system

Abstract

The system according to this embodiment aims to analyze an individual's data and generate a clone that reflects that person's personality and way of thinking. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a dialogue unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates clones based on the data analyzed by the analysis unit. The dialogue unit allows the clones generated by the generation unit to engage in dialogue.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to utilize personal data to generate a clone reflecting the personality and thinking of that person, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze personal data and generate a clone reflecting the personality and thinking of that person.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a dialogue unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates clones based on the data analyzed by the analysis unit. The dialogue unit allows the clones generated by the generation unit to engage in dialogue. [Effects of the Invention]

[0007] The system according to this embodiment can analyze an individual's data and generate a clone that reflects that person's personality and way of thinking. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that acquires a person's personality and thoughts from acquired training data. This system collects data by utilizing how an individual uses their smartphone and its contents (emails, social media, etc.), and generates a clone only when the person authenticates. This system aims to reduce the burden on the person with the heaviest burden and increase truly necessary family communication by creating clones of all family members. The clone can repeatedly converse with the user's thoughts and encourage growth by observing the other person's changes. Daily activities are sensed from smart speakers, cameras, etc., and interactions with children are also incorporated as training data. The target is dual-income households and family caregivers, and this will reduce the burden of childcare and caregiving and increase family communication. For example, the system collects how an individual uses their smartphone and its contents (emails, social media, etc.). For example, the system acquires personality and thoughts from the collected data. For example, the system generates a clone only when the person authenticates. For example, the system repeatedly converses with the user's thoughts and encourages growth by observing the other person's changes. For example, the system senses daily activities from smart speakers, cameras, etc. This allows the system to extract a person's personality and way of thinking from the acquired training data and generate a clone.

[0029] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a dialogue unit. The collection unit collects data. The collection unit can, for example, collect how an individual uses their smartphone and the content of their posts (emails, social media, etc.). The collection unit can, for example, collect the app usage history of a smartphone. The collection unit can, for example, collect the content of social media posts. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, obtain personality traits and thoughts from the collected data. The analysis unit can, for example, analyze the data using machine learning algorithms. The analysis unit can, for example, analyze the data using statistical analysis. The generation unit generates clones based on the data analyzed by the analysis unit. The generation unit can, for example, generate digital avatars. The generation unit can, for example, generate AI agents. The generation unit can, for example, generate clones only if the user has authenticated. The dialogue unit allows the clones generated by the generation unit to engage in dialogue. The dialogue unit can, for example, conduct voice dialogues. The dialogue unit can, for example, conduct text dialogues. The dialogue unit can, for example, repeatedly engage in dialogue based on the user's thoughts and encourage growth while observing the changes in the other party. As a result, the system according to the embodiment can collect and analyze data, generate clones, and engage in dialogue.

[0030] The data collection unit collects data. For example, the data collection unit can collect information about how individuals use their smartphones and the content of their posts (emails, social media, etc.). Specifically, it can collect smartphone app usage history, browsing history, location information, call history, message content, social media posts, media files such as photos and videos, and even device sensor data (accelerometer, gyroscope, environmental sensors, etc.). This data is extremely diverse and reflects the user's behavioral patterns, interests, and detailed information about their daily life. The data collection unit collects this data in real time and transmits it to a central database. Data collection is carried out using appropriate security protocols and encryption technologies to protect user privacy. Furthermore, the data collection unit is required to collect data only with the user's consent, and the collected data is anonymized. This allows the data collection unit to efficiently collect detailed data while protecting user privacy. In addition, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and scope of data collection. For example, if a particular app is used frequently, the data collection unit can focus on collecting data related to that app. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can extract personality and thought processes from the collected data. Specifically, it uses machine learning algorithms to analyze data and extract user behavior patterns, interests, emotional states, and thought tendencies. For example, it can use natural language processing technology to analyze user emotions and intentions from email and social media posts. It can also use statistical analysis to grasp user interest trends from app usage history and browsing history. Furthermore, by analyzing data correlations, it can identify user behavior patterns and habits and build predictive models. This allows the analysis unit to quickly and accurately analyze collected data and gain a deep understanding of user personality and thought processes. In addition, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and predictions. For example, it can predict user behavior patterns in specific time periods or situations based on past behavioral data and predict future behavior. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term trend analysis and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates clones based on data analyzed by the analysis unit. The generation unit can, for example, generate digital avatars. Specifically, it can generate digital avatars that reflect the user's personality and thoughts, and perform conversations and actions on behalf of the user. The digital avatars faithfully reproduce the user's appearance, voice, speaking style, and facial expressions, enabling natural conversations that reflect the user's personality. Furthermore, the generation unit can generate AI agents. These AI agents learn the user's behavioral patterns and thought patterns, and can perform tasks and provide information on behalf of the user. For example, they can automatically manage the user's schedule, reply to emails, and post on social media. In addition, the generation unit can only generate clones after authentication. This protects user privacy and prevents the creation of unauthorized clones. Biometric authentication technologies such as fingerprint recognition, facial recognition, and voice recognition can be used for authentication. This allows the generation unit to safely and accurately generate clones that reflect the user's personality and thoughts, improving the overall reliability and security of the system.

