System

Abstract

An object of a system according to an embodiment is to enable a user to easily interpret the content and emotion of his / her dream.SOLUTION: A system includes an input unit, an analysis unit, and a provision unit. The input part receives the content and feeling of the dream of the user. The analysis part analyzes the content and feeling of the dream received by the input part. The provision unit provides the meaning of the dream and the message analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that it is difficult for users to interpret the content and emotions of their dreams, and specialized knowledge is required.

[0005] The system according to the embodiment aims to enable users to easily interpret the content and emotions of their dreams. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, and a provision unit. The input unit receives the content and emotions of a user's dream. The analysis unit analyzes the content and emotions of the dream received by the input unit. The provision unit provides the meaning and message of the dream analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to easily interpret the content and emotions of their dreams. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The dream interpretation system according to an embodiment of the present invention is a system in which, when a user communicates the content and emotions of a dream to a generation AI, the generation AI analyzes the information and provides the meaning and message of the dream. This allows the dream interpretation system to analyze the user's dream and provide the meaning and message.

[0029] The dream interpretation system according to the embodiment includes an input unit, an analysis unit, and a providing unit. The input unit accepts the content and emotions of a user's dream. For example, the user inputs the content of the dream in text format. The input unit can also record the content of the dream using voice input. For example, the user inputs the content of the dream using voice, and the generation AI analyzes the voice data. The input unit can also upload illustrations or sketches of the dream drawn by the user. For example, the user expresses the content of the dream using illustrations or sketches and uploads them to the generation AI. The analysis unit analyzes the content and emotions of the dream accepted by the input unit. For example, the generation AI analyzes the content and emotions of the dream input by the user and interprets the meaning and message of the dream. The analysis unit can also use an emotion estimation function to analyze the facial expression and tone of voice when the user inputs the content of the dream and automatically complete the intensity and type of emotion. For example, when the user inputs the content of the dream, the facial expression and tone of voice can be analyzed using a camera or microphone, and the intensity and type of emotion can be automatically completed. The providing unit provides the meaning and message of the dream analyzed by the analysis unit. For example, the generation AI provides the user with the meaning and message of the dream based on the analysis results. The providing unit can also provide a more personalized message by referring to the user's past dream interpretation results and feedback. For example, when providing the meaning and message of a dream, the generation AI can provide a more personalized message by referring to the user's past dream interpretation results. This allows the dream interpretation system according to the embodiment to analyze the user's dream and provide the meaning and message. For example, a user can input the content of their dream and review their daily behavior based on the analysis results, thereby leading a healthier life. Furthermore, those interested in dream interpretation can gain new discoveries and enjoyment by learning the meaning of their dreams.

[0030] The input unit can record the content of dreams using voice input, and the analysis unit can analyze the voice data. For example, the input unit allows a user to input the content of their dream through voice, and the generation AI analyzes the voice data. For example, if a user says, "Last night, I dreamed of swimming in a big ocean," the generation AI converts the voice into text and analyzes it. The input unit can also use voice input to allow the user to describe the content of their dream in detail. For example, the user can input specific details such as, "While I was swimming in the ocean, a big wave suddenly came and surprised me," and the generation AI analyzes the details. The input unit can also allow the user to input the content of their dream through voice, along with the emotions they felt at the time. For example, if the user says, "I felt very free while swimming in the ocean," the generation AI will include those emotions in the analysis. This allows the content of dreams to be recorded using voice input and the voice data to be analyzed.

[0031] The input unit can upload illustrations or sketches of dreams drawn by the user, and the analysis unit can analyze the visual data. For example, the input unit allows the user to express the content of their dream in an illustration or sketch and upload it to the generation AI. For example, the user can upload an illustration of a scene swimming in the ocean, and the generation AI analyzes the visual data. The input unit can also allow the user to express the content of their dream in a sketch and upload that sketch to the generation AI. For example, the user can sketch the scenery or characters seen in the dream, and the generation AI analyzes the visual data. The input unit can also allow the user to express the content of their dream in an illustration and upload that illustration to the generation AI. For example, the user can upload an illustration that expresses the emotions felt in the dream using colors and shapes, and the generation AI analyzes the visual data. This makes it possible to analyze the illustrations and sketches of the dreams drawn by the user.

