system

Abstract

The system according to this embodiment aims to analyze the content of dreams and explain their meaning and underlying psychological state. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a diary unit. The reception unit inputs the content of the dream. The analysis unit analyzes the content of the dream input by the reception unit. The provision unit provides the results analyzed by the analysis unit. The diary unit manages the record of the dream.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, the content of dreams has not been sufficiently analyzed to explain its meaning and potential psychological state, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the content of dreams and explain its meaning and potential psychological state.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a diary unit. The reception unit inputs the content of dreams. The analysis unit analyzes the content of dreams input by the reception unit. The provision unit provides the result analyzed by the analysis unit. The diary unit manages the recording of dreams.

Effects of the Invention

[0007] The system according to this embodiment can analyze the content of dreams and explain their meaning and underlying psychological state. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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] <000009​​​​​​​​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 dream analysis assistant according to an embodiment of the present invention is a system that, upon input of a dream a user has had, analyzes its content and explains its meaning and underlying psychological state. The dream analysis assistant analyzes the content of a dream a user has had and explains its meaning and underlying psychological state. Furthermore, the dream analysis assistant provides a dream journal function, allowing the user to analyze dream patterns over the long term. For example, the dream analysis assistant receives input from the user about the content of their dream. Next, the dream analysis assistant uses AI to analyze the input content of the dream and explains its meaning and underlying psychological state. In addition, the dream analysis assistant allows the user to record their dreams daily using the dream journal function and analyze dream patterns over the long term. For example, based on past dream data, the dream analysis assistant identifies frequently appearing themes and symbols and provides feedback to the user. This mechanism allows the user to deepen their self-understanding and supports their mental health.

[0029] The dream analysis assistant according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a diary unit. The reception unit inputs the content of the dream the user has seen. The content of the dream the user has seen may include, but is not limited to, text format, audio format, or video format. The reception unit may, for example, provide a text input interface, allowing the user to input the dream content in text format. The reception unit may also provide an audio input interface, allowing the user to input the dream content in audio format. Furthermore, the reception unit may provide a video input interface, allowing the user to input the dream content in video format. For example, the reception unit may use speech recognition technology to convert audio input into text data. The reception unit may also use video analysis technology to convert video input into text data. The analysis unit uses AI to analyze the dream content input by the reception unit. The analysis may, for example, use natural language processing technology, image analysis technology, or audio analysis technology, but is not limited to these examples. For example, the analysis unit may use natural language processing technology to analyze the text data and explain the meaning of the dream and the underlying psychological state. Furthermore, the analysis unit can use image analysis technology to analyze video data and explain the meaning of dreams and underlying psychological states. The analysis unit can also use voice analysis technology to analyze audio data and explain the meaning of dreams and underlying psychological states. The provision unit provides the results analyzed by the analysis unit. Provision is provided in the form of, for example, text reports, graphs, and audio feedback, but is not limited to these examples. For example, the provision unit provides the analysis results as a text report. The provision unit can also provide the analysis results as a graph. The provision unit can also provide the analysis results as audio feedback. The diary unit records the dreams the user has each day and analyzes dream patterns over the long term. Recording is done in, for example, digital format, paper format, and cloud storage, but is not limited to these examples. For example, the diary unit records the content of dreams in digital format. The diary unit can also record the content of dreams on paper. The diary unit can also record the content of dreams using cloud storage.This allows the dream analysis assistant according to the embodiment to efficiently input, analyze, provide, and record the dreams the user has had.

[0030] The reception system inputs the content of the user's dreams. This content may include, but is not limited to, text, audio, or video formats. For example, the reception system provides a text input interface, allowing the user to input their dreams in text format. It also provides an audio input interface, enabling users to input their dreams in audio format. Furthermore, it provides a video input interface, allowing users to input their dreams in video format. For example, the reception system uses speech recognition technology to convert audio input into text data. It also uses video analysis technology to convert video input into text data. Specifically, the text input interface is designed to allow users to input dream content using a keyboard or touchscreen. The audio input interface captures the user's voice via a microphone and converts it to text using speech recognition technology. The video input interface uses a camera to capture dream scenes and related visuals drawn by the user and converts the video into text data using video analysis technology. This allows users to input their dreams in the most natural way possible, ensuring accurate recording of their content. Furthermore, the reception section is equipped with an interface for centrally managing the data entered by users and transmitting it to the analysis section. This ensures that the content of the dreams entered by users is quickly and accurately transmitted to the analysis section, allowing for smooth analysis in the next step.

[0031] The analysis unit uses AI to analyze the content of dreams entered by the reception unit. The analysis is performed using, but is not limited to, natural language processing, image analysis, and speech analysis technologies. For example, the analysis unit can use natural language processing to analyze text data and explain the meaning of the dream and the underlying psychological state. The analysis unit can also use image analysis to analyze video data and explain the meaning of the dream and the underlying psychological state. Furthermore, the analysis unit can use speech analysis to analyze audio data and explain the meaning of the dream and the underlying psychological state. Specifically, natural language processing is used to extract keywords and phrases from text data and analyze their relationships to identify the theme and underlying psychological state of the dream. Image analysis is used to recognize specific scenes and objects from video data and analyze their meaning. Speech analysis is used to analyze emotions and tone from audio data and clarify the psychological state behind the dream. In this way, the analysis unit can analyze the content of the user's dreams from multiple angles and provide a detailed explanation. Furthermore, the analysis unit can improve the accuracy of its analysis results by referencing past dream data and psychological databases. For example, by comparing current dream data with past data, it can identify specific patterns and trends, providing insights into the user's psychological state and life circumstances. The analysis unit can also incorporate user feedback and continuously improve its analysis algorithms. This allows the analysis unit to always provide users with highly accurate analysis results using the latest information and technology.

[0032] The service provider provides the results analyzed by the analysis provider. The results are provided in various forms, including, but are not limited to, text reports, graphs, and audio feedback. For example, the service provider may provide the analysis results as a text report, graphs, or audio feedback. Specifically, text reports provide detailed descriptions of the analysis results in a user-friendly format. Graphs visually represent the analysis results, allowing users to understand them at a glance. Audio feedback explains the analysis results verbally, enabling users to understand them aurally. This allows the service provider to offer analysis results in diverse formats, ensuring users receive information in the most easily understood way. Furthermore, the service provider can customize the delivery format according to user preferences. For example, if a user prefers text reports, a detailed text report is provided; if they prefer graphs, visual graphs are provided; and if they prefer audio feedback, the analysis results are explained verbally. This allows the service provider to respond flexibly to user needs and improve user satisfaction. Furthermore, the service also includes a function to save analysis results, allowing users to refer to them later. This enables users to review past analysis results and identify long-term dream patterns and trends.

[0033] The dream journal function allows users to record their daily dreams and analyze dream patterns over the long term. Recording can be done digitally, on paper, or via cloud storage, but is not limited to these methods. For example, the dream journal function can record dream content digitally, on paper, or using cloud storage. Specifically, in digital format, users input dream content into their smartphones or computers and record it using dedicated applications or software. In paper format, users handwrite dream content and save it in a dedicated notebook or diary. With cloud storage, users input dream content online and save it to the cloud, allowing access anytime, anywhere. This allows the dream journal function to record user dreams over the long term and analyze dream patterns and trends. Furthermore, the dream journal function can collaborate with the analysis function to perform long-term analysis of the recorded dream data. For example, based on past dream data, it can identify specific patterns and trends, providing insights into the user's psychological state and life circumstances. The dream journal function also includes a feature that allows users to easily search for dream content. This allows users to look back on past dreams and quickly find information about specific dreams or themes. Furthermore, the journal section also provides a function for users to share the content of their dreams with other users. This allows users to share their dreams with others and discuss common themes and patterns. In this way, the journal section can efficiently record the dreams that users have and support long-term analysis and sharing.

[0034] The dream analysis assistant includes an identification unit that identifies themes and symbols based on past dream data. For example, the identification unit analyzes past dream data to identify frequently appearing themes and symbols. It can also analyze specific objects and situations appearing in dreams and calculate their frequency of appearance. Furthermore, the identification unit can cluster symbols and themes in dreams to identify those with high relevance. The identification unit also provides feedback to the user based on past dream data. For example, it explains the meaning of frequently appearing themes and symbols to the user. This improves the accuracy of the user's dream analysis by identifying themes and symbols based on past dream data. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input past dream data into an AI and have the AI ​​perform the identification of themes and symbols.