[0033] The dialogue unit uses clones generated by the generation unit to engage in conversations. The dialogue unit can, for example, conduct voice conversations. Specifically, the generated digital avatar or AI agent can converse on behalf of the user using voice, achieving natural conversation. By combining speech recognition and speech synthesis technologies, it can faithfully reproduce the user's voice and speaking style, enabling natural conversations that do not cause discomfort to the conversation partner. The dialogue unit can also conduct text conversations. In text conversations, it can engage in text-based dialogues, similar to a chatbot, answering user questions and providing information. Furthermore, the dialogue unit can repeatedly engage in conversations based on the user's thoughts, fostering growth by observing the changes in the other party. Specifically, by collecting user feedback through conversations and enabling the AI ​​agent to self-learn, it can acquire more advanced conversational capabilities. This allows the dialogue unit to respond flexibly to user needs and situations, improving the overall system performance. Additionally, the dialogue unit supports multiple conversation modes and combines voice and text conversations to provide the user with an optimal conversational experience. This allows the dialogue unit to provide users with quick and appropriate information and support, improving the reliability and usability of the overall system.

[0034] The system includes a sensing unit that senses daily activities from sources such as smart speakers and cameras. The sensing unit can, for example, collect voice data using a smart speaker. The sensing unit can, for example, collect image data using a camera. The sensing unit can collect more detailed data by sensing daily activities. Thus, the system can collect more detailed data by sensing daily activities.

[0035] The data collection unit can collect information about how individuals use their smartphones and the content they use. For example, the data collection unit can collect smartphone app usage history. For example, the data collection unit can collect email content. For example, the data collection unit can collect social media posts. By collecting information about how individuals use their smartphones and the content they use, the data collection unit can obtain detailed data.

[0036] The analysis unit can extract personality and thought processes from the collected data. For example, the analysis unit can analyze data using machine learning algorithms. For example, the analysis unit can analyze data using statistical analysis. For example, the analysis unit can analyze data using data mining techniques. As a result, the accuracy of the clones improves by allowing the analysis unit to extract personality and thought processes from the collected data.

[0037] The generation unit can only generate clones if the user has authenticated. The generation unit can authenticate the user using, for example, password authentication. The generation unit can authenticate the user using, for example, biometric authentication. The generation unit can authenticate the user using, for example, two-factor authentication. This allows the generation unit to protect privacy by generating clones only if the user has authenticated.

[0038] The dialogue unit can repeatedly engage in conversations based on the user's thoughts, fostering growth by observing the other party's changes. For example, the dialogue unit can learn the content of the conversation using a learning algorithm. For example, the dialogue unit can foster growth by providing feedback. For example, the dialogue unit can adjust the content of the conversation based on the user's thoughts. This allows for more natural conversations by enabling the dialogue unit to repeatedly engage in conversations based on the user's thoughts, fostering growth by observing the other party's changes.

[0039] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data from applications that the user has frequently used in the past. For example, the data collection unit can prioritize collecting data that contains important information from data collected by the user in the past. For example, the data collection unit can collect data at specific time periods based on the user's past data collection history. In this way, the data collection unit can select the optimal collection method by analyzing the user's past data collection history.

[0040] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is at work, the unit can prioritize collecting work-related data. If the user is engaged in a hobby, the unit can collect data related to that hobby. If the user is on vacation, the unit can collect data related to travel and leisure. In this way, the data collection unit can collect highly relevant data by filtering based on the user's current activities and areas of interest.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in a specific location, the data collection unit can prioritize the collection of data related to that location. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to their travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data related to their home. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user frequently posts about a particular topic, the data collection unit can collect data related to that topic. For example, if a user participates in a particular group, the data collection unit can collect data related to that group. For example, if a user uses a particular hashtag, the data collection unit can collect data related to that hashtag. In this way, the data collection unit can collect relevant data by analyzing a user's social media activity.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can perform an analysis with an appropriate level of detail on moderately important data. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can apply an image recognition algorithm to image data. For example, the analysis unit can apply a speech recognition algorithm to audio data. By applying different analysis algorithms depending on the data category, the analysis unit can perform more accurate analysis.

[0045] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can perform analysis while referring to past data. For example, the analysis unit can prioritize the analysis of data collected during a specific period. As a result, the analysis unit can perform efficient analysis by determining the priority of analysis based on the data collection period.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0047] The generation unit can analyze the user's past data to select the optimal generation method when generating clones. For example, the generation unit can analyze the user's past behavior patterns and generate clones based on them. For example, the generation unit can determine the clone's dialogue style by referring to the user's past dialogue history. For example, the generation unit can build the clone's knowledge base by considering the user's past interests. As a result, the generation unit can select the optimal clone generation method by analyzing the user's past data.

[0048] The generation unit can customize the means of cloning based on the user's current life circumstances. For example, if the user is busy, the generation unit can generate a concise and efficient clone. If the user is relaxed, the generation unit can generate a clone that provides detailed information. If the user is working on a specific project, the generation unit can generate a clone related to that project. In this way, the generation unit can generate a more appropriate clone by customizing the means of cloning based on the user's current life circumstances.