[0032] The input unit provides an interface that allows a user to select dream scenes and characters in a choice-based format, thereby reducing the effort required for input. For example, when a user inputs the content of a dream, the input unit provides an interface that allows a user to select dream scenes and characters in a choice-based format. For example, scenes such as "ocean," "mountain," and "city" and characters such as "family," "friends," and "animals" can be selected. The input unit also provides an interface that allows a user to select dream scenes and characters in a choice-based format when inputting the content of a dream, thereby reducing the effort required for input. For example, in response to the question "Where were you in your dream?", options such as "ocean," "mountain," and "city" are provided. The input unit also provides an interface that allows a user to select dream scenes and characters in a choice-based format when inputting the content of a dream, thereby reducing the effort required for input. For example, in response to the question "Who were you with in your dream?", options such as "family," "friends," and "animals" are provided. This provides an interface that allows a user to select dream scenes and characters in a choice-based format, thereby reducing the effort required for input.

[0033] The input unit may add a function that allows a user to refer to similar dream examples of other users, and use the examples as a reference for input. For example, when a user inputs the content of a dream, the input unit may add a function that allows a user to refer to similar dream examples of other users. For example, when a user inputs "a dream of swimming in the ocean," examples of "dreams of swimming in the ocean" that other users had. The input unit may also add a function that allows a user to refer to similar dream examples of other users, and use the examples as a reference for input. For example, when a user inputs "a dream of being surprised by big waves," examples of "dreams of being surprised by big waves" that other users had. The input unit may also add a function that allows a user to refer to similar dream examples of other users, and use the examples as a reference for input. For example, when a user inputs "a dream of swimming freely," examples of "dreams of swimming freely" that other users had. This adds a function that allows a user to refer to similar dream examples of other users, and use the examples as a reference for input.

[0034] The analysis unit can refer to the user's past dream data and psychological state history to provide a more accurate interpretation. For example, when the generation AI analyzes the content of a dream, the analysis unit can refer to the user's past dream data to provide a more accurate interpretation. For example, it can analyze the patterns of past dreams and interpret the meaning of the current dream. The analysis unit can also refer to the user's psychological state history to allow the generation AI to analyze the content of the dream. For example, it can compare the content of the current dream with the user's past psychological state to provide a more accurate interpretation. The analysis unit can also refer to the user's past dream data and psychological state history to provide a more accurate interpretation when the generation AI analyzes the content of a dream. For example, it can compare the emotions in past dreams with the emotions in the current dream and reflect them in the interpretation. This allows the user to refer to the user's past dream data and psychological state history to provide a more accurate interpretation.

[0035] The analysis unit can refer to dream interpretation databases from different cultures and regions to provide interpretations from a global perspective. For example, when the generation AI analyzes the content of a dream, the analysis unit can refer to dream interpretation databases from different cultures to provide interpretations from a global perspective. For example, the meaning of the dream can be interpreted based on dream interpretation data from Asia or Europe. The analysis unit can also refer to dream interpretation databases from different regions to enable the generation AI to analyze the content of the dream. For example, the meaning of the dream can be interpreted based on dream interpretation data from North America or South America. The analysis unit can also refer to dream interpretation databases from different cultures and regions to provide interpretations from a global perspective. For example, the meaning of the dream can be interpreted based on dream interpretation data from Africa or the Middle East. This allows the generation AI to refer to dream interpretation databases from different cultures and regions to provide interpretations from a global perspective.

[0036] The analysis unit can refer to data related to the user's living environment and daily events to provide a more specific interpretation. For example, when the generation AI analyzes the content of a dream, the analysis unit can refer to data related to the user's living environment to provide a more specific interpretation. For example, the analysis unit can interpret the meaning of a dream based on data related to the user's area of ​​residence and occupation. The analysis unit can also refer to data related to the user's daily events to enable the generation AI to analyze the content of a dream. For example, the analysis unit can interpret the meaning of a dream based on recent events and stress factors. The analysis unit can also refer to data related to the user's living environment and daily events to provide a more specific interpretation when the generation AI analyzes the content of a dream. For example, the analysis unit can interpret the meaning of a dream based on data related to the user's home environment and relationships. This allows the analysis unit to refer to data related to the user's living environment and daily events to provide a more specific interpretation.

