analysis unit can use an appropriate analysis method based on the user's interactions with friends on social media. In this way, the analysis accuracy can be improved by referring to social media activity.

[0082] The predictive conversion unit can analyze the user's emotions and adjust the display method of predictive conversion candidates based on the analyzed user's emotions. For example, when the user is feeling stressed, the predictive conversion unit displays simple and intuitive predictive conversion candidates. Furthermore, when the user is relaxed, the predictive conversion unit can also display detailed predictive conversion candidates. Furthermore, when the user is in a hurry, the predictive conversion unit can also display predictive conversion candidates that can be quickly entered. In this way, by adjusting the display method of predictive conversion candidates according to the user's emotions, more appropriate candidates can be provided.

[0083] The predictive conversion unit can improve prediction accuracy based on the user's past input history during predictive conversion. The predictive conversion unit displays predictive conversion candidates based on the user's past input history, for example. The predictive conversion unit can also improve prediction accuracy by referring to the user's past input patterns. Furthermore, the predictive conversion unit can analyze the user's past input content and improve the accuracy of predictive conversion candidates. In this way, prediction accuracy is improved by referring to the past input history.

[0084] The predictive conversion unit can customize predictive conversion candidates based on the user's current activity status during predictive conversion. For example, when the user is walking, the predictive conversion unit displays simplified predictive conversion candidates. Furthermore, when the user is driving, the predictive conversion unit can also display predictive conversion candidates corresponding to voice input. Furthermore, when the user is stationary, the predictive conversion unit can also display detailed predictive conversion candidates. In this way, by customizing the predictive conversion candidates according to the user's activity status, more appropriate candidates can be provided.

[0085] The predictive conversion unit can analyze the user's emotions and determine the priority of predictive conversion candidates based on the analyzed user's emotions. For example, when the user is nervous, the predictive conversion unit can preferentially display important predictive conversion candidates. Furthermore, when the user is relaxed, the predictive conversion unit can also preferentially display normal predictive conversion candidates. Furthermore, when the user is in a hurry, the predictive conversion unit can also preferentially display predictive conversion candidates that can be input quickly. In this way, by determining the priority of predictive conversion candidates according to the user's emotions, important candidates can be preferentially displayed.

[0086] The predictive conversion unit can present predictive conversion candidates based on the user's geographical location information during predictive conversion. For example, when the user is in a specific location, the predictive conversion unit displays predictive conversion candidates related to the location. Furthermore, when the user is traveling, the predictive conversion unit can also display predictive conversion candidates related to travel. Furthermore, when the user is at home, the predictive conversion unit can also display predictive conversion candidates related to daily life. In this way, by taking geographical location information into consideration, more relevant predictive conversion candidates can be provided.

[0087] The predictive conversion unit can present predictive conversion candidates based on the user's social media activity during predictive conversion. For example, the predictive conversion unit displays phrases frequently used by the user on social media as predictive conversion candidates. The predictive conversion unit can also display relevant predictive conversion candidates based on the user's social media activities. Furthermore, the predictive conversion unit can also display appropriate predictive conversion candidates based on the user's interactions with friends on social media. This makes it possible to provide more appropriate predictive conversion candidates by referring to social media activities.

[0088] The emoji recommendation unit can analyze the user's emotions and adjust the emoji recommendation method based on the analyzed user's emotions. For example, if the user is feeling stressed, the emoji recommendation unit can recommend simple and intuitive emojis. Also, if the user is relaxed, the emoji recommendation unit can recommend detailed emojis. Furthermore, if the user is in a hurry, the emoji recommendation unit can recommend emojis that can be quickly selected. In this way, by adjusting the emoji recommendation method according to the user's emotions, more appropriate emojis can be provided.