[0049] The generation unit can select the optimal generation method when generating clones, taking into account the user's geographical location information. For example, if the user is in a specific location, the generation unit can generate clones related to that location. For example, if the user is traveling, the generation unit can generate clones related to the travel destination. For example, if the user is at home, the generation unit can generate clones related to home. In this way, the generation unit can select the optimal clone generation method by taking into account the user's geographical location information.

[0050] The generation unit can analyze the user's social media activity and suggest a method for generating clones. For example, if the user frequently posts about a particular topic, the generation unit can generate clones related to that topic. For example, if the user belongs to a particular group, the generation unit can generate clones related to that group. For example, if the user uses a particular hashtag, the generation unit can generate clones related to that hashtag. In this way, the generation unit can suggest the optimal method for generating clones by analyzing the user's social media activity.

[0051] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can prioritize using dialogue styles that the user has preferred in the past. For example, the dialogue unit can provide relevant information based on the questions the user has frequently asked in the past. For example, the dialogue unit can prioritize conversations on specific topics based on the user's past dialogue history. In this way, the dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history.

[0052] The dialogue unit can customize the means of dialogue based on the user's current life situation during a conversation. For example, if the user is busy, the dialogue unit can conduct a concise and efficient conversation. For example, if the user is relaxed, the dialogue unit can conduct a conversation that provides detailed information. For example, if the user is working on a specific project, the dialogue unit can conduct a conversation related to that project. In this way, the dialogue unit can conduct more appropriate conversations by customizing the means of dialogue based on the user's current life situation.

[0053] The dialogue unit can select the optimal dialogue method by considering the user's geographical location during a conversation. For example, if the user is in a specific location, the dialogue unit can conduct conversations related to that location. For example, if the user is traveling, the dialogue unit can conduct conversations related to the travel destination. For example, if the user is at home, the dialogue unit can conduct conversations related to home. In this way, the dialogue unit can select the optimal dialogue method by considering the user's geographical location.

[0054] The dialogue unit can analyze the user's social media activity during a conversation and suggest appropriate dialogue methods. For example, if the user frequently posts about a particular topic, the dialogue unit can conduct a conversation related to that topic. For example, if the user belongs to a particular group, the dialogue unit can conduct a conversation related to that group. For example, if the user uses a particular hashtag, the dialogue unit can conduct a conversation related to that hashtag. In this way, the dialogue unit can suggest the most suitable dialogue method by analyzing the user's social media activity.

[0055] The sensing unit can select the optimal sensing method by referring to the user's past behavior history during sensing. For example, the sensing unit can select the optimal sensing method based on actions the user has frequently performed in the past. For example, the sensing unit can perform sensing at a specific time period based on the user's past behavior history. For example, the sensing unit can analyze the user's past behavior history and select the most efficient sensing method. In this way, the sensing unit can select the optimal sensing method by referring to the user's past behavior history.

[0056] The sensing unit can customize its sensing methods based on the user's current living situation during sensing. For example, if the user is busy, the sensing unit can perform concise and efficient sensing. For example, if the user is relaxed, the sensing unit can perform sensing that provides detailed information. For example, if the user is working on a specific project, the sensing unit can perform sensing related to that project. In this way, the sensing unit can perform more appropriate sensing by customizing its sensing methods based on the user's current living situation.

[0057] The sensing unit can select the optimal sensing method by considering the user's geographical location information during sensing. For example, if the user is in a specific location, the sensing unit can perform sensing related to that location. For example, if the user is traveling, the sensing unit can perform sensing related to the travel destination. For example, if the user is at home, the sensing unit can perform sensing related to home. In this way, the sensing unit can select the optimal sensing method by considering the user's geographical location information.

[0058] The sensing unit can analyze the user's social media activity during sensing and propose sensing methods. For example, if a user frequently posts about a particular topic, the sensing unit can perform sensing related to that topic. For example, if a user participates in a particular group, the sensing unit can perform sensing related to that group. For example, if a user uses a particular hashtag, the sensing unit can perform sensing related to that hashtag. In this way, the sensing unit can propose the optimal sensing method by analyzing the user's social media activity.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data from applications that the user has frequently used in the past. For example, the data collection unit can prioritize collecting data that contains important information from data collected by the user in the past. For example, the data collection unit can collect data at specific time periods based on the user's past data collection history. In this way, the data collection unit can select the optimal collection method by analyzing the user's past data collection history.

[0061] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can perform an analysis with an appropriate level of detail on moderately important data. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0062] The generation unit can analyze the user's past data to select the optimal generation method when generating clones. For example, the generation unit can analyze the user's past behavior patterns and generate clones based on them. For example, the generation unit can determine the clone's dialogue style by referring to the user's past dialogue history. For example, the generation unit can build the clone's knowledge base by considering the user's past interests. As a result, the generation unit can select the optimal clone generation method by analyzing the user's past data.

[0063] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can prioritize using dialogue styles that the user has preferred in the past. For example, the dialogue unit can provide relevant information based on the questions the user has frequently asked in the past. For example, the dialogue unit can prioritize conversations on specific topics based on the user's past dialogue history. In this way, the dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history.