[0037] The providing unit can refer to the user's past dream interpretation results and feedback to provide a more personalized message. For example, when the generation AI provides the meaning of a dream or a message, the providing unit can refer to the user's past dream interpretation results to provide a more personalized message. For example, the meaning of the current dream is interpreted based on the past interpretation results. The providing unit can also refer to the user's feedback so that the generation AI can provide the meaning of a dream or a message. For example, the most appropriate message for the user is provided based on the past feedback. The providing unit can also refer to the user's past dream interpretation results and feedback so that the generation AI can provide a more personalized message when the generation AI provides the meaning of a dream or a message. For example, the meaning of the current dream is interpreted based on the patterns of past dreams. This makes it possible to refer to the user's past dream interpretation results and feedback to provide a more personalized message.

[0038] The providing unit can include specific advice or suggestions according to the user's current psychological state or living situation. For example, when the generation AI provides the meaning or message of a dream, the providing unit includes specific advice according to the user's current psychological state. For example, if stress is high, the providing unit can suggest ways to relax. The providing unit can also include specific suggestions according to the user's living situation. For example, if the dream is caused by work stress, the providing unit can suggest ways to improve work. The providing unit can also include specific advice or suggestions according to the user's current psychological state or living situation when the generation AI provides the meaning or message of a dream. For example, if the dream is caused by family problems, the providing unit can suggest ways to improve the home. This makes it possible to include specific advice or suggestions according to the user's current psychological state or living situation.

[0039] The providing unit can present multiple interpretations and messages for the user to choose from, allowing the user to select the one that most resonates with them. For example, when the generation AI provides the meaning or message of a dream, the providing unit presents multiple interpretations for the user to choose from. For example, multiple interpretations such as "I am seeking freedom" or "I want to take on a new challenge" can be presented, allowing the user to select one. The providing unit can also present multiple messages for the user to choose from. For example, multiple messages such as "You are seeking freedom" or "It's time to take on a new challenge" can be presented, allowing the user to select one. For example, multiple messages such as "You are seeking freedom" or "It's time to take on a new challenge" can be presented, allowing the user to select one. In this way, multiple interpretations and messages can be presented for the user to choose from, allowing the user to select the one that most resonates with them.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The dream interpretation system may also have a function for sharing the content of a user's dreams with other users. For example, when a user inputs the content of a dream, the content can be shared anonymously with other users and feedback can be received from them. Other users can also add comments and interpretations to the shared dream content. This allows the user to obtain interpretations from other perspectives and gain a deeper understanding of the meaning of the dream. Furthermore, if other users have had similar dreams based on the content of the shared dream, they can refer to the interpretation of that dream. In this way, the dream interpretation system can promote interaction between users and enrich dream interpretation.

[0042] The dream interpretation system can further include a function to recommend related literary works or movies based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," related literary works or movies can be recommended, and the meaning of the dream can be more deeply understood through those works. Similarly, if a user inputs "a dream of being surprised by big waves," related works can be similarly recommended, and the interpretation of the dream can be complemented through those works. In this way, the dream interpretation system can more deeply understand the meaning of the dream through related literary works or movies based on the content of the user's dream.

[0043] The dream interpretation system can further include a function to provide relevant psychological interpretations and theories based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," relevant psychological interpretations and theories can be provided, allowing for a deeper understanding of the meaning of the dream. Similarly, if a user inputs "a dream of being surprised by a big wave," relevant psychological interpretations and theories can be provided to complement the interpretation of the dream. In this way, the dream interpretation system can provide a deeper understanding of the meaning of the dream through relevant psychological interpretations and theories based on the content of the user's dream.

[0044] The dream interpretation system can further include a function to provide related historical events and cultural background based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," the system can provide related historical events and cultural background, thereby providing a deeper understanding of the meaning of the dream. Similarly, if a user inputs "a dream of being surprised by a big wave," the system can similarly provide related historical events and cultural background to complement the interpretation of the dream. In this way, the dream interpretation system can provide a deeper understanding of the meaning of the dream through related historical events and cultural background based on the content of the user's dream.

[0045] The dream interpretation system can further include a function to recommend related music and artwork based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," related music and artwork can be recommended, and the meaning of the dream can be more deeply understood through those artworks. Similarly, if a user inputs "a dream of being surprised by big waves," related artworks can be similarly recommended, and the interpretation of the dream can be complemented through those artworks. In this way, the dream interpretation system can more deeply understand the meaning of the dream through related music and artworks based on the content of the user's dream.