[0089] When recommending emojis, the emoji recommendation unit can improve the recommendation accuracy based on the user's past input history. The emoji recommendation unit recommends emojis, for example, based on the user's past input history. The emoji recommendation unit can also improve the recommendation accuracy by referring to the user's past input patterns. Furthermore, the emoji recommendation unit can also analyze the user's past input content and improve the accuracy of the emoji recommendation. As a result, by referring to the past input history, the recommendation accuracy is improved.

[0090] When recommending an emoji, the emoji recommendation unit can customize the recommendation based on the user's current activity status. For example, when the user is walking, the emoji recommendation unit recommends a simplified emoji. Also, when the user is driving, the emoji recommendation unit can recommend an emoji corresponding to a voice input. Furthermore, when the user is standing still, the emoji recommendation unit can recommend a detailed emoji. In this way, by customizing the recommendation according to the user's activity status, more appropriate emoji can be provided.

[0091] The emoji recommendation unit can analyze the user's emotions and determine the recommendation priority of emojis based on the analyzed user's emotions. For example, when the user is nervous, the emoji recommendation unit can preferentially recommend important emojis. Furthermore, when the user is relaxed, the emoji recommendation unit can also preferentially recommend normal emojis. Furthermore, when the user is in a hurry, the emoji recommendation unit can preferentially recommend emojis that can be selected quickly. In this way, by determining the recommendation priority of emojis according to the user's emotions, important emojis can be preferentially provided.

[0092] When recommending emojis, the emoji recommendation unit can make recommendations based on the user's geographical location information. For example, if the user is in a specific location, the emoji recommendation unit can recommend emojis related to that location. Also, if the user is traveling, the emoji recommendation unit can recommend travel-related emojis. Furthermore, if the user is at home, the emoji recommendation unit can recommend emojis related to daily life. In this way, by taking geographical location information into consideration, more relevant emojis can be provided.

[0093] When recommending emojis, the emoji recommendation unit can make recommendations based on the user's social media activity. For example, the emoji recommendation unit recommends emojis that the user frequently uses on social media. The emoji recommendation unit can also recommend related emojis based on the user's activities on social media. Furthermore, the emoji recommendation unit can also recommend appropriate emojis based on the user's interactions with friends on social media. This makes it possible to provide more appropriate emojis by referring to social media activity.

[0094] The learning unit can analyze the user's emotions and select learning data based on the analyzed user's emotions. For example, if the user is feeling stressed, the learning unit selects simple data as learning data. Also, if the user is relaxed, the learning unit can select detailed data as learning data. Furthermore, if the user is in a hurry, the learning unit can select data that can be learned quickly. In this way, by selecting learning data according to the user's emotions, the accuracy of learning is improved.

[0095] During learning, the learning unit can appropriately adjust the learning algorithm based on past learning data. The learning unit, for example, optimizes the learning algorithm based on past learning data. The learning unit can also improve learning accuracy by referring to past learning patterns. Furthermore, the learning unit can analyze past learning content and improve the accuracy of the learning algorithm. In this way, the accuracy of the learning algorithm is improved by referring to past learning data.

[0096] The learning unit can analyze the user's emotions and adjust the learning frequency based on the analyzed user's emotions. For example, if the user is nervous, the learning unit can set the learning frequency low. Also, if the user is relaxed, the learning unit can set the learning frequency to a normal frequency. Furthermore, if the user is in a hurry, the learning unit can set the learning frequency high. In this way, adjusting the learning frequency according to the user's emotions enables more effective learning.

[0097] During learning, the learning unit can weight the learning data based on the time when the input history was submitted. For example, the learning unit weights the learning data by prioritizing recent input history. The learning unit can also weight the learning data by prioritizing history that was input intensively during a specific period. Furthermore, the learning unit can weight the learning data based on the frequency of input by the user. Thus, weighting the learning data based on the time when the input history was submitted improves the accuracy of learning.