[0064] The sensing unit can select the optimal sensing method by considering the user's geographical location information during sensing. For example, if the user is in a specific location, the sensing unit can perform sensing related to that location. For example, if the user is traveling, the sensing unit can perform sensing related to the travel destination. For example, if the user is at home, the sensing unit can perform sensing related to home. In this way, the sensing unit can select the optimal sensing method by considering the user's geographical location information.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection unit collects data. For example, it can collect information on how individuals use their smartphones and the content of their posts (emails, social media, etc.), their smartphone app usage history, and the content of their social media posts. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can extract personality traits and thought processes from the collected data, and analyze the data using machine learning algorithms and statistical analysis. Step 3: The generation unit generates clones based on the data analyzed by the analysis unit. For example, it can generate digital avatars or AI agents, and clones can only be generated if the user authenticates. Step 4: The dialogue unit engages in conversation with the clones generated by the generation unit. For example, it can engage in voice or text dialogue, allowing the user to repeatedly interact with the clones based on their thoughts and encourage growth by observing the changes in the other party.

[0067] (Example of form 2) The system according to an embodiment of the present invention is a system that acquires a person's personality and thoughts from acquired training data. This system collects data by utilizing how an individual uses their smartphone and its contents (emails, social media, etc.), and generates a clone only when the person authenticates. This system aims to reduce the burden on the person with the heaviest burden and increase truly necessary family communication by creating clones of all family members. The clone can repeatedly converse with the user's thoughts and encourage growth by observing the other person's changes. Daily activities are sensed from smart speakers, cameras, etc., and interactions with children are also incorporated as training data. The target is dual-income households and family caregivers, and this will reduce the burden of childcare and caregiving and increase family communication. For example, the system collects how an individual uses their smartphone and its contents (emails, social media, etc.). For example, the system acquires personality and thoughts from the collected data. For example, the system generates a clone only when the person authenticates. For example, the system repeatedly converses with the user's thoughts and encourages growth by observing the other person's changes. For example, the system senses daily activities from smart speakers, cameras, etc. This allows the system to extract a person's personality and way of thinking from the acquired training data and generate a clone.

[0068] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a dialogue unit. The collection unit collects data. The collection unit can, for example, collect how an individual uses their smartphone and the content of their posts (emails, social media, etc.). The collection unit can, for example, collect the app usage history of a smartphone. The collection unit can, for example, collect the content of social media posts. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, obtain personality traits and thoughts from the collected data. The analysis unit can, for example, analyze the data using machine learning algorithms. The analysis unit can, for example, analyze the data using statistical analysis. The generation unit generates clones based on the data analyzed by the analysis unit. The generation unit can, for example, generate digital avatars. The generation unit can, for example, generate AI agents. The generation unit can, for example, generate clones only if the user has authenticated. The dialogue unit allows the clones generated by the generation unit to engage in dialogue. The dialogue unit can, for example, conduct voice dialogues. The dialogue unit can, for example, conduct text dialogues. The dialogue unit can, for example, repeatedly engage in dialogue based on the user's thoughts and encourage growth while observing the changes in the other party. As a result, the system according to the embodiment can collect and analyze data, generate clones, and engage in dialogue.

[0069] The data collection unit collects data. For example, the data collection unit can collect information about how individuals use their smartphones and the content of their posts (emails, social media, etc.). Specifically, it can collect smartphone app usage history, browsing history, location information, call history, message content, social media posts, media files such as photos and videos, and even device sensor data (accelerometer, gyroscope, environmental sensors, etc.). This data is extremely diverse and reflects the user's behavioral patterns, interests, and detailed information about their daily life. The data collection unit collects this data in real time and transmits it to a central database. Data collection is carried out using appropriate security protocols and encryption technologies to protect user privacy. Furthermore, the data collection unit is required to collect data only with the user's consent, and the collected data is anonymized. This allows the data collection unit to efficiently collect detailed data while protecting user privacy. In addition, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and scope of data collection. For example, if a particular app is used frequently, the data collection unit can focus on collecting data related to that app. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0070] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can extract personality and thought processes from the collected data. Specifically, it uses machine learning algorithms to analyze data and extract user behavior patterns, interests, emotional states, and thought tendencies. For example, it can use natural language processing technology to analyze user emotions and intentions from email and social media posts. It can also use statistical analysis to grasp user interest trends from app usage history and browsing history. Furthermore, by analyzing data correlations, it can identify user behavior patterns and habits and build predictive models. This allows the analysis unit to quickly and accurately analyze collected data and gain a deep understanding of user personality and thought processes. In addition, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and predictions. For example, it can predict user behavior patterns in specific time periods or situations based on past behavioral data and predict future behavior. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term trend analysis and anomaly detection, thereby improving the reliability and safety of the entire system.