[0046] The dream interpretation system can further include a function to provide related health information and lifestyle advice based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," related health information and lifestyle advice can be provided, allowing for a deeper understanding of the meaning of the dream. Similarly, if a user inputs "a dream of being surprised by a big wave," related health information and lifestyle advice can be provided to complement the interpretation of the dream. In this way, the dream interpretation system can provide a deeper understanding of the meaning of the dream through related health information and lifestyle advice based on the content of the user's dream.

[0047] The dream interpretation system can further include a function to provide related meditation and relaxation methods based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," related meditation and relaxation methods can be provided, allowing the user to gain a deeper understanding of the meaning of the dream. Similarly, if a user inputs "a dream of being surprised by big waves," related meditation and relaxation methods can be provided to complement the interpretation of the dream. In this way, the dream interpretation system can provide a deeper understanding of the meaning of the dream through related meditation and relaxation methods based on the content of the user's dream.

[0048] The dream interpretation system can also have a function to recommend related travel destinations and tourist attractions based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," related travel destinations and tourist attractions can be recommended, and visiting those places can help the user gain a deeper understanding of the meaning of the dream. Similarly, if a user inputs "a dream of being surprised by big waves," related travel destinations and tourist attractions can be recommended, and visiting those places can help complement the interpretation of the dream. In this way, the dream interpretation system can provide a deeper understanding of the meaning of the dream through related travel destinations and tourist attractions based on the content of the user's dream.

[0049] The dream interpretation system can further include a function to recommend related hobbies and activities based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," related hobbies and activities can be recommended, and the meaning of the dream can be more deeply understood through those activities. Similarly, if a user inputs "a dream of being surprised by big waves," related hobbies and activities can be similarly recommended, and the interpretation of the dream can be complemented through those activities. In this way, the dream interpretation system can more deeply understand the meaning of the dream through related hobbies and activities based on the content of the user's dream.

[0050] The dream interpretation system can further include a function to provide related learning resources and educational content based on the content of the user's dream. For example, if a user inputs "I dreamed of swimming in the ocean," related learning resources and educational content can be provided, through which the meaning of the dream can be more deeply understood. Similarly, if a user inputs "I dreamed of being surprised by big waves," related learning resources and educational content can be provided, through which the interpretation of the dream can be supplemented. In this way, the dream interpretation system can provide a deeper understanding of the meaning of the dream through related learning resources and educational content based on the content of the user's dream.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The input unit accepts the content and emotions of the user's dream. For example, the user can input the content of the dream in text format. The input unit can also record the content of the dream using voice input. Furthermore, the user can upload illustrations or sketches of the dream. Step 2: The analysis unit analyzes the dream content and emotions received by the input unit. For example, the generation AI analyzes the dream content and emotions input by the user and interprets the meaning and message of the dream. The analysis unit can also use an emotion estimation function to analyze facial expressions and tone of voice when the user inputs the dream content, automatically completing the intensity and type of emotions. Step 3: The provider provides the dream meaning and message analyzed by the analyzer. For example, the generation AI provides the user with the dream meaning and message based on the analysis results. The provider can also refer to the user's past dream interpretation results and feedback to provide a more personalized message.

[0053] (Example 2) The dream interpretation system according to an embodiment of the present invention is a system in which, when a user communicates the content and emotions of a dream to a generation AI, the generation AI analyzes the information and provides the meaning and message of the dream. This allows the dream interpretation system to analyze the user's dream and provide the meaning and message.

[0054] The dream interpretation system according to the embodiment includes an input unit, an analysis unit, and a providing unit. The input unit accepts the content and emotions of a user's dream. For example, the user inputs the content of the dream in text format. The input unit can also record the content of the dream using voice input. For example, the user inputs the content of the dream using voice, and the generation AI analyzes the voice data. The input unit can also upload illustrations or sketches of the dream drawn by the user. For example, the user expresses the content of the dream using illustrations or sketches and uploads them to the generation AI. The analysis unit analyzes the content and emotions of the dream accepted by the input unit. For example, the generation AI analyzes the content and emotions of the dream input by the user and interprets the meaning and message of the dream. The analysis unit can also use an emotion estimation function to analyze the facial expression and tone of voice when the user inputs the content of the dream and automatically complete the intensity and type of emotion. For example, when the user inputs the content of the dream, the facial expression and tone of voice can be analyzed using a camera or microphone, and the intensity and type of emotion can be automatically completed. The providing unit provides the meaning and message of the dream analyzed by the analysis unit. For example, the generation AI provides the user with the meaning and message of the dream based on the analysis results. The providing unit can also provide a more personalized message by referring to the user's past dream interpretation results and feedback. For example, when providing the meaning and message of a dream, the generation AI can provide a more personalized message by referring to the user's past dream interpretation results. This allows the dream interpretation system according to the embodiment to analyze the user's dream and provide the meaning and message. For example, a user can input the content of their dream and review their daily behavior based on the analysis results, thereby leading a healthier life. Furthermore, those interested in dream interpretation can gain new discoveries and enjoyment by learning the meaning of their dreams.