[0035] The dream analysis assistant includes an identification unit that identifies psychological states and stressors. For example, the identification unit analyzes the content of dreams to identify the user's psychological state and stressors. For instance, it analyzes specific symbols or themes appearing in dreams to determine whether they represent the user's psychological state or stressors. The identification unit can also analyze the relationship between dream content and the user's life circumstances to identify psychological states and stressors. Furthermore, the identification unit provides feedback to the user regarding the identified psychological states and stressors. For example, it explains to the user the psychological states and stressors indicated by the dream content. This supports the user's mental health by identifying psychological states and stressors. Some or all of the above-described processes in the identification unit may be performed using AI or not. For example, the identification unit can input dream content into AI and have the AI ​​perform the identification of psychological states and stressors.

[0036] The dream analysis assistant includes an advice unit that provides appropriate advice. The advice unit provides appropriate advice to the user based on the analysis results and identification results obtained by the analysis unit and identification unit, for example. For example, the advice unit provides psychological advice tailored to the user's psychological state and stress factors. The advice unit can also provide actionable guidelines based on the content of the user's dreams. Furthermore, the advice unit can suggest relaxation methods and stress relief techniques related to the content of the dreams. For example, the advice unit can suggest breathing exercises and meditation techniques for relaxation. The advice unit can also suggest exercises and hobbies for stress relief. By providing appropriate advice, the system deepens the user's self-understanding. Some or all of the above-described processes in the advice unit may be performed using AI, or they may not. For example, the advice unit can input analysis results and identification results into an AI, allowing the AI ​​to generate advice.

[0037] The analysis unit can analyze the content of dreams and explain their meaning and underlying psychological states. For example, the analysis unit can analyze the content of dreams using natural language processing technology and explain its meaning. For example, the analysis unit can analyze specific symbols and themes in dreams and explain what they represent. The analysis unit can also analyze the content of dreams from a psychological perspective and explain underlying psychological states. For example, the analysis unit can analyze specific situations and actions in dreams and explain what they represent. The analysis unit can also analyze the content of dreams based on cultural background and explain its meaning. For example, the analysis unit can analyze specific symbols and themes in dreams based on cultural background and explain what they represent. In this way, by analyzing the content of dreams and explaining their meaning and underlying psychological states, the system deepens the user's self-understanding.

[0038] The dream journal feature allows users to record their dreams daily and analyze dream patterns over the long term. For example, the dream journal can record the content of users' dreams digitally. For instance, users can input dream content in text format and save it as digital data. It can also allow users to input dream content in audio format and save it as digital data. Furthermore, it can allow users to input dream content in video format and save it as digital data. In addition, the dream journal can use cloud storage to store users' dream data long-term. For example, it can upload users' dream data to cloud storage for easy access. The dream journal can also analyze users' dream data to identify long-term dream patterns. For example, it can analyze users' dream data to identify frequently appearing themes and symbols. This allows users to understand their dream tendencies by recording their dreams daily and analyzing dream patterns over the long term.

[0039] The reception desk can analyze the user's past dream input history and provide the optimal input interface. For example, the reception desk can analyze the user's past dream input history and automatically display frequently entered content as suggestions. For example, the reception desk can present input suggestions based on the content of dreams the user has frequently entered in the past. The reception desk can also prioritize suggesting the user's past input methods (voice, text, etc.). For example, the reception desk can provide the optimal input interface based on the input methods the user has used in the past. The reception desk can also predict and suggest the content of dreams to be entered at a specific time period based on the user's past input history. For example, the reception desk can analyze the user's past input history, predict the content of dreams to be entered at a specific time period, and present it as an input suggestion. In this way, by analyzing past input history, the reception desk can provide the user with the optimal input interface. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into AI and have the AI ​​perform the task of providing the optimal input interface.

[0040] The reception unit can filter the input content based on the user's current life situation and areas of interest when the user enters dream content. For example, the reception unit prioritizes input of dreams relevant to the user's current life situation. For example, the reception unit presents input suggestions when the user enters dream content related to their current life situation. The reception unit can also filter relevant dream content and simplify input based on the user's areas of interest. For example, the reception unit presents input suggestions when the user enters dream content related to their areas of interest. The reception unit can also customize the input content and provide an appropriate input method based on the user's life situation and areas of interest. For example, the reception unit customizes the input interface and simplifies input based on the user's life situation and areas of interest. This simplifies input by filtering the input content based on the user's life situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input data on the user's life situation and areas of interest into the AI ​​and have the AI ​​perform the filtering of the input content.

[0041] The reception system can prioritize inputting highly relevant content when users input dream content, taking into account their geographical location. For example, if a user is in a specific region, the reception system will prioritize inputting dream content related to that region. For example, when a user inputs dream content related to a specific region, the reception system will present input suggestions. The reception system can also filter relevant dream content based on the user's geographical location, simplifying the input process. For example, when a user inputs dream content related to their geographical location, the reception system will present input suggestions. The reception system can also provide an appropriate input method, taking into account the user's geographical location. For example, the reception system will customize the input interface based on the user's geographical location, simplifying the input process. This allows for the priority input of highly relevant content by considering geographical location. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can input the user's geographical location into AI and have AI perform the filtering of input content.

[0042] The reception desk can analyze the user's social media activity when inputting dream content and input relevant content. For example, the reception desk can prioritize inputting relevant dream content based on the user's social media activity. For example, the reception desk can suggest input options when the user is inputting dream content related to their social media activity. The reception desk can also analyze the user's social media activity and filter relevant dream content. For example, the reception desk can suggest input options when the user is inputting dream content related to their social media activity. The reception desk can also provide an appropriate input method based on the user's social media activity. For example, the reception desk can customize the input interface and simplify input based on the user's social media activity. This allows for efficient input of relevant content by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's social media activity into AI and have AI perform filtering of the input content.

[0043] The analysis unit can adjust the level of detail in the analysis based on the importance of the dream. For example, in the case of an important dream, the analysis unit will provide detailed analysis results. For instance, the analysis unit will analyze the content of the dream in detail and explain its meaning and underlying psychological state in detail. The analysis unit can also provide concise analysis results for general dreams. For example, the analysis unit will briefly analyze the content of the dream and explain only the main points. Furthermore, the analysis unit can provide detailed analysis results for dreams that the user is particularly interested in. For example, the analysis unit will analyze the content of a dream that the user is particularly interested in in detail and explain its meaning and underlying psychological state in detail. In this way, by adjusting the level of detail in the analysis based on the importance of the dream, detailed analysis results can be provided for important dreams.

[0044] The analysis unit can apply different analysis algorithms depending on the dream category during analysis. For example, in the case of a nightmare, the analysis unit applies a specific analysis algorithm. For instance, it uses a specific algorithm for analyzing the content of nightmares to explain the meaning of the dream and the underlying psychological state. The analysis unit can also apply a different analysis algorithm for happy dreams. For example, it uses a different algorithm for analyzing the content of happy dreams to explain the meaning of the dream and the underlying psychological state. Furthermore, the analysis unit can apply yet another analysis algorithm for realistic dreams. For example, it uses yet another algorithm for analyzing the content of realistic dreams to explain the meaning of the dream and the underlying psychological state. By applying different analysis algorithms depending on the dream category, more accurate analysis results can be provided.

[0045] The analysis unit can prioritize the analysis based on when the dreams were submitted. For example, it can prioritize the analysis of recently submitted dreams. For instance, it can prioritize the analysis of the content of recently submitted dreams and explain their meaning and underlying psychological state. The analysis unit can also prioritize the analysis of dreams submitted within a specific period. For example, it can prioritize the analysis of the content of dreams submitted within a specific period and explain their meaning and underlying psychological state. Furthermore, the analysis unit can prioritize the analysis of dreams that the user is particularly interested in. For example, it can prioritize the analysis of dreams that the user is particularly interested in and explain their meaning and underlying psychological state. By prioritizing the analysis based on when the dreams were submitted, the most recent dreams can be analyzed first.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the dreams during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant dreams. For instance, it can prioritize the analysis of the content of highly relevant dreams and explain their meaning and underlying psychological state. The analysis unit can also postpone the analysis of less relevant dreams. For example, it can postpone the analysis of the content of less relevant dreams and explain their meaning and underlying psychological state. Furthermore, the analysis unit can prioritize the analysis of dreams that the user is particularly interested in. For instance, it can prioritize the analysis of dreams that the user is particularly interested in and explain their meaning and underlying psychological state. In this way, by adjusting the order of analysis based on the relevance of the dreams, highly relevant dreams can be prioritized.