[0071] The generation unit generates clones based on data analyzed by the analysis unit. The generation unit can, for example, generate digital avatars. Specifically, it can generate digital avatars that reflect the user's personality and thoughts, and perform conversations and actions on behalf of the user. The digital avatars faithfully reproduce the user's appearance, voice, speaking style, and facial expressions, enabling natural conversations that reflect the user's personality. Furthermore, the generation unit can generate AI agents. These AI agents learn the user's behavioral patterns and thought patterns, and can perform tasks and provide information on behalf of the user. For example, they can automatically manage the user's schedule, reply to emails, and post on social media. In addition, the generation unit can only generate clones after authentication. This protects user privacy and prevents the creation of unauthorized clones. Biometric authentication technologies such as fingerprint recognition, facial recognition, and voice recognition can be used for authentication. This allows the generation unit to safely and accurately generate clones that reflect the user's personality and thoughts, improving the overall reliability and security of the system.

[0072] The dialogue unit uses clones generated by the generation unit to engage in conversations. The dialogue unit can, for example, conduct voice conversations. Specifically, the generated digital avatar or AI agent can converse on behalf of the user using voice, achieving natural conversation. By combining speech recognition and speech synthesis technologies, it can faithfully reproduce the user's voice and speaking style, enabling natural conversations that do not cause discomfort to the conversation partner. The dialogue unit can also conduct text conversations. In text conversations, it can engage in text-based dialogues, similar to a chatbot, answering user questions and providing information. Furthermore, the dialogue unit can repeatedly engage in conversations based on the user's thoughts, fostering growth by observing the changes in the other party. Specifically, by collecting user feedback through conversations and enabling the AI ​​agent to self-learn, it can acquire more advanced conversational capabilities. This allows the dialogue unit to respond flexibly to user needs and situations, improving the overall system performance. Additionally, the dialogue unit supports multiple conversation modes and combines voice and text conversations to provide the user with an optimal conversational experience. This allows the dialogue unit to provide users with quick and appropriate information and support, improving the reliability and usability of the overall system.

[0073] The system includes a sensing unit that senses daily activities from sources such as smart speakers and cameras. The sensing unit can, for example, collect voice data using a smart speaker. The sensing unit can, for example, collect image data using a camera. The sensing unit can collect more detailed data by sensing daily activities. Thus, the system can collect more detailed data by sensing daily activities.

[0074] The data collection unit can collect information about how individuals use their smartphones and the content they use. For example, the data collection unit can collect smartphone app usage history. For example, the data collection unit can collect email content. For example, the data collection unit can collect social media posts. By collecting information about how individuals use their smartphones and the content they use, the data collection unit can obtain detailed data.

[0075] The analysis unit can extract personality and thought processes from the collected data. For example, the analysis unit can analyze data using machine learning algorithms. For example, the analysis unit can analyze data using statistical analysis. For example, the analysis unit can analyze data using data mining techniques. As a result, the accuracy of the clones improves by allowing the analysis unit to extract personality and thought processes from the collected data.

[0076] The generation unit can only generate clones if the user has authenticated. The generation unit can authenticate the user using, for example, password authentication. The generation unit can authenticate the user using, for example, biometric authentication. The generation unit can authenticate the user using, for example, two-factor authentication. This allows the generation unit to protect privacy by generating clones only if the user has authenticated.

[0077] The dialogue unit can repeatedly engage in conversations based on the user's thoughts, fostering growth by observing the other party's changes. For example, the dialogue unit can learn the content of the conversation using a learning algorithm. For example, the dialogue unit can foster growth by providing feedback. For example, the dialogue unit can adjust the content of the conversation based on the user's thoughts. This allows for more natural conversations by enabling the dialogue unit to repeatedly engage in conversations based on the user's thoughts, fostering growth by observing the other party's changes.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection and collect data when the user is relaxed. For example, if the user is concentrating, the data collection unit can avoid collecting data at that time. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection and collect more detailed data. In this way, the data collection unit can collect data at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data from applications that the user has frequently used in the past. For example, the data collection unit can prioritize collecting data that contains important information from data collected by the user in the past. For example, the data collection unit can collect data at specific time periods based on the user's past data collection history. In this way, the data collection unit can select the optimal collection method by analyzing the user's past data collection history.

[0080] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is at work, the unit can prioritize collecting work-related data. If the user is engaged in a hobby, the unit can collect data related to that hobby. If the user is on vacation, the unit can collect data related to travel and leisure. In this way, the data collection unit can collect highly relevant data by filtering based on the user's current activities and areas of interest.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting data that helps reduce stress. For example, if the user is relaxed, the data collection unit can prioritize collecting data related to relaxation. For example, if the user is excited, the data collection unit can prioritize collecting data related to excitement. In this way, the data collection unit can prioritize collecting more important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in a specific location, the data collection unit can prioritize the collection of data related to that location. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to their travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data related to their home. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location.