[0055] The input unit can record the content of dreams using voice input, and the analysis unit can analyze the voice data. For example, the input unit allows a user to input the content of their dream through voice, and the generation AI analyzes the voice data. For example, if a user says, "Last night, I dreamed of swimming in a big ocean," the generation AI converts the voice into text and analyzes it. The input unit can also use voice input to allow the user to describe the content of their dream in detail. For example, the user can input specific details such as, "While I was swimming in the ocean, a big wave suddenly came and surprised me," and the generation AI analyzes the details. The input unit can also allow the user to input the content of their dream through voice, along with the emotions they felt at the time. For example, if the user says, "I felt very free while swimming in the ocean," the generation AI will include those emotions in the analysis. This allows the content of dreams to be recorded using voice input and the voice data to be analyzed.

[0056] The input unit can upload illustrations or sketches of dreams drawn by the user, and the analysis unit can analyze the visual data. For example, the input unit allows the user to express the content of their dream in an illustration or sketch and upload it to the generation AI. For example, the user can upload an illustration of a scene swimming in the ocean, and the generation AI analyzes the visual data. The input unit can also allow the user to express the content of their dream in a sketch and upload that sketch to the generation AI. For example, the user can sketch the scenery or characters seen in the dream, and the generation AI analyzes the visual data. The input unit can also allow the user to express the content of their dream in an illustration and upload that illustration to the generation AI. For example, the user can upload an illustration that expresses the emotions felt in the dream using colors and shapes, and the generation AI analyzes the visual data. This makes it possible to analyze the illustrations and sketches of the dreams drawn by the user.

[0057] The input unit can use the emotion estimation function to analyze the facial expression and tone of voice when the user inputs the content of their dream, and automatically complete the intensity and type of emotion. For example, when the user inputs the content of their dream, the input unit uses a camera or microphone to analyze the facial expression and tone of voice, and automatically complete the intensity and type of emotion. For example, if the user says, "I was very scared," the intensity of the emotion is analyzed from the facial expression and tone of voice. The input unit can also use the emotion estimation function to analyze the facial expression when the user inputs the content of their dream, and automatically complete the intensity and type of emotion. For example, if the user speaks with a smile, the emotion is included in the analysis as "joy." The input unit can also analyze the tone of voice when the user inputs the content of their dream, and automatically complete the intensity and type of emotion. For example, if the user speaks in a low voice, the emotion is included in the analysis as "sadness." In this way, the input unit can analyze the facial expression and tone of voice when the user inputs the content of their dream, and automatically complete the intensity and type of emotion.

[0058] The input unit provides an interface that allows a user to select dream scenes and characters in a choice-based format, thereby reducing the effort required for input. For example, when a user inputs the content of a dream, the input unit provides an interface that allows a user to select dream scenes and characters in a choice-based format. For example, scenes such as "ocean," "mountain," and "city" and characters such as "family," "friends," and "animals" can be selected. The input unit also provides an interface that allows a user to select dream scenes and characters in a choice-based format when inputting the content of a dream, thereby reducing the effort required for input. For example, in response to the question "Where were you in your dream?", options such as "ocean," "mountain," and "city" are provided. The input unit also provides an interface that allows a user to select dream scenes and characters in a choice-based format when inputting the content of a dream, thereby reducing the effort required for input. For example, in response to the question "Who were you with in your dream?", options such as "family," "friends," and "animals" are provided. This provides an interface that allows a user to select dream scenes and characters in a choice-based format, thereby reducing the effort required for input.