[0047] The service provider can adjust the level of detail provided based on the importance of the analysis results. For example, if the analysis results are important, the service provider will provide detailed information. For instance, the service provider will provide a detailed report of the analysis results, explaining the meaning and underlying psychological state of the results in detail to the user. Alternatively, if the analysis results are general, the service provider can provide concise information. For example, the service provider will provide a concise report of the analysis results, explaining only the main points. Furthermore, if the analysis results are of particular interest to the user, the service provider can provide detailed information. For example, the service provider will provide a detailed report of the analysis results of particular interest to the user, explaining their meaning and underlying psychological state in detail. This allows the service provider to provide important information in detail by adjusting the level of detail provided based on the importance of the analysis results.

[0048] The service provider can apply different service provision algorithms depending on the category of the analysis results. For example, in the case of nightmare analysis results, the service provider applies a specific service provision algorithm. For instance, the service provider uses a specific algorithm for providing nightmare analysis results to explain the meaning of the analysis results and the underlying psychological state to the user. The service provider can also apply a different service provision algorithm in the case of happy dream analysis results. For example, the service provider uses a different algorithm for providing happy dream analysis results to explain the meaning of the analysis results and the underlying psychological state to the user. Furthermore, the service provider can apply yet another service provision algorithm in the case of realistic dream analysis results. For example, the service provider uses yet another algorithm for providing realistic dream analysis results to explain the meaning of the analysis results and the underlying psychological state to the user. By applying different service provision algorithms depending on the category of the analysis results, more appropriate information can be provided.

[0049] The service provider can determine the priority of service provision based on the submission date of the analysis results. For example, the service provider may prioritize providing recently submitted analysis results. For instance, the service provider may prioritize providing recently submitted analysis results to provide users with the latest information. The service provider may also prioritize providing analysis results submitted within a specific period. For example, the service provider may prioritize providing analysis results submitted within a specific period to provide users with important information. Furthermore, the service provider may also prioritize providing analysis results that users are particularly interested in. For example, the service provider may prioritize providing analysis results that users are particularly interested in and explain their meaning and underlying psychological states. By determining the priority of service provision based on the submission date of the analysis results, the service provider can prioritize providing the latest information.

[0050] The service provider can adjust the order in which analysis results are provided based on their relevance. For example, the service provider can prioritize providing highly relevant analysis results. For instance, the service provider can prioritize providing highly relevant analysis results to provide users with important information. The service provider can also postpone providing less relevant analysis results. For example, the service provider can postpone providing less relevant analysis results and explain only the main points to the user. Furthermore, the service provider can prioritize providing analysis results that the user is particularly interested in. For example, the service provider can prioritize providing analysis results that the user is particularly interested in and explain their meaning and underlying psychological state. In this way, by adjusting the order in which analysis results are provided based on their relevance, highly relevant information can be prioritized.

[0051] The diary function can provide the optimal recording method by referring to the user's past dream records when they are writing in their diary. For example, the diary function can suggest the optimal recording method based on the content of dreams the user has recorded in the past. For example, the diary function can analyze the content of dreams the user has recorded in the past and suggest the optimal recording method. The diary function can also provide an appropriate recording method by referring to the user's past recording methods. For example, the diary function can provide the optimal recording method based on the recording methods the user has used in the past. Furthermore, the diary function can predict and suggest the content of dreams to be recorded at a specific time period based on the user's past recording history. For example, the diary function can analyze the user's past recording history, predict the content of dreams to be recorded at a specific time period, and suggest a recording method. In this way, by referring to past records, the diary function can provide the user with the optimal recording method.

[0052] The diary function allows users to customize their entries based on their current lifestyle. For example, if a user is busy, the diary function provides a concise recording method, recording only the main points. Alternatively, if a user is relaxed, the diary function can provide a more detailed recording method, allowing them to record details of their dreams. Furthermore, the diary function can provide an appropriate recording method based on the user's lifestyle. For example, the diary function can customize the recording interface and simplify the recording process based on the user's lifestyle. This allows for more appropriate recording by customizing the content based on the user's lifestyle.

[0053] The diary function can provide the optimal recording method when a user is recording a diary entry, taking into account the user's geographical location. For example, if the user is in a specific region, the diary function will prioritize recording dreams related to that region. For example, when the user is recording dreams related to a specific region, the diary function will present recording options. The diary function can also filter relevant dream content based on the user's geographical location, simplifying the recording process. For example, when the user is recording dreams related to their geographical location, the diary function will present recording options. The diary function can also provide an appropriate recording method, taking into account the user's geographical location. For example, the diary function will customize the recording interface and simplify the recording process based on the user's geographical location. This allows for the priority recording of highly relevant content by considering geographical location.

[0054] The diary function can analyze the user's social media activity and suggest content to record when a diary entry is made. For example, the diary function can prioritize recording relevant dream content based on the user's social media activity. For example, when recording dream content related to the user's social media activity, the diary function can suggest recording options. The diary function can also analyze the user's social media activity and filter relevant dream content. For example, when recording dream content related to the user's social media activity, the diary function can suggest recording options. The diary function can also provide an appropriate recording method based on the user's social media activity. For example, the diary function can customize the recording interface and simplify recording based on the user's social media activity. This allows for efficient recording of relevant content by analyzing social media activity. Some or all of the above processing in the diary function may be performed using AI or not. For example, the diary function can input data on the user's social media activity into an AI and have the AI ​​suggest content to record.

[0055] The identification unit can optimize the identification algorithm by referring to past dream data at the time of identification. For example, the identification unit optimizes the identification algorithm based on dream data previously recorded by the user. For example, the identification unit analyzes dream data previously recorded by the user and optimizes the identification algorithm. The identification unit can also provide an appropriate identification algorithm by referring to the user's past dream data. For example, the identification unit provides an identification algorithm based on dream data previously recorded by the user. The identification unit can also analyze the user's past dream data and optimize the identification algorithm. For example, the identification unit analyzes dream data previously recorded by the user and optimizes the identification algorithm. By referring to past data, the identification algorithm can be optimized, enabling more accurate identification. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input the user's past dream data into AI and have AI perform the optimization of the identification algorithm.

[0056] The identification unit can determine specific priorities based on the time of dream submission. For example, the identification unit can prioritize recently submitted dreams. For example, the identification unit can prioritize the content of recently submitted dreams and explain their meaning and underlying psychological state. The identification unit can also prioritize dreams submitted within a specific period. For example, the identification unit can prioritize the content of dreams submitted within a specific period and explain their meaning and underlying psychological state. The identification unit can also prioritize dreams that the user is particularly interested in. For example, the identification unit can prioritize the content of dreams that the user is particularly interested in and explain their meaning and underlying psychological state. This allows for the prioritization of the most recent dreams by determining specific priorities based on the time of dream submission. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input data on the time of the user's dream submission into the AI ​​and have the AI ​​perform the determination of specific priorities.

[0057] The advice unit can provide optimal advice by referring to the analysis results of the user's past dreams when providing advice. For example, the advice unit can provide optimal advice based on the results of dreams the user has analyzed in the past. For example, the advice unit can analyze the results of dreams the user has analyzed in the past and provide optimal advice. The advice unit can also provide appropriate advice by referring to the user's past analysis results. For example, the advice unit can provide appropriate advice based on the results of dreams the user has analyzed in the past. The advice unit can also analyze the user's past analysis results and provide optimal advice. For example, the advice unit can analyze the results of dreams the user has analyzed in the past and provide optimal advice. In this way, by referring to past analysis results, the advice unit can provide the user with the best possible advice. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the analysis results of the user's past dreams into AI and have AI perform the task of providing optimal advice.

[0058] The advice unit can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the advice unit will provide advice relevant to that region. For example, the advice unit will provide advice relevant to a specific region and show the user appropriate action guidelines. The advice unit can also provide relevant advice based on the user's geographical location. For example, the advice unit will provide advice relevant to the user's geographical location and suggest appropriate relaxation methods or stress relief techniques. The advice unit can also provide appropriate advice by considering the user's geographical location. For example, the advice unit will customize the advice interface and simplify the advice based on the user's geographical location. This allows for the provision of highly relevant advice by considering geographical location. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's geographical location into AI and have AI perform the task of providing optimal advice.

[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 dream analysis assistant can take into account the user's current health condition when analyzing the content of their dreams. For example, the analysis unit can acquire the user's health data (e.g., sleep patterns, heart rate, stress level, etc.) and analyze the dream content based on this data. This allows the system to reveal the relationship between the user's health condition and the content of their dreams, providing more accurate analysis results. The analysis unit can also identify potential health risks indicated by the dream content based on the user's health condition and provide feedback to the user. For example, if the analysis unit sees a high stress level in the user's dream content, it can provide advice on how to reduce that stress. This enables dream analysis that takes the user's health condition into account, supporting the user's mental and physical well-being.