[0083] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user frequently posts about a particular topic, the data collection unit can collect data related to that topic. For example, if a user participates in a particular group, the data collection unit can collect data related to that group. For example, if a user uses a particular hashtag, the data collection unit can collect data related to that hashtag. In this way, the data collection unit can collect relevant data by analyzing a user's social media activity.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can perform an analysis with an appropriate level of detail on moderately important data. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can apply an image recognition algorithm to image data. For example, the analysis unit can apply a speech recognition algorithm to audio data. By applying different analysis algorithms depending on the data category, the analysis unit can perform more accurate analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can perform analysis while referring to past data. For example, the analysis unit can prioritize the analysis of data collected during a specific period. As a result, the analysis unit can perform efficient analysis by determining the priority of analysis based on the data collection period.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0090] The generation unit can estimate the user's emotions and adjust the clone generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a clone that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit can generate a clone that responds quickly. For example, if the user is excited, the generation unit can generate a visually stimulating clone. In this way, the generation unit can generate more appropriate clones by adjusting the clone generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The generation unit can analyze the user's past data to select the optimal generation method when generating clones. For example, the generation unit can analyze the user's past behavior patterns and generate clones based on them. For example, the generation unit can determine the clone's dialogue style by referring to the user's past dialogue history. For example, the generation unit can build the clone's knowledge base by considering the user's past interests. As a result, the generation unit can select the optimal clone generation method by analyzing the user's past data.

[0092] The generation unit can customize the means of cloning based on the user's current life circumstances. For example, if the user is busy, the generation unit can generate a concise and efficient clone. If the user is relaxed, the generation unit can generate a clone that provides detailed information. If the user is working on a specific project, the generation unit can generate a clone related to that project. In this way, the generation unit can generate a more appropriate clone by customizing the means of cloning based on the user's current life circumstances.

[0093] The generation unit can estimate the user's emotions and determine the priority of clone generation based on the estimated user emotions. For example, if the user is stressed, the generation unit can prioritize generating clones that help reduce stress. For example, if the user is relaxed, the generation unit can prioritize generating clones related to relaxation. For example, if the user is excited, the generation unit can prioritize generating clones related to excitement. In this way, the generation unit can prioritize generating more important clones by determining the priority of clone generation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The generation unit can select the optimal generation method when generating clones, taking into account the user's geographical location information. For example, if the user is in a specific location, the generation unit can generate clones related to that location. For example, if the user is traveling, the generation unit can generate clones related to the travel destination. For example, if the user is at home, the generation unit can generate clones related to home. In this way, the generation unit can select the optimal clone generation method by taking into account the user's geographical location information.

[0095] The generation unit can analyze the user's social media activity and suggest a method for generating clones. For example, if the user frequently posts about a particular topic, the generation unit can generate clones related to that topic. For example, if the user belongs to a particular group, the generation unit can generate clones related to that group. For example, if the user uses a particular hashtag, the generation unit can generate clones related to that hashtag. In this way, the generation unit can suggest the optimal method for generating clones by analyzing the user's social media activity.

[0096] The dialogue unit can estimate the user's emotions and adjust the way it expresses itself based on those emotions. For example, if the user is nervous, the dialogue unit can use a calm tone. If the user is relaxed, the dialogue unit can use a bright tone. If the user is in a hurry, the dialogue unit can use a quick and concise tone. This allows the dialogue unit to provide more appropriate conversations by adjusting its expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can prioritize using dialogue styles that the user has preferred in the past. For example, the dialogue unit can provide relevant information based on the questions the user has frequently asked in the past. For example, the dialogue unit can prioritize conversations on specific topics based on the user's past dialogue history. In this way, the dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history.

[0098] The dialogue unit can customize the means of dialogue based on the user's current life situation during a conversation. For example, if the user is busy, the dialogue unit can conduct a concise and efficient conversation. For example, if the user is relaxed, the dialogue unit can conduct a conversation that provides detailed information. For example, if the user is working on a specific project, the dialogue unit can conduct a conversation related to that project. In this way, the dialogue unit can conduct more appropriate conversations by customizing the means of dialogue based on the user's current life situation.

[0099] The dialogue unit can estimate the user's emotions and prioritize conversations based on those emotions. For example, if the user is stressed, the dialogue unit can prioritize conversations that help reduce stress. For example, if the user is relaxed, the dialogue unit can prioritize conversations related to relaxation. For example, if the user is excited, the dialogue unit can prioritize conversations related to excitement. In this way, the dialogue unit can prioritize more important conversations by prioritizing them based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The dialogue unit can select the optimal dialogue method by considering the user's geographical location during a conversation. For example, if the user is in a specific location, the dialogue unit can conduct conversations related to that location. For example, if the user is traveling, the dialogue unit can conduct conversations related to the travel destination. For example, if the user is at home, the dialogue unit can conduct conversations related to home. In this way, the dialogue unit can select the optimal dialogue method by considering the user's geographical location.

[0101] The dialogue unit can analyze the user's social media activity during a conversation and suggest appropriate dialogue methods. For example, if the user frequently posts about a particular topic, the dialogue unit can conduct a conversation related to that topic. For example, if the user belongs to a particular group, the dialogue unit can conduct a conversation related to that group. For example, if the user uses a particular hashtag, the dialogue unit can conduct a conversation related to that hashtag. In this way, the dialogue unit can suggest the most suitable dialogue method by analyzing the user's social media activity.