[0059] The input unit may add a function that allows a user to refer to similar dream examples of other users, and use the examples as a reference for input. For example, when a user inputs the content of a dream, the input unit may add a function that allows a user to refer to similar dream examples of other users. For example, when a user inputs "a dream of swimming in the ocean," examples of "dreams of swimming in the ocean" that other users had. The input unit may also add a function that allows a user to refer to similar dream examples of other users, and use the examples as a reference for input. For example, when a user inputs "a dream of being surprised by big waves," examples of "dreams of being surprised by big waves" that other users had. The input unit may also add a function that allows a user to refer to similar dream examples of other users, and use the examples as a reference for input. For example, when a user inputs "a dream of swimming freely," examples of "dreams of swimming freely" that other users had. This adds a function that allows a user to refer to similar dream examples of other users, and use the examples as a reference for input.

[0060] The input unit can use the emotion estimation function to provide real-time emotional feedback according to the input content to encourage input. For example, the input unit can use the emotion estimation function to provide real-time emotional feedback according to the input content when the user inputs the content of their dream. For example, if the user inputs "I was very scared," the emotional feedback of "fear" is displayed. The input unit can also use the emotion estimation function to provide real-time emotional feedback according to the input content when the user inputs the content of their dream to encourage input. For example, if the user inputs "I was swimming feeling free," the emotional feedback of "freedom" is displayed. The input unit can also use the emotion estimation function to provide real-time emotional feedback according to the input content when the user inputs the content of their dream to encourage input. For example, if the user inputs "I was surprised by the big waves," the emotional feedback of "surprise" is displayed. In this way, real-time emotional feedback according to the input content can be provided to encourage input.

[0061] The analysis unit can refer to the user's past dream data and psychological state history to provide a more accurate interpretation. For example, when the generation AI analyzes the content of a dream, the analysis unit can refer to the user's past dream data to provide a more accurate interpretation. For example, it can analyze the patterns of past dreams and interpret the meaning of the current dream. The analysis unit can also refer to the user's psychological state history to allow the generation AI to analyze the content of the dream. For example, it can compare the content of the current dream with the user's past psychological state to provide a more accurate interpretation. The analysis unit can also refer to the user's past dream data and psychological state history to provide a more accurate interpretation when the generation AI analyzes the content of a dream. For example, it can compare the emotions in past dreams with the emotions in the current dream and reflect them in the interpretation. This allows the user to refer to the user's past dream data and psychological state history to provide a more accurate interpretation.

[0062] The analysis unit can use the emotion estimation function to estimate the user's current psychological state and stress level based on the analysis results of the dream content and emotions, and reflect the result in the interpretation. For example, the analysis unit can use the emotion estimation function to estimate the user's current psychological state based on the analysis results of the dream content and emotions. For example, the analysis unit can analyze the current psychological state based on the emotions felt in the dream. The analysis unit can also estimate the user's stress level based on the results of the analysis of the dream content and emotions by the generation AI. For example, the analysis unit can analyze the stress level based on the anxiety or fear felt in the dream. The analysis unit can also use the emotion estimation function to estimate the user's current psychological state and stress level based on the analysis results of the dream content and emotions, and reflect the result in the interpretation. For example, the analysis unit can analyze the current psychological state based on the joy or sadness felt in the dream. In this way, the emotion estimation function can estimate the user's current psychological state and stress level based on the analysis results of the dream content and emotions, and reflect the result in the interpretation.

[0063] The analysis unit can refer to dream interpretation databases from different cultures and regions to provide interpretations from a global perspective. For example, when the generation AI analyzes the content of a dream, the analysis unit can refer to dream interpretation databases from different cultures to provide interpretations from a global perspective. For example, the meaning of the dream can be interpreted based on dream interpretation data from Asia or Europe. The analysis unit can also refer to dream interpretation databases from different regions to enable the generation AI to analyze the content of the dream. For example, the meaning of the dream can be interpreted based on dream interpretation data from North America or South America. The analysis unit can also refer to dream interpretation databases from different cultures and regions to provide interpretations from a global perspective. For example, the meaning of the dream can be interpreted based on dream interpretation data from Africa or the Middle East. This allows the generation AI to refer to dream interpretation databases from different cultures and regions to provide interpretations from a global perspective.