[0061] The dream analysis assistant can consider the user's past traumas and significant events when analyzing the content of their dreams. For example, the analysis unit can acquire data on the user's past traumas and significant events and use this data to analyze the dream content. This can reveal the relationship between the user's past experiences and the content of their dreams, providing deeper insights. The analysis unit can also identify how the user's past traumas and significant events have influenced the content of their dreams and provide feedback to the user. For example, if the analysis unit sees the user's past trauma being replayed in their dreams, it can provide advice on how to overcome that trauma. This enables dream analysis that takes the user's past experiences into account, supporting the user's psychological well-being.

[0062] The dream analysis assistant can consider a user's social relationships when analyzing the content of their dreams. For example, the analysis unit can acquire the user's social network data and analyze the dream content based on this data. This can reveal the relationship between the user's social relationships and the content of their dreams, providing deeper insights. The analysis unit can also identify how the user's social relationships influence the content of their dreams and provide feedback to the user. For example, if a particular person frequently appears in the user's dreams, the analysis unit can analyze the impact of that relationship on the user's psychological state and provide appropriate advice. This enables dream analysis that takes into account the user's social relationships, supporting the user's psychological well-being.

[0063] The dream analysis assistant can consider the user's cultural background when analyzing the content of their dreams. For example, the analysis unit can acquire data on the user's cultural background and analyze the dream content based on this data. This can reveal the relationship between the user's cultural background and the content of their dreams, providing deeper insights. The analysis unit can also identify how the user's cultural background influences the content of their dreams and provide feedback to the user. For example, the analysis unit can explain the meaning of specific symbols or themes in the dream based on the user's cultural background. This enables dream analysis that takes the user's cultural background into account, deepening the user's self-understanding.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk inputs the content of the dream the user had. Users can input the dream content in text, audio, or video format. For example, text input interfaces, audio input interfaces, and video input interfaces are provided, and speech recognition technology and video analysis technology are used to convert the input data into text data. Step 2: The analysis unit uses AI to analyze the content of the dream entered by the reception unit. The analysis is performed using natural language processing technology, image analysis technology, voice analysis technology, etc., and explains the meaning of the dream and the underlying psychological state. Step 3: The delivery unit provides the results analyzed by the analysis unit. The delivery is provided in the form of text reports, graphs, audio feedback, etc. Step 4: The diary section records the dreams the user has each day and analyzes dream patterns over the long term. Records can be made digitally, on paper, or via cloud storage.

[0066] (Example of form 2) The dream analysis assistant according to an embodiment of the present invention is a system that, upon input of a dream a user has had, analyzes its content and explains its meaning and underlying psychological state. The dream analysis assistant analyzes the content of a dream a user has had and explains its meaning and underlying psychological state. Furthermore, the dream analysis assistant provides a dream journal function, allowing the user to analyze dream patterns over the long term. For example, the dream analysis assistant receives input from the user about the content of their dream. Next, the dream analysis assistant uses AI to analyze the input content of the dream and explains its meaning and underlying psychological state. In addition, the dream analysis assistant allows the user to record their dreams daily using the dream journal function and analyze dream patterns over the long term. For example, based on past dream data, the dream analysis assistant identifies frequently appearing themes and symbols and provides feedback to the user. This mechanism allows the user to deepen their self-understanding and supports their mental health.

[0067] The dream analysis assistant according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a diary unit. The reception unit inputs the content of the dream the user has seen. The content of the dream the user has seen may include, but is not limited to, text format, audio format, or video format. The reception unit may, for example, provide a text input interface, allowing the user to input the dream content in text format. The reception unit may also provide an audio input interface, allowing the user to input the dream content in audio format. Furthermore, the reception unit may provide a video input interface, allowing the user to input the dream content in video format. For example, the reception unit may use speech recognition technology to convert audio input into text data. The reception unit may also use video analysis technology to convert video input into text data. The analysis unit uses AI to analyze the dream content input by the reception unit. The analysis may, for example, use natural language processing technology, image analysis technology, or audio analysis technology, but is not limited to these examples. For example, the analysis unit may use natural language processing technology to analyze the text data and explain the meaning of the dream and the underlying psychological state. Furthermore, the analysis unit can use image analysis technology to analyze video data and explain the meaning of dreams and underlying psychological states. The analysis unit can also use voice analysis technology to analyze audio data and explain the meaning of dreams and underlying psychological states. The provision unit provides the results analyzed by the analysis unit. Provision is provided in the form of, for example, text reports, graphs, and audio feedback, but is not limited to these examples. For example, the provision unit provides the analysis results as a text report. The provision unit can also provide the analysis results as a graph. The provision unit can also provide the analysis results as audio feedback. The diary unit records the dreams the user has each day and analyzes dream patterns over the long term. Recording is done in, for example, digital format, paper format, and cloud storage, but is not limited to these examples. For example, the diary unit records the content of dreams in digital format. The diary unit can also record the content of dreams on paper. The diary unit can also record the content of dreams using cloud storage.This allows the dream analysis assistant according to the embodiment to efficiently input, analyze, provide, and record the dreams the user has had.

[0068] The reception system inputs the content of the user's dreams. This content may include, but is not limited to, text, audio, or video formats. For example, the reception system provides a text input interface, allowing the user to input their dreams in text format. It also provides an audio input interface, enabling users to input their dreams in audio format. Furthermore, it provides a video input interface, allowing users to input their dreams in video format. For example, the reception system uses speech recognition technology to convert audio input into text data. It also uses video analysis technology to convert video input into text data. Specifically, the text input interface is designed to allow users to input dream content using a keyboard or touchscreen. The audio input interface captures the user's voice via a microphone and converts it to text using speech recognition technology. The video input interface uses a camera to capture dream scenes and related visuals drawn by the user and converts the video into text data using video analysis technology. This allows users to input their dreams in the most natural way possible, ensuring accurate recording of their content. Furthermore, the reception section is equipped with an interface for centrally managing the data entered by users and transmitting it to the analysis section. This ensures that the content of the dreams entered by users is quickly and accurately transmitted to the analysis section, allowing for smooth analysis in the next step.

[0069] The analysis unit uses AI to analyze the content of dreams entered by the reception unit. The analysis is performed using, but is not limited to, natural language processing, image analysis, and speech analysis technologies. For example, the analysis unit can use natural language processing to analyze text data and explain the meaning of the dream and the underlying psychological state. The analysis unit can also use image analysis to analyze video data and explain the meaning of the dream and the underlying psychological state. Furthermore, the analysis unit can use speech analysis to analyze audio data and explain the meaning of the dream and the underlying psychological state. Specifically, natural language processing is used to extract keywords and phrases from text data and analyze their relationships to identify the theme and underlying psychological state of the dream. Image analysis is used to recognize specific scenes and objects from video data and analyze their meaning. Speech analysis is used to analyze emotions and tone from audio data and clarify the psychological state behind the dream. In this way, the analysis unit can analyze the content of the user's dreams from multiple angles and provide a detailed explanation. Furthermore, the analysis unit can improve the accuracy of its analysis results by referencing past dream data and psychological databases. For example, by comparing current dream data with past data, it can identify specific patterns and trends, providing insights into the user's psychological state and life circumstances. The analysis unit can also incorporate user feedback and continuously improve its analysis algorithms. This allows the analysis unit to always provide users with highly accurate analysis results using the latest information and technology.

[0070] The service provider provides the results analyzed by the analysis provider. The results are provided in various forms, including, but are not limited to, text reports, graphs, and audio feedback. For example, the service provider may provide the analysis results as a text report, graphs, or audio feedback. Specifically, text reports provide detailed descriptions of the analysis results in a user-friendly format. Graphs visually represent the analysis results, allowing users to understand them at a glance. Audio feedback explains the analysis results verbally, enabling users to understand them aurally. This allows the service provider to offer analysis results in diverse formats, ensuring users receive information in the most easily understood way. Furthermore, the service provider can customize the delivery format according to user preferences. For example, if a user prefers text reports, a detailed text report is provided; if they prefer graphs, visual graphs are provided; and if they prefer audio feedback, the analysis results are explained verbally. This allows the service provider to respond flexibly to user needs and improve user satisfaction. Furthermore, the service also includes a function to save analysis results, allowing users to refer to them later. This enables users to review past analysis results and identify long-term dream patterns and trends.