[0098] The data protection unit can analyze the user's emotions and adjust the level of data protection based on the analyzed user's emotions. For example, if the user is feeling stressed, the data protection unit can set a high level of data protection. Also, if the user is relaxed, the data protection unit can set a normal level of data protection. Furthermore, if the user is in a hurry, the data protection unit can quickly protect data. In this way, by adjusting the level of data protection according to the user's emotions, more appropriate data protection is possible.

[0099] When protecting data, the data protection unit can select an appropriate protection method based on the user's past data protection history. The data protection unit selects the optimal protection method, for example, based on the user's past data protection history. The data protection unit can also improve protection accuracy by referring to the user's past data protection patterns. Furthermore, the data protection unit can analyze the user's past data protection details and select the optimal protection method. In this way, the optimal protection method can be selected by referring to the past data protection history.

[0100] The data protection unit can analyze the user's emotions and determine the priority of data protection based on the analyzed user's emotions. For example, if the user is nervous, the data protection unit can prioritize the protection of important data. Also, if the user is relaxed, the data protection unit can perform data protection in the normal order. Furthermore, if the user is in a hurry, the data protection unit can quickly protect data. In this way, by determining the priority of data protection according to the user's emotions, important data can be protected with priority.

[0101] The data protection unit can select an appropriate protection method based on the user's geographical location information when protecting data. For example, if the user is in a specific location, the data protection unit selects a data protection method related to that location. If the user is traveling, the data protection unit can also select a data protection method related to travel. Furthermore, if the user is at home, the data protection unit can also select a data protection method related to daily life. This allows for more appropriate data protection by taking geographical location information into consideration.

[0102] The input assistance unit can analyze the user's emotions and adjust the input assistance method based on the analyzed user's emotions. For example, when the user is feeling stressed, the input assistance unit can provide a simple and intuitive input assistance method. When the user is relaxed, the input assistance unit can also provide a detailed input assistance method. Furthermore, when the user is in a hurry, the input assistance unit can also provide an assistance method that allows quick input. In this way, by adjusting the input assistance method according to the user's emotions, more appropriate input assistance is possible.

[0103] The input assistance unit can select an appropriate assistance method based on the user's past input history when providing input assistance. The input assistance unit selects the optimal input assistance method based on the user's past input history, for example. The input assistance unit can also improve assistance accuracy by referring to the user's past input patterns. Furthermore, the input assistance unit can analyze the user's past input contents and select the optimal input assistance method. In this way, the optimal input assistance method can be selected by referring to the past input history.

[0104] The input assistance unit can analyze the user's emotions and determine the priority of input assistance based on the analyzed user's emotions. For example, when the user is nervous, the input assistance unit can prioritize important input assistance. Furthermore, when the user is relaxed, the input assistance unit can also perform input assistance in the normal order. Furthermore, when the user is in a hurry, the input assistance unit can also provide input assistance quickly. In this way, by determining the priority of input assistance according to the user's emotions, important input assistance can be provided preferentially.