[0064] The analysis unit can refer to data related to the user's living environment and daily events to provide a more specific interpretation. For example, when the generation AI analyzes the content of a dream, the analysis unit can refer to data related to the user's living environment to provide a more specific interpretation. For example, the analysis unit can interpret the meaning of a dream based on data related to the user's area of ​​residence and occupation. The analysis unit can also refer to data related to the user's daily events to enable the generation AI to analyze the content of a dream. For example, the analysis unit can interpret the meaning of a dream based on recent events and stress factors. The analysis unit can also refer to data related to the user's living environment and daily events to provide a more specific interpretation when the generation AI analyzes the content of a dream. For example, the analysis unit can interpret the meaning of a dream based on data related to the user's home environment and relationships. This allows the analysis unit to refer to data related to the user's living environment and daily events to provide a more specific interpretation.

[0065] The analysis unit can use the emotion estimation function to analyze the user's emotional fluctuation patterns based on the analysis results of the dream content and emotions, and reflect the long-term trends in their psychological state in the interpretation. For example, the analysis unit can use the emotion estimation function to analyze the user's emotional fluctuation patterns based on the analysis results of the dream content and emotions. For example, the analysis unit can analyze the emotional fluctuation patterns felt in the dream and reflect the long-term trends in their psychological state in the interpretation. The analysis unit can also analyze the user's emotional fluctuation patterns based on the results of the analysis of the dream content and emotions using the generation AI. For example, the analysis unit can analyze the user's emotional fluctuation patterns based on the analysis results of the dream content and emotions. For example, the analysis unit can analyze the user's emotional fluctuation patterns based on the analysis results of the dream content and emotions, and reflect the long-term trends in their psychological state in the interpretation. For example, the analysis unit can analyze the user's long-term psychological state based on the intensity and type of emotions felt in the dream. This allows the emotion estimation function to analyze the user's emotional fluctuation patterns based on the analysis results of the dream content and emotions, and reflect the long-term trends in their psychological state in the interpretation.

[0066] The providing unit can refer to the user's past dream interpretation results and feedback to provide a more personalized message. For example, when the generation AI provides the meaning of a dream or a message, the providing unit can refer to the user's past dream interpretation results to provide a more personalized message. For example, the meaning of the current dream is interpreted based on the past interpretation results. The providing unit can also refer to the user's feedback so that the generation AI can provide the meaning of a dream or a message. For example, the most appropriate message for the user is provided based on the past feedback. The providing unit can also refer to the user's past dream interpretation results and feedback so that the generation AI can provide a more personalized message when the generation AI provides the meaning of a dream or a message. For example, the meaning of the current dream is interpreted based on the patterns of past dreams. This makes it possible to refer to the user's past dream interpretation results and feedback to provide a more personalized message.

[0067] The providing unit can include specific advice or suggestions according to the user's current psychological state or living situation. For example, when the generation AI provides the meaning or message of a dream, the providing unit includes specific advice according to the user's current psychological state. For example, if stress is high, the providing unit can suggest ways to relax. The providing unit can also include specific suggestions according to the user's living situation. For example, if the dream is caused by work stress, the providing unit can suggest ways to improve work. The providing unit can also include specific advice or suggestions according to the user's current psychological state or living situation when the generation AI provides the meaning or message of a dream. For example, if the dream is caused by family problems, the providing unit can suggest ways to improve the home. This makes it possible to include specific advice or suggestions according to the user's current psychological state or living situation.

[0068] The providing unit can use the emotion estimation function to automatically generate words of encouragement or comfort according to the user's emotions when providing the meaning of a dream or a message, thereby providing emotional support. For example, the providing unit can use the emotion estimation function to automatically generate words of encouragement according to the user's emotions when providing the meaning of a dream or a message. For example, if the user is feeling anxious, the providing unit can provide a message such as "It's okay, you're strong." The providing unit can also automatically generate words of comfort according to the user's emotions. For example, if the user is feeling sad, the providing unit can provide a message such as "I understand how you feel." The providing unit can also use the emotion estimation function to automatically generate words of encouragement or comfort according to the user's emotions when providing the meaning of a dream or a message, thereby providing emotional support. For example, if the user is feeling stressed, the providing unit can provide a message such as "Relax and take a deep breath." In this way, the emotion estimation function can automatically generate words of encouragement or comfort according to the user's emotions when providing the meaning of a dream or a message, thereby providing emotional support.