[0102] The sensing unit can estimate the user's emotions and adjust the timing of sensing based on the estimated emotions. For example, if the user is stressed, the sensing unit can reduce the frequency of sensing and perform sensing when the user is relaxed. For example, if the user is concentrating, the sensing unit can avoid sensing at that time. For example, if the user is relaxed, the sensing unit can increase the frequency of sensing and collect more detailed data. In this way, the sensing unit can perform sensing at a more appropriate time by adjusting the timing of sensing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The sensing unit can select the optimal sensing method by referring to the user's past behavior history during sensing. For example, the sensing unit can select the optimal sensing method based on actions the user has frequently performed in the past. For example, the sensing unit can perform sensing at a specific time period based on the user's past behavior history. For example, the sensing unit can analyze the user's past behavior history and select the most efficient sensing method. In this way, the sensing unit can select the optimal sensing method by referring to the user's past behavior history.

[0104] The sensing unit can customize its sensing methods based on the user's current living situation during sensing. For example, if the user is busy, the sensing unit can perform concise and efficient sensing. For example, if the user is relaxed, the sensing unit can perform sensing that provides detailed information. For example, if the user is working on a specific project, the sensing unit can perform sensing related to that project. In this way, the sensing unit can perform more appropriate sensing by customizing its sensing methods based on the user's current living situation.

[0105] The sensing unit can estimate the user's emotions and determine sensing priorities based on the estimated emotions. For example, if the user is stressed, the sensing unit can prioritize sensing that helps reduce stress. For example, if the user is relaxed, the sensing unit can prioritize sensing related to relaxation. For example, if the user is excited, the sensing unit can prioritize sensing related to excitement. In this way, the sensing unit can prioritize more important sensings by determining sensing priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The sensing unit can select the optimal sensing method by considering the user's geographical location information during sensing. For example, if the user is in a specific location, the sensing unit can perform sensing related to that location. For example, if the user is traveling, the sensing unit can perform sensing related to the travel destination. For example, if the user is at home, the sensing unit can perform sensing related to home. In this way, the sensing unit can select the optimal sensing method by considering the user's geographical location information.

[0107] The sensing unit can analyze the user's social media activity during sensing and propose sensing methods. For example, if a user frequently posts about a particular topic, the sensing unit can perform sensing related to that topic. For example, if a user participates in a particular group, the sensing unit can perform sensing related to that group. For example, if a user uses a particular hashtag, the sensing unit can perform sensing related to that hashtag. In this way, the sensing unit can propose the optimal sensing method by analyzing the user's social media activity.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can prioritize analyzing data that helps reduce stress. For example, if the user is relaxed, the analysis unit can prioritize analyzing data related to relaxation. For example, if the user is excited, the analysis unit can prioritize analyzing data related to excitement. In this way, the analysis unit can prioritize analyzing more important data by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The data collection unit can estimate the user's emotions and determine the priority of data collection based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting data that helps reduce stress. For example, if the user is relaxed, the data collection unit can prioritize collecting data related to relaxation. For example, if the user is excited, the data collection unit can prioritize collecting data related to excitement. In this way, the data collection unit can prioritize collecting more important data by determining the priority of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The generation unit can estimate the user's emotions and adjust the clone generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a clone that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit can generate a clone that responds quickly. For example, if the user is excited, the generation unit can generate a visually stimulating clone. In this way, the generation unit can generate more appropriate clones by adjusting the clone generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The dialogue unit can estimate the user's emotions and adjust the way it expresses itself based on those emotions. For example, if the user is nervous, the dialogue unit can use a calm tone. If the user is relaxed, the dialogue unit can use a bright tone. If the user is in a hurry, the dialogue unit can use a quick and concise tone. This allows the dialogue unit to provide more appropriate conversations by adjusting its expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The sensing unit can estimate the user's emotions and adjust the timing of sensing based on the estimated emotions. For example, if the user is stressed, the sensing unit can reduce the frequency of sensing and perform sensing when the user is relaxed. For example, if the user is concentrating, the sensing unit can avoid sensing at that time. For example, if the user is relaxed, the sensing unit can increase the frequency of sensing and collect more detailed data. In this way, the sensing unit can perform sensing at a more appropriate time by adjusting the timing of sensing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data from applications that the user has frequently used in the past. For example, the data collection unit can prioritize collecting data that contains important information from data collected by the user in the past. For example, the data collection unit can collect data at specific time periods based on the user's past data collection history. In this way, the data collection unit can select the optimal collection method by analyzing the user's past data collection history.

[0115] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can perform an analysis with an appropriate level of detail on moderately important data. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0116] The generation unit can analyze the user's past data to select the optimal generation method when generating clones. For example, the generation unit can analyze the user's past behavior patterns and generate clones based on them. For example, the generation unit can determine the clone's dialogue style by referring to the user's past dialogue history. For example, the generation unit can build the clone's knowledge base by considering the user's past interests. As a result, the generation unit can select the optimal clone generation method by analyzing the user's past data.

[0117] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can prioritize using dialogue styles that the user has preferred in the past. For example, the dialogue unit can provide relevant information based on the questions the user has frequently asked in the past. For example, the dialogue unit can prioritize conversations on specific topics based on the user's past dialogue history. In this way, the dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history.