[0071] The dream journal function allows users to record their daily dreams and analyze dream patterns over the long term. Recording can be done digitally, on paper, or via cloud storage, but is not limited to these methods. For example, the dream journal function can record dream content digitally, on paper, or using cloud storage. Specifically, in digital format, users input dream content into their smartphones or computers and record it using dedicated applications or software. In paper format, users handwrite dream content and save it in a dedicated notebook or diary. With cloud storage, users input dream content online and save it to the cloud, allowing access anytime, anywhere. This allows the dream journal function to record user dreams over the long term and analyze dream patterns and trends. Furthermore, the dream journal function can collaborate with the analysis function to perform long-term analysis of the recorded dream data. For example, based on past dream data, it can identify specific patterns and trends, providing insights into the user's psychological state and life circumstances. The dream journal function also includes a feature that allows users to easily search for dream content. This allows users to look back on past dreams and quickly find information about specific dreams or themes. Furthermore, the journal section also provides a function for users to share the content of their dreams with other users. This allows users to share their dreams with others and discuss common themes and patterns. In this way, the journal section can efficiently record the dreams that users have and support long-term analysis and sharing.

[0072] The dream analysis assistant includes an identification unit that identifies themes and symbols based on past dream data. For example, the identification unit analyzes past dream data to identify frequently appearing themes and symbols. It can also analyze specific objects and situations appearing in dreams and calculate their frequency of appearance. Furthermore, the identification unit can cluster symbols and themes in dreams to identify those with high relevance. The identification unit also provides feedback to the user based on past dream data. For example, it explains the meaning of frequently appearing themes and symbols to the user. This improves the accuracy of the user's dream analysis by identifying themes and symbols based on past dream data. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input past dream data into an AI and have the AI ​​perform the identification of themes and symbols.

[0073] The dream analysis assistant includes an identification unit that identifies psychological states and stressors. For example, the identification unit analyzes the content of dreams to identify the user's psychological state and stressors. For instance, it analyzes specific symbols or themes appearing in dreams to determine whether they represent the user's psychological state or stressors. The identification unit can also analyze the relationship between dream content and the user's life circumstances to identify psychological states and stressors. Furthermore, the identification unit provides feedback to the user regarding the identified psychological states and stressors. For example, it explains to the user the psychological states and stressors indicated by the dream content. This supports the user's mental health by identifying psychological states and stressors. Some or all of the above-described processes in the identification unit may be performed using AI or not. For example, the identification unit can input dream content into AI and have the AI ​​perform the identification of psychological states and stressors.

[0074] The dream analysis assistant includes an advice unit that provides appropriate advice. The advice unit provides appropriate advice to the user based on the analysis results and identification results obtained by the analysis unit and identification unit, for example. For example, the advice unit provides psychological advice tailored to the user's psychological state and stress factors. The advice unit can also provide actionable guidelines based on the content of the user's dreams. Furthermore, the advice unit can suggest relaxation methods and stress relief techniques related to the content of the dreams. For example, the advice unit can suggest breathing exercises and meditation techniques for relaxation. The advice unit can also suggest exercises and hobbies for stress relief. By providing appropriate advice, the system deepens the user's self-understanding. Some or all of the above-described processes in the advice unit may be performed using AI, or they may not. For example, the advice unit can input analysis results and identification results into an AI, allowing the AI ​​to generate advice.

[0075] The analysis unit can analyze the content of dreams and explain their meaning and underlying psychological states. For example, the analysis unit can analyze the content of dreams using natural language processing technology and explain its meaning. For example, the analysis unit can analyze specific symbols and themes in dreams and explain what they represent. The analysis unit can also analyze the content of dreams from a psychological perspective and explain underlying psychological states. For example, the analysis unit can analyze specific situations and actions in dreams and explain what they represent. The analysis unit can also analyze the content of dreams based on cultural background and explain its meaning. For example, the analysis unit can analyze specific symbols and themes in dreams based on cultural background and explain what they represent. In this way, by analyzing the content of dreams and explaining their meaning and underlying psychological states, the system deepens the user's self-understanding.

[0076] The dream journal feature allows users to record their dreams daily and analyze dream patterns over the long term. For example, the dream journal can record the content of users' dreams digitally. For instance, users can input dream content in text format and save it as digital data. It can also allow users to input dream content in audio format and save it as digital data. Furthermore, it can allow users to input dream content in video format and save it as digital data. In addition, the dream journal can use cloud storage to store users' dream data long-term. For example, it can upload users' dream data to cloud storage for easy access. The dream journal can also analyze users' dream data to identify long-term dream patterns. For example, it can analyze users' dream data to identify frequently appearing themes and symbols. This allows users to understand their dream tendencies by recording their dreams daily and analyzing dream patterns over the long term.

[0077] The reception unit can estimate the user's emotions and adjust the input method for dream content based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate input by adjusting the input method according to 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. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception desk can input image data of the user captured by a camera into a generative AI, which can then perform an estimation of the user's emotions.

[0078] The reception desk can analyze the user's past dream input history and provide the optimal input interface. For example, the reception desk can analyze the user's past dream input history and automatically display frequently entered content as suggestions. For example, the reception desk can present input suggestions based on the content of dreams the user has frequently entered in the past. The reception desk can also prioritize suggesting the user's past input methods (voice, text, etc.). For example, the reception desk can provide the optimal input interface based on the input methods the user has used in the past. The reception desk can also predict and suggest the content of dreams to be entered at a specific time period based on the user's past input history. For example, the reception desk can analyze the user's past input history, predict the content of dreams to be entered at a specific time period, and present it as an input suggestion. In this way, by analyzing past input history, the reception desk can provide the user with the optimal input interface. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into AI and have the AI ​​perform the task of providing the optimal input interface.

[0079] The reception unit can filter the input content based on the user's current life situation and areas of interest when the user enters dream content. For example, the reception unit prioritizes input of dreams relevant to the user's current life situation. For example, the reception unit presents input suggestions when the user enters dream content related to their current life situation. The reception unit can also filter relevant dream content and simplify input based on the user's areas of interest. For example, the reception unit presents input suggestions when the user enters dream content related to their areas of interest. The reception unit can also customize the input content and provide an appropriate input method based on the user's life situation and areas of interest. For example, the reception unit customizes the input interface and simplifies input based on the user's life situation and areas of interest. This simplifies input by filtering the input content based on the user's life situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input data on the user's life situation and areas of interest into the AI ​​and have the AI ​​perform the filtering of the input content.

[0080] The reception unit can estimate the user's emotions and prioritize the content of the dreams to be entered based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate. This allows the system to prioritize input content according to the user's emotions, ensuring that important content is entered first. 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. Some or all of the above-described processes at the reception desk may be performed using AI, or they may not be performed using AI. For example, the reception desk can input image data of the user captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0081] The reception system can prioritize inputting highly relevant content when users input dream content, taking into account their geographical location. For example, if a user is in a specific region, the reception system will prioritize inputting dream content related to that region. For example, when a user inputs dream content related to a specific region, the reception system will present input suggestions. The reception system can also filter relevant dream content based on the user's geographical location, simplifying the input process. For example, when a user inputs dream content related to their geographical location, the reception system will present input suggestions. The reception system can also provide an appropriate input method, taking into account the user's geographical location. For example, the reception system will customize the input interface based on the user's geographical location, simplifying the input process. This allows for the priority input of highly relevant content by considering geographical location. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can input the user's geographical location into AI and have AI perform the filtering of input content.

[0082] The reception desk can analyze the user's social media activity when inputting dream content and input relevant content. For example, the reception desk can prioritize inputting relevant dream content based on the user's social media activity. For example, the reception desk can suggest input options when the user is inputting dream content related to their social media activity. The reception desk can also analyze the user's social media activity and filter relevant dream content. For example, the reception desk can suggest input options when the user is inputting dream content related to their social media activity. The reception desk can also provide an appropriate input method based on the user's social media activity. For example, the reception desk can customize the input interface and simplify input based on the user's social media activity. This allows for efficient input of relevant content by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's social media activity into AI and have AI perform filtering of the input content.

[0083] The analysis unit can estimate the user's emotions and adjust the method of expressing the analysis based on the estimated user emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. By adjusting the method of expressing the analysis according to the user's emotions, more appropriate analysis results can be provided. 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. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user image data captured by a camera into a generating AI, allowing the AI ​​to estimate the user's emotions.

[0084] The analysis unit can adjust the level of detail in the analysis based on the importance of the dream. For example, in the case of an important dream, the analysis unit will provide detailed analysis results. For instance, the analysis unit will analyze the content of the dream in detail and explain its meaning and underlying psychological state in detail. The analysis unit can also provide concise analysis results for general dreams. For example, the analysis unit will briefly analyze the content of the dream and explain only the main points. Furthermore, the analysis unit can provide detailed analysis results for dreams that the user is particularly interested in. For example, the analysis unit will analyze the content of a dream that the user is particularly interested in in detail and explain its meaning and underlying psychological state in detail. In this way, by adjusting the level of detail in the analysis based on the importance of the dream, detailed analysis results can be provided for important dreams.