[0105] The input assistance unit can select an appropriate assistance method based on the user's geographical location information when providing input assistance. For example, when the user is in a specific location, the input assistance unit selects an input assistance method related to that location. Furthermore, when the user is traveling, the input assistance unit can select an input assistance method related to travel. Furthermore, when the user is at home, the input assistance unit can select an input assistance method related to daily life. This allows for more appropriate input assistance by taking the geographical location information into consideration. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, predictive conversion unit, emoji recommendation unit, learning unit, data protection unit, and input assistance unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives user input content in real time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's situation and feelings using natural language processing technology. The predictive conversion unit and emoji recommendation unit are realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and display appropriate predictive conversion candidates and emojis. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's past input history. The data protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymizes the input content and deletes the data after analysis. The input assistance unit is realized by, for example, the control unit 46A of the smart device 14, and assists the user in explicitly inputting a particular situation or feeling. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, predictive conversion unit, emoji recommendation unit, learning unit, data protection unit, and input assistance unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives user input content in real time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's situation and feelings using natural language processing technology. The predictive conversion unit and emoji recommendation unit are realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and display appropriate predictive conversion candidates and emojis. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's past input history. The data protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymizes the input content and deletes the data after analysis. The input assistance unit is realized by, for example, the control unit 46A of the smart glasses 214, and supports the user in explicitly inputting a specific situation or feeling. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, predictive conversion unit, emoji recommendation unit, learning unit, data protection unit, and input assistance unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives user input content in real time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's situation and feelings using natural language processing technology. The predictive conversion unit and emoji recommendation unit are realized, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and display appropriate predictive conversion candidates and emojis. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's past input history. The data protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymizes the input content and deletes the data after analysis. The input assistance unit is realized by, for example, the control unit 46A of the headset type terminal 314, and supports the user in explicitly inputting a particular situation or feeling. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, predictive conversion unit, emoji recommendation unit, learning unit, data protection unit, and input assistance unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives user input content in real time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's situation and feelings using natural language processing technology. The predictive conversion unit and emoji recommendation unit are realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and display appropriate predictive conversion candidates and emojis. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's past input history. The data protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymizes the input content and deletes the data after analysis. The input assistance unit is realized by, for example, the control unit 46A of the robot 414, and supports the user in explicitly inputting a specific situation or feeling.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When accepting input content from a user, the acceptance unit can automatically switch the input mode taking into account the user's current activity status. For example, if the user is walking, the acceptance unit can automatically switch to voice input mode. Also, if the user is driving, the acceptance unit can automatically switch to hands-free mode. Furthermore, if the user is stationary, the acceptance unit can return to normal text input mode. In this way, by automatically switching the input mode according to the user's activity status, a more appropriate input method can be provided.

[0108] When learning the user's past input history, the learning unit can select learning data taking into account the user's geographical location information. For example, if the user is in a specific location, input history related to that location can be learned preferentially. Also, if the user is traveling, input history related to travel can be learned preferentially. Furthermore, if the user is at home, input history related to daily life can be learned preferentially. In this way, by taking geographical location information into account, more relevant learning data can be selected.

[0109] When anonymizing the user's input and deleting the data after analysis, the data protection unit can analyze the user's emotions and adjust the level of data protection based on the analyzed user's emotions. For example, if the user is feeling stressed, the data protection level can be set high. Alternatively, if the user is relaxed, the data protection level can be set to a normal level. Furthermore, if the user is in a hurry, data protection can be performed quickly. In this way, more appropriate data protection can be achieved by adjusting the data protection level according to the user's emotions.

[0110] When assisting a user in explicitly inputting a specific situation or feeling, the input assistance unit can analyze the user's social media activity and suggest a related input assistance method. For example, phrases frequently used by the user on social media can be suggested as input assistance methods. Also, related input assistance methods can be suggested based on the user's social media activities. Furthermore, appropriate input assistance methods can be suggested based on the user's interactions with friends on social media. Thus, by analyzing social media activity, it is possible to suggest input assistance methods relevant to the user.

[0111] The reception unit can analyze the user's emotions and dynamically change the input reception interface based on the analyzed user emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable quick input. In this way, by changing the interface according to the user's emotions, a more comfortable input experience can be provided.

[0112] The reception unit can analyze the user's past input patterns and suggest an appropriate input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also predict and suggest an input method that the user will use during a specific time period. Furthermore, it can suggest an input method that suits a specific situation based on the user's past input patterns. In this way, it is possible to suggest the optimal input method for the user by analyzing the user's past input patterns.

[0113] The reception unit can automatically switch the input mode based on the user's current activity status when receiving input. For example, if the user is walking, the reception unit can automatically switch to voice input mode. If the user is driving, the reception unit can automatically switch to hands-free mode. Furthermore, if the user is stationary, the reception unit can return to normal text input mode. This makes it possible to provide a more appropriate input method by automatically switching the input mode according to the user's activity status.