[0118] The sensing unit can select the optimal sensing method by considering the user's geographical location information during sensing. For example, if the user is in a specific location, the sensing unit can perform sensing related to that location. For example, if the user is traveling, the sensing unit can perform sensing related to the travel destination. For example, if the user is at home, the sensing unit can perform sensing related to home. In this way, the sensing unit can select the optimal sensing method by considering the user's geographical location information.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The data collection unit collects data. For example, it can collect information on how individuals use their smartphones and the content of their posts (emails, social media, etc.), their smartphone app usage history, and the content of their social media posts. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can extract personality traits and thought processes from the collected data, and analyze the data using machine learning algorithms and statistical analysis. Step 3: The generation unit generates clones based on the data analyzed by the analysis unit. For example, it can generate digital avatars or AI agents, and clones can only be generated if the user authenticates. Step 4: The dialogue unit engages in conversation with the clones generated by the generation unit. For example, it can engage in voice or text dialogue, allowing the user to repeatedly interact with the clones based on their thoughts and encourage growth by observing the changes in the other party.

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, dialogue unit, and sensing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information on how an individual uses their smartphone and the content of their posts (emails, social media, etc.). The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts personality and thoughts from the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a clone based on the analyzed data. The dialogue unit is implemented by the control unit 46A of the smart device 14 and the generated clone engages in voice or text dialogue. The sensing unit senses daily activities using the camera 42 or smart speaker of the smart device 14 and collects detailed data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 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.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, dialogue unit, and sensing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information on how an individual uses their smartphone and the content of their posts (emails, social media, etc.). The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts personality traits and thoughts from the collected data. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a clone based on the analyzed data. The dialogue unit is implemented by the control unit 46A of the smart glasses 214 and the generated clone engages in voice and text dialogue. The sensing unit senses daily activities using the camera 42 of the smart glasses 214 or a smart speaker and collects detailed data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, dialogue unit, and sensing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information on how an individual uses their smartphone and the content of their posts (emails, social media, etc.). The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts personality traits and thoughts from the collected data. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a clone based on the analyzed data. The dialogue unit is implemented by the control unit 46A of the headset terminal 314 and the generated clone engages in voice and text dialogue. The sensing unit senses daily activities using the camera 42 of the headset terminal 314 or a smart speaker and collects detailed data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the 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.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, dialogue unit, and sensing unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information on how an individual uses their smartphone and the content of their posts (emails, social media, etc.). The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts personality and thoughts from the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a clone based on the analyzed data. The dialogue unit is implemented by the control unit 46A of the robot 414 and the generated clone engages in voice and text dialogue. The sensing unit senses daily activities using the camera 42 of the robot 414 or a smart speaker and collects detailed data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0174] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates clones based on the data analyzed by the analysis unit, The system includes a dialogue unit in which the clones generated by the generation unit interact. A system characterized by the following features. (Note 2) It features a sensing unit that senses daily activities from smart speakers, cameras, and other devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collecting information about how individuals use their smartphones and what they do with their devices. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Personality and thought processes are extracted from collected data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is A clone will only be generated if the user has authenticated it. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned dialogue unit, It engages in repeated dialogue based on the user's thinking, fostering growth while observing the other party's changes. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the cloning method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During clone generation, the system analyzes the user's past data to select the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During cloning, the method of creation is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of cloning based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When creating a clone, the optimal creation method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During clone generation, the system analyzes the user's social media activity to propose generation methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, During a conversation, the system selects the optimal conversation method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, During conversations, the means of communication are customized based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dialogue unit, During the interaction, the system selects the optimal interaction method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dialogue unit, During the conversation, we analyze the user's social media activity and suggest ways to communicate. The system described in Appendix 1, characterized by the features described herein. (Note 31) The sensing unit is, It estimates the user's emotions and adjusts the timing of sensing based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The sensing unit is, During sensing, the system selects the optimal sensing method by referring to the user's past behavior history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The sensing unit is, During sensing, the sensing method is customized based on the user's current living situation. The system described in Appendix 2, characterized by the features described herein. (Note 34) The sensing unit is, The system estimates the user's emotions and prioritizes sensing based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The sensing unit is, During sensing, the optimal sensing method is selected considering the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 36) The sensing unit is, During sensing, we analyze the user's social media activity and propose sensing methods. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates clones based on the data analyzed by the analysis unit, The system includes a dialogue unit in which the clones generated by the generation unit interact. A system characterized by the following features.

2. It features a sensing unit that senses daily activities from smart speakers, cameras, and other devices. The system according to feature 1.

3. The aforementioned collection unit is Collecting information about how individuals use their smartphones and what they do with their devices. The system according to feature 1.

4. The aforementioned analysis unit, Personality and thought processes are extracted from collected data. The system according to feature 1.

5. The generating unit is A clone will only be generated if the user has authenticated it. The system according to feature 1.

6. The aforementioned dialogue unit, It engages in repeated dialogue based on the user's thinking, fostering growth while observing the other party's changes. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system according to feature 1.

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