[0085] The analysis unit can apply different analysis algorithms depending on the dream category during analysis. For example, in the case of a nightmare, the analysis unit applies a specific analysis algorithm. For instance, it uses a specific algorithm for analyzing the content of nightmares to explain the meaning of the dream and the underlying psychological state. The analysis unit can also apply a different analysis algorithm for happy dreams. For example, it uses a different algorithm for analyzing the content of happy dreams to explain the meaning of the dream and the underlying psychological state. Furthermore, the analysis unit can apply yet another analysis algorithm for realistic dreams. For example, it uses yet another algorithm for analyzing the content of realistic dreams to explain the meaning of the dream and the underlying psychological state. By applying different analysis algorithms depending on the dream category, more accurate analysis results can be provided.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user image data captured by a camera into a generating AI, allowing the AI ​​to estimate the user's emotions.

[0087] The analysis unit can prioritize the analysis based on when the dreams were submitted. For example, it can prioritize the analysis of recently submitted dreams. For instance, it can prioritize the analysis of the content of recently submitted dreams and explain their meaning and underlying psychological state. The analysis unit can also prioritize the analysis of dreams submitted within a specific period. For example, it can prioritize the analysis of the content of dreams submitted within a specific period and explain their meaning and underlying psychological state. Furthermore, the analysis unit can prioritize the analysis of dreams that the user is particularly interested in. For example, it can prioritize the analysis of dreams that the user is particularly interested in and explain their meaning and underlying psychological state. By prioritizing the analysis based on when the dreams were submitted, the most recent dreams can be analyzed first.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the dreams during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant dreams. For instance, it can prioritize the analysis of the content of highly relevant dreams and explain their meaning and underlying psychological state. The analysis unit can also postpone the analysis of less relevant dreams. For example, it can postpone the analysis of the content of less relevant dreams and explain their meaning and underlying psychological state. Furthermore, the analysis unit can prioritize the analysis of dreams that the user is particularly interested in. For instance, it can prioritize the analysis of dreams that the user is particularly interested in and explain their meaning and underlying psychological state. In this way, by adjusting the order of analysis based on the relevance of the dreams, highly relevant dreams can be prioritized.

[0089] The service provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in facial expressions. The service provider can also record the user's voice and estimate the emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice and calculate an emotion score. The service provider can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate. This allows the service provider to provide more appropriate information by adjusting the way the information is presented according to 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. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input image data of the user captured by a camera into a generating AI, and have the generating AI perform the estimation of the user's emotions.

[0090] The service provider can adjust the level of detail provided based on the importance of the analysis results. For example, if the analysis results are important, the service provider will provide detailed information. For instance, the service provider will provide a detailed report of the analysis results, explaining the meaning and underlying psychological state of the results in detail to the user. Alternatively, if the analysis results are general, the service provider can provide concise information. For example, the service provider will provide a concise report of the analysis results, explaining only the main points. Furthermore, if the analysis results are of particular interest to the user, the service provider can provide detailed information. For example, the service provider will provide a detailed report of the analysis results of particular interest to the user, explaining their meaning and underlying psychological state in detail. This allows the service provider to provide important information in detail by adjusting the level of detail provided based on the importance of the analysis results.

[0091] The service provider can apply different service provision algorithms depending on the category of the analysis results. For example, in the case of nightmare analysis results, the service provider applies a specific service provision algorithm. For instance, the service provider uses a specific algorithm for providing nightmare analysis results to explain the meaning of the analysis results and the underlying psychological state to the user. The service provider can also apply a different service provision algorithm in the case of happy dream analysis results. For example, the service provider uses a different algorithm for providing happy dream analysis results to explain the meaning of the analysis results and the underlying psychological state to the user. Furthermore, the service provider can apply yet another service provision algorithm in the case of realistic dream analysis results. For example, the service provider uses yet another algorithm for providing realistic dream analysis results to explain the meaning of the analysis results and the underlying psychological state to the user. By applying different service provision algorithms depending on the category of the analysis results, more appropriate information can be provided.

[0092] The service provider can estimate the user's emotions and adjust the length of the information provided based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in facial expressions. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice and calculate an emotion score. The service provider can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate. This allows the service provider to provide more appropriate information by adjusting the length of the information according to 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. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input image data of the user captured by a camera into a generating AI, and have the generating AI perform the estimation of the user's emotions.

[0093] The service provider can determine the priority of service provision based on the submission date of the analysis results. For example, the service provider may prioritize providing recently submitted analysis results. For instance, the service provider may prioritize providing recently submitted analysis results to provide users with the latest information. The service provider may also prioritize providing analysis results submitted within a specific period. For example, the service provider may prioritize providing analysis results submitted within a specific period to provide users with important information. Furthermore, the service provider may also prioritize providing analysis results that users are particularly interested in. For example, the service provider may prioritize providing analysis results that users are particularly interested in and explain their meaning and underlying psychological states. By determining the priority of service provision based on the submission date of the analysis results, the service provider can prioritize providing the latest information.

[0094] The service provider can adjust the order in which analysis results are provided based on their relevance. For example, the service provider can prioritize providing highly relevant analysis results. For instance, the service provider can prioritize providing highly relevant analysis results to provide users with important information. The service provider can also postpone providing less relevant analysis results. For example, the service provider can postpone providing less relevant analysis results and explain only the main points to the user. Furthermore, the service provider can prioritize providing analysis results that the user is particularly interested in. For example, the service provider can prioritize providing analysis results that the user is particularly interested in and explain their meaning and underlying psychological state. In this way, by adjusting the order in which analysis results are provided based on their relevance, highly relevant information can be prioritized.

[0095] The diary unit can estimate the user's emotions and adjust the diary recording method based on the estimated emotions. For example, the diary unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the diary unit can calculate an emotion score based on changes in facial expressions. The diary unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the diary unit can analyze the tone and speed of the voice and calculate an emotion score. The diary unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the diary unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate recording by adjusting the diary recording method according to 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. Some or all of the above processing in the diary unit may be performed using AI or not. For example, the diary function can input image data of the user taken with a camera into a generative AI, which can then perform an estimation of the user's emotions.

[0096] The diary function can provide the optimal recording method by referring to the user's past dream records when they are writing in their diary. For example, the diary function can suggest the optimal recording method based on the content of dreams the user has recorded in the past. For example, the diary function can analyze the content of dreams the user has recorded in the past and suggest the optimal recording method. The diary function can also provide an appropriate recording method by referring to the user's past recording methods. For example, the diary function can provide the optimal recording method based on the recording methods the user has used in the past. Furthermore, the diary function can predict and suggest the content of dreams to be recorded at a specific time period based on the user's past recording history. For example, the diary function can analyze the user's past recording history, predict the content of dreams to be recorded at a specific time period, and suggest a recording method. In this way, by referring to past records, the diary function can provide the user with the optimal recording method.

[0097] The diary function allows users to customize their entries based on their current lifestyle. For example, if a user is busy, the diary function provides a concise recording method, recording only the main points. Alternatively, if a user is relaxed, the diary function can provide a more detailed recording method, allowing them to record details of their dreams. Furthermore, the diary function can provide an appropriate recording method based on the user's lifestyle. For example, the diary function can customize the recording interface and simplify the recording process based on the user's lifestyle. This allows for more appropriate recording by customizing the content based on the user's lifestyle.

[0098] The diary function can estimate the user's emotions and determine the priority of diary entries based on the estimated emotions. For example, the diary function can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the diary function can calculate an emotion score based on changes in facial expressions. The diary function can also record the user's voice and estimate emotions using voice analysis technology. For example, the diary function can analyze the tone and speed of the voice and calculate an emotion score. The diary function can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the diary function can calculate an emotion score based on fluctuations in heart rate. This allows important content to be recorded preferentially by determining the recording priority according to the user's emotions. Emotion estimation is implemented 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. Some or all of the above processing in the diary function may be performed using AI or not. For example, the diary function can input image data of the user taken with a camera into a generative AI, which can then perform an estimation of the user's emotions.

[0099] The diary function can provide the optimal recording method when a user is recording a diary entry, taking into account the user's geographical location. For example, if the user is in a specific region, the diary function will prioritize recording dreams related to that region. For example, when the user is recording dreams related to a specific region, the diary function will present recording options. The diary function can also filter relevant dream content based on the user's geographical location, simplifying the recording process. For example, when the user is recording dreams related to their geographical location, the diary function will present recording options. The diary function can also provide an appropriate recording method, taking into account the user's geographical location. For example, the diary function will customize the recording interface and simplify the recording process based on the user's geographical location. This allows for the priority recording of highly relevant content by considering geographical location.