[0114] The reception unit can analyze the user's emotions and determine the priority of input reception based on the analyzed user's emotions. For example, if the user is nervous, important inputs can be received with priority. Also, if the user is relaxed, inputs can be received in the normal order. Furthermore, if the user is in a hurry, urgent inputs can be received with priority. In this way, by determining the priority of input reception according to the user's emotions, important inputs can be processed with priority.

[0115] When receiving input, the reception unit can prioritize presenting highly relevant input methods by taking into account the user's geographical location information. For example, if the user is in a specific location, input methods related to that location can be prioritized. Also, if the user is traveling, input methods related to travel can be prioritized. Furthermore, if the user is at home, input methods related to daily life can be prioritized. In this way, highly relevant input methods can be provided by taking into account the user's geographical location information.

[0116] When receiving input, the receiving unit can analyze the user's social media activity and suggest a related input method. For example, it can suggest phrases that the user frequently uses on social media as input methods. It can also suggest a related input method based on the user's social media activity. It can also suggest an appropriate input method based on the user's interactions with friends on social media. In this way, it is possible to suggest an input method related to the user by analyzing social media activity.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives user input. User input includes, for example, text messages, emails, and social media posts. The reception unit receives characters entered by the user using the smartphone keyboard in real time. It can also receive voice input, which is converted into text using voice recognition technology. Step 2: The analysis unit analyzes the input content received by the reception unit and infers the user's situation and feelings. The analysis unit uses natural language processing (NLP) technology to analyze the context of the input content and infer the user's intentions and emotions. It also uses machine learning algorithms to learn the user's past input history and perform more accurate analysis. Step 3: The predictive conversion unit displays appropriate predictive conversion candidates based on the situation and feelings estimated by the analysis unit. The predictive conversion unit presents the most relevant words and phrases to the string of characters entered by the user. It can also refer to the user's past input history to prioritize the display of frequently used words and phrases. Step 4: The emoji recommendation unit recommends appropriate emojis based on the situation and feelings estimated by the analysis unit. For example, if a user types "thank you," the system will recommend emojis that express gratitude. It can also refer to the user's emoji usage history and prioritize frequently used emojis.

[0119] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0120] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0121] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0150] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0152] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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, RAM 30, and 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.

[0158] 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.

[0159] 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 audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] 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 carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. 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.

[0163] 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.

[0164] 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.

[0165] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0167] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] 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 voice indicating a user input regarding 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.

[0169] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0170] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] 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.

[0180] 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 USB (Universal Serial Bus) 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.

[0181] Alternatively, 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 in response to a request from the data processing device 12.

[0182] 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.

[0183] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0185] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; an analysis unit that analyzes the input content received by the reception unit and analyzes the user's situation or feelings; a predictive conversion unit that displays appropriate predictive conversion candidates based on the situation or feeling analyzed by the analysis unit; an emoji recommendation unit that recommends emojis based on the situation or feeling analyzed by the analysis unit; A system characterized by:

2. Equipped with a learning unit that learns the user's past input history 2. The system of claim 1.

3. Equipped with a data protection unit that anonymizes user input and deletes data after analysis 2. The system of claim 1.

4. An input assistance section is provided that allows the user to explicitly input a specific situation or feeling.

2. The system of claim 1.

5. The reception unit Analyzes user emotions and dynamically changes the input reception interface based on the analyzed user emotions.

2. The system of claim 1.

6. The reception unit Analyzes the user's past input patterns and suggests appropriate input methods 2. The system of claim 1.

7. The reception unit When accepting input, automatically switch input modes based on the user's current activity 2. The system of claim 1.

8. The reception unit Analyze user emotions and determine input acceptance priority based on the analyzed user emotions.

2. The system of claim 1.

9. The reception unit When accepting input, the system takes into account the user's geographic location and prioritizes the most relevant input method.

2. The system of claim 1.

10. The reception unit When accepting input, analyze the user's social media activity and suggest relevant input methods.

2. The system of claim 1.

Images