[0100] The diary function can analyze the user's social media activity and suggest content to record when a diary entry is made. For example, the diary function can prioritize recording relevant dream content based on the user's social media activity. For example, when recording dream content related to the user's social media activity, the diary function can suggest recording options. The diary function can also analyze the user's social media activity and filter relevant dream content. For example, when recording dream content related to the user's social media activity, the diary function can suggest recording options. The diary function can also provide an appropriate recording method based on the user's social media activity. For example, the diary function can customize the recording interface and simplify recording based on the user's social media activity. This allows for efficient recording of relevant content by analyzing social media activity. Some or all of the above processing in the diary function may be performed using AI or not. For example, the diary function can input data on the user's social media activity into an AI and have the AI ​​suggest content to record.

[0101] The identification unit can estimate the user's emotions and determine the priority of themes and symbols to identify based on the estimated emotions. For example, the identification unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the identification unit can calculate an emotion score based on changes in facial expressions. The identification unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the identification unit can analyze the tone and speed of the voice and calculate an emotion score. The identification unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the identification unit can calculate an emotion score based on fluctuations in heart rate. This allows for the priority of themes and symbols to be determined according to the user's emotions, thereby prioritizing the identification of important content. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the specific unit may be performed using AI or not using AI. For example, the specific unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0102] The identification unit can optimize the identification algorithm by referring to past dream data at the time of identification. For example, the identification unit optimizes the identification algorithm based on dream data previously recorded by the user. For example, the identification unit analyzes dream data previously recorded by the user and optimizes the identification algorithm. The identification unit can also provide an appropriate identification algorithm by referring to the user's past dream data. For example, the identification unit provides an identification algorithm based on dream data previously recorded by the user. The identification unit can also analyze the user's past dream data and optimize the identification algorithm. For example, the identification unit analyzes dream data previously recorded by the user and optimizes the identification algorithm. By referring to past data, the identification algorithm can be optimized, enabling more accurate identification. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input the user's past dream data into AI and have AI perform the optimization of the identification algorithm.

[0103] The identification unit can estimate the user's emotions and adjust the display method of identified themes and symbols based on the estimated user emotions. For example, the identification unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the identification unit can calculate an emotion score based on changes in facial expressions. The identification unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the identification unit can analyze the tone and speed of the voice and calculate an emotion score. The identification unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the identification unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate display by adjusting the display method according to 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. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the specific unit can input image data of the user captured by the camera into a generating AI, and have the generating AI perform the estimation of the user's emotions.

[0104] The identification unit can determine specific priorities based on the time of dream submission. For example, the identification unit can prioritize recently submitted dreams. For example, the identification unit can prioritize the content of recently submitted dreams and explain their meaning and underlying psychological state. The identification unit can also prioritize dreams submitted within a specific period. For example, the identification unit can prioritize the content of dreams submitted within a specific period and explain their meaning and underlying psychological state. The identification unit can also prioritize dreams that the user is particularly interested in. For example, the identification unit can prioritize the content of dreams that the user is particularly interested in and explain their meaning and underlying psychological state. This allows for the prioritization of the most recent dreams by determining specific priorities based on the time of dream submission. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input data on the time of the user's dream submission into the AI ​​and have the AI ​​perform the determination of specific priorities.

[0105] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on the estimated emotions. For example, the advice unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the advice unit can calculate an emotion score based on changes in facial expressions. The advice unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the advice unit can analyze the tone and speed of the voice and calculate an emotion score. The advice unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the advice unit can calculate an emotion score based on fluctuations in heart rate. This allows the advice unit to provide more appropriate advice by adjusting the way it expresses advice according to 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. Some or all of the above-described processes in the advice unit may be performed using AI or not. For example, the advice unit can input image data of the user captured by the camera into a generating AI, and have the generating AI perform an estimation of the user's emotions.

[0106] The advice unit can provide optimal advice by referring to the analysis results of the user's past dreams when providing advice. For example, the advice unit can provide optimal advice based on the results of dreams the user has analyzed in the past. For example, the advice unit can analyze the results of dreams the user has analyzed in the past and provide optimal advice. The advice unit can also provide appropriate advice by referring to the user's past analysis results. For example, the advice unit can provide appropriate advice based on the results of dreams the user has analyzed in the past. The advice unit can also analyze the user's past analysis results and provide optimal advice. For example, the advice unit can analyze the results of dreams the user has analyzed in the past and provide optimal advice. In this way, by referring to past analysis results, the advice unit can provide the user with the best possible advice. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the analysis results of the user's past dreams into AI and have AI perform the task of providing optimal advice.

[0107] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, the advice unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the advice unit can calculate an emotion score based on changes in facial expressions. The advice unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the advice unit can analyze the tone and speed of the voice and calculate an emotion score. The advice unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the advice unit can calculate an emotion score based on fluctuations in heart rate. This allows the advice unit to prioritize important advice by determining the priority of advice according to 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. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input image data of the user captured by the camera into a generating AI, and have the generating AI perform an estimation of the user's emotions.

[0108] The advice unit can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the advice unit will provide advice relevant to that region. For example, the advice unit will provide advice relevant to a specific region and show the user appropriate action guidelines. The advice unit can also provide relevant advice based on the user's geographical location. For example, the advice unit will provide advice relevant to the user's geographical location and suggest appropriate relaxation methods or stress relief techniques. The advice unit can also provide appropriate advice by considering the user's geographical location. For example, the advice unit will customize the advice interface and simplify the advice based on the user's geographical location. This allows for the provision of highly relevant advice by considering geographical location. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's geographical location into AI and have AI perform the task of providing optimal advice.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The dream analysis assistant can take into account the user's current health condition when analyzing the content of their dreams. For example, the analysis unit can acquire the user's health data (e.g., sleep patterns, heart rate, stress level, etc.) and analyze the dream content based on this data. This allows the system to reveal the relationship between the user's health condition and the content of their dreams, providing more accurate analysis results. The analysis unit can also identify potential health risks indicated by the dream content based on the user's health condition and provide feedback to the user. For example, if the analysis unit sees a high stress level in the user's dream content, it can provide advice on how to reduce that stress. This enables dream analysis that takes the user's health condition into account, supporting the user's mental and physical well-being.

[0111] The dream analysis assistant can consider the user's past traumas and significant events when analyzing the content of their dreams. For example, the analysis unit can acquire data on the user's past traumas and significant events and use this data to analyze the dream content. This can reveal the relationship between the user's past experiences and the content of their dreams, providing deeper insights. The analysis unit can also identify how the user's past traumas and significant events have influenced the content of their dreams and provide feedback to the user. For example, if the analysis unit sees the user's past trauma being replayed in their dreams, it can provide advice on how to overcome that trauma. This enables dream analysis that takes the user's past experiences into account, supporting the user's psychological well-being.

[0112] The dream analysis assistant can estimate the user's emotions when analyzing the content of their dreams and customize the analysis results based on those emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. This allows the system to understand the user's emotional state when they input their dream content and provide analysis results tailored to those emotions. For instance, if the user is feeling anxious, the analysis unit can identify factors in the dream that are causing that anxiety and provide advice to alleviate it. Similarly, if the user is feeling joyful, the analysis unit can identify factors in the dream that are causing that joy and provide advice to amplify that joy. This allows the system to provide customized analysis results tailored to the user's emotions, supporting their psychological well-being.

[0113] The dream analysis assistant can consider a user's social relationships when analyzing the content of their dreams. For example, the analysis unit can acquire the user's social network data and analyze the dream content based on this data. This can reveal the relationship between the user's social relationships and the content of their dreams, providing deeper insights. The analysis unit can also identify how the user's social relationships influence the content of their dreams and provide feedback to the user. For example, if a particular person frequently appears in the user's dreams, the analysis unit can analyze the impact of that relationship on the user's psychological state and provide appropriate advice. This enables dream analysis that takes into account the user's social relationships, supporting the user's psychological well-being.

[0114] The dream analysis assistant can consider the user's cultural background when analyzing the content of their dreams. For example, the analysis unit can acquire data on the user's cultural background and analyze the dream content based on this data. This can reveal the relationship between the user's cultural background and the content of their dreams, providing deeper insights. The analysis unit can also identify how the user's cultural background influences the content of their dreams and provide feedback to the user. For example, the analysis unit can explain the meaning of specific symbols or themes in the dream based on the user's cultural background. This enables dream analysis that takes the user's cultural background into account, deepening the user's self-understanding.

[0115] The dream analysis assistant can estimate the user's emotions when analyzing the content of their dreams and prioritize the analysis based on those emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. This allows the system to prioritize the analysis of dreams in which the user has particularly strong emotions. For instance, if the user has a dream in which they feel fear, the analysis unit can prioritize the analysis of that dream, identify the cause of the fear, and provide advice on how to alleviate it. Similarly, if the user has a dream in which they feel joy, the analysis unit can prioritize the analysis of that dream, identify the factors contributing to the joy, and provide advice on how to amplify that joy. This allows the system to analyze dreams with priorities aligned with the user's emotions, supporting their psychological well-being.

[0116] The dream analysis assistant can estimate the user's emotions when analyzing the content of their dreams and adjust the presentation of the analysis results based on those estimated emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. This allows the system to understand the user's emotional state when they input the dream content and provide analysis results tailored to those emotions. For instance, if the user is feeling sad, the analysis unit can identify the factors in the dream that are causing the sadness and provide advice to alleviate that sadness. Similarly, if the user is feeling excited, the analysis unit can identify the factors in the dream that are causing the excitement and provide advice to manage that excitement appropriately. This allows the system to provide customized analysis results tailored to the user's emotions, supporting their psychological well-being.

[0117] The dream analysis assistant can estimate the user's emotions when analyzing the content of their dreams and adjust the level of detail of the analysis based on those emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. This allows for a detailed analysis of dreams in which the user has particularly strong emotions. For instance, if the user dreams of anger, the analysis unit can analyze the dream in detail, identify the cause of the anger, and provide advice on how to alleviate it. Similarly, if the user dreams of happiness, the analysis unit can analyze the dream in detail, identify the factors contributing to that happiness, and provide advice on how to enhance that happiness. This allows for dream analysis with a level of detail appropriate to the user's emotions, supporting their psychological well-being.

[0118] The dream analysis assistant can estimate the user's emotions when analyzing the content of their dreams and adjust the way the analysis results are provided based on those estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the system to understand the user's emotional state when they input the dream content and provide analysis results tailored to those emotions. For instance, if the user is feeling anxious, the analysis unit can identify factors in the dream that are causing the anxiety and provide advice to alleviate that anxiety. Similarly, if the user is relaxed, the analysis unit can identify factors in the dream that are causing relaxation and provide advice to maintain that relaxation. This allows the system to provide customized analysis results tailored to the user's emotions, supporting their psychological well-being.

[0119] The dream analysis assistant can estimate the user's emotions when analyzing the content of their dreams and adjust the timing of the analysis based on those emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. This allows the system to understand the user's emotional state when they input their dream content and provide analysis results at a timing appropriate to those emotions. For instance, if the user is stressed, the analysis unit can identify factors in the dream that cause stress and provide advice to alleviate that stress. Similarly, if the user is relaxed, the analysis unit can identify factors in the dream that cause relaxation and provide advice to maintain that relaxation. This allows the system to analyze dreams at a timing appropriate to the user's emotions, supporting their psychological well-being.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk inputs the content of the dream the user had. Users can input the dream content in text, audio, or video format. For example, text input interfaces, audio input interfaces, and video input interfaces are provided, and speech recognition technology and video analysis technology are used to convert the input data into text data. Step 2: The analysis unit uses AI to analyze the content of the dream entered by the reception unit. The analysis is performed using natural language processing technology, image analysis technology, voice analysis technology, etc., and explains the meaning of the dream and the underlying psychological state. Step 3: The delivery unit provides the results analyzed by the analysis unit. The delivery is provided in the form of text reports, graphs, audio feedback, etc. Step 4: The diary section records the dreams the user has each day and analyzes dream patterns over the long term. Records can be made digitally, on paper, or via cloud storage.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, diary unit, identification unit, and advice unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which inputs the content of the dream the user had. The analysis unit is implemented by the identification unit 290 of the data processing unit 12, which analyzes the content of the dream using AI. The provision unit is implemented by the control unit 46A of the smart device 14, which provides the analysis results to the user. The diary unit is implemented by the control unit 46A of the smart device 14, which records the content of the dream. The identification unit is implemented by the identification unit 290 of the data processing unit 12, which identifies themes and symbols based on past dream data. The advice unit is implemented by the identification unit 290 of the data processing unit 12, which provides appropriate advice based on the analysis results and identification results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, diary unit, identification unit, and advice unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which inputs the content of the dream the user had. The analysis unit is implemented by the identification unit 290 of the data processing unit 12, which analyzes the content of the dream using AI. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides the analysis results to the user. The diary unit is implemented by the control unit 46A of the smart glasses 214, which records the content of the dream. The identification unit is implemented by the identification unit 290 of the data processing unit 12, which identifies themes and symbols based on past dream data. The advice unit is implemented by the identification unit 290 of the data processing unit 12, which provides appropriate advice based on the analysis results and identification results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, diary unit, identification unit, and advice unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which inputs the content of the dream the user had. The analysis unit is implemented by the identification unit 290 of the data processing unit 12, which analyzes the content of the dream using AI. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides the analysis results to the user. The diary unit is implemented by the control unit 46A of the headset terminal 314, which records the content of the dream. The identification unit is implemented by the identification unit 290 of the data processing unit 12, which identifies themes and symbols based on past dream data. The advice unit is implemented by the identification unit 290 of the data processing unit 12, which provides appropriate advice based on the analysis results and identification results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, diary unit, identification unit, and advice unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and inputs the content of the dream the user had. The analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes the content of the dream using AI. The provision unit is implemented by the control unit 46A of the robot 414 and provides the analysis results to the user. The diary unit is implemented by the control unit 46A of the robot 414 and records the content of the dream. The identification unit is implemented by the identification unit 290 of the data processing unit 12 and identifies themes and symbols based on past dream data. The advice unit is implemented by the identification unit 290 of the data processing unit 12 and provides appropriate advice based on the analysis results and identification results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A reception desk where you enter the details of your dream, An analysis unit analyzes the content of the dream entered by the reception unit, A providing unit that provides the results of the analysis performed by the aforementioned analysis unit, It includes a diary section for managing dream records. A system characterized by the following features. (Note 2) It is equipped with a special unit that identifies themes and symbols based on past dream data. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a special unit for identifying psychological states and stressors. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with an advisory department to provide appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, This analyzes the content of dreams and explains their meaning and underlying psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned diary section, The system records the dreams users have each day and analyzes dream patterns over the long term. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the way dream content is entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past dream input history and provides the optimal input interface. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input the content of their dreams, the system filters the input based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the dream content to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter the content of their dreams, the system prioritizes input of highly relevant content, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter the content of their dreams, the system analyzes their social media activity and inputs relevant information. 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, the level of detail of the analysis is adjusted based on the importance of the dream. 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 dream 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 the analysis, the priority of the analysis will be determined based on when the dreams were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the dreams. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the data, we will adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the results, different provisioning algorithms will be applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the data, the priority of provision will be determined based on the timing of the submission of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the data, the order of delivery will be adjusted based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned diary section, The system estimates the user's emotions and adjusts the diary recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned diary section, When users record their dreams in their journal, the system provides the optimal recording method by referencing their past dream records. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned diary section, When writing a diary, the content of the entry is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned diary section, The system estimates the user's emotions and determines the priority of diary entries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned diary section, When recording a diary, the system provides the optimal recording 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 diary section, When you write in your diary, the system analyzes your social media activity and suggests content to record. The system described in Appendix 1, characterized by the features described herein. (Note 31) The specified part is, It estimates the user's emotions and determines the priority of themes and symbols to identify based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The specified part is, At specific times, the system optimizes a particular algorithm by referencing past dream data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The specified part is, It estimates the user's emotions and adjusts how specific themes and symbols are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The specified part is, At specific times, a particular priority is determined based on the timing of dream submissions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned advice section, When providing advice, we refer to the analysis results of the user's past dreams to provide the most suitable advice. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned advice section, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0194] 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 reception desk where you enter the details of your dream, An analysis unit analyzes the content of the dream entered by the reception unit, A providing unit that provides the results of the analysis performed by the aforementioned analysis unit, It includes a diary section for managing dream records. A system characterized by the following features.

2. It is equipped with a special unit that identifies themes and symbols based on past dream data. The system according to feature 1.

3. It includes a special unit for identifying psychological states and stressors. The system according to feature 1.

4. Equipped with an advisory department to provide appropriate advice. The system according to feature 1.

5. The aforementioned analysis unit, This analyzes the content of dreams and explains their meaning and underlying psychological state. The system according to feature 1.

6. The aforementioned diary section, The system records the dreams users have each day and analyzes dream patterns over the long term. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the way dream content is entered based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past dream input history and provides the optimal input interface. The system according to feature 1.

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