[0069] The providing unit can present multiple interpretations and messages for the user to choose from, allowing the user to select the one that most resonates with them. For example, when the generation AI provides the meaning or message of a dream, the providing unit presents multiple interpretations for the user to choose from. For example, multiple interpretations such as "I am seeking freedom" or "I want to take on a new challenge" can be presented, allowing the user to select one. The providing unit can also present multiple messages for the user to choose from. For example, multiple messages such as "You are seeking freedom" or "It's time to take on a new challenge" can be presented, allowing the user to select one. For example, multiple messages such as "You are seeking freedom" or "It's time to take on a new challenge" can be presented, allowing the user to select one. In this way, multiple interpretations and messages can be presented for the user to choose from, allowing the user to select the one that most resonates with them.

[0070] The providing unit can use the emotion estimation function to monitor the user's emotional reactions in real time when providing the meaning of a dream or a message, and adjust the message as needed. For example, the providing unit can use the emotion estimation function to monitor the user's emotional reactions in real time when providing the meaning of a dream or a message. For example, if the user is feeling anxious, the providing unit can adjust the message to give a sense of security. The providing unit can also monitor the user's emotional reactions in real time and adjust the message as needed. For example, if the user is feeling happy, the providing unit can provide a message that reinforces that emotion. The providing unit can also use the emotion estimation function to monitor the user's emotional reactions in real time when providing the meaning of a dream or a message, and adjust the message as needed. For example, if the user is feeling sad, the providing unit can add words of comfort. In this way, the emotion estimation function can monitor the user's emotional reactions in real time when providing the meaning of a dream or a message, and adjust the message as needed.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The dream interpretation system may also have a function for sharing the content of a user's dreams with other users. For example, when a user inputs the content of a dream, the content can be shared anonymously with other users and feedback can be received from them. Other users can also add comments and interpretations to the shared dream content. This allows the user to obtain interpretations from other perspectives and gain a deeper understanding of the meaning of the dream. Furthermore, if other users have had similar dreams based on the content of the shared dream, they can refer to the interpretation of that dream. In this way, the dream interpretation system can promote interaction between users and enrich dream interpretation.

[0073] The dream interpretation system can further include a function to recommend related literary works or movies based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," related literary works or movies can be recommended, and the meaning of the dream can be more deeply understood through those works. Similarly, if a user inputs "a dream of being surprised by big waves," related works can be similarly recommended, and the interpretation of the dream can be complemented through those works. In this way, the dream interpretation system can more deeply understand the meaning of the dream through related literary works or movies based on the content of the user's dream.

[0074] The dream interpretation system can further include a function to provide relevant psychological interpretations and theories based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," relevant psychological interpretations and theories can be provided, allowing for a deeper understanding of the meaning of the dream. Similarly, if a user inputs "a dream of being surprised by a big wave," relevant psychological interpretations and theories can be provided to complement the interpretation of the dream. In this way, the dream interpretation system can provide a deeper understanding of the meaning of the dream through relevant psychological interpretations and theories based on the content of the user's dream.

[0075] The dream interpretation system can further include a function to provide related historical events and cultural background based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," the system can provide related historical events and cultural background, thereby providing a deeper understanding of the meaning of the dream. Similarly, if a user inputs "a dream of being surprised by a big wave," the system can similarly provide related historical events and cultural background to complement the interpretation of the dream. In this way, the dream interpretation system can provide a deeper understanding of the meaning of the dream through related historical events and cultural background based on the content of the user's dream.

[0076] The dream interpretation system can further include a function to recommend related music and artwork based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," related music and artwork can be recommended, and the meaning of the dream can be more deeply understood through those artworks. Similarly, if a user inputs "a dream of being surprised by big waves," related artworks can be similarly recommended, and the interpretation of the dream can be complemented through those artworks. In this way, the dream interpretation system can more deeply understand the meaning of the dream through related music and artworks based on the content of the user's dream.

[0077] The dream interpretation system can further include a function to provide related health information and lifestyle advice based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," related health information and lifestyle advice can be provided, allowing for a deeper understanding of the meaning of the dream. Similarly, if a user inputs "a dream of being surprised by a big wave," related health information and lifestyle advice can be provided to complement the interpretation of the dream. In this way, the dream interpretation system can provide a deeper understanding of the meaning of the dream through related health information and lifestyle advice based on the content of the user's dream.

[0078] The dream interpretation system can further include a function to provide related meditation and relaxation methods based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," related meditation and relaxation methods can be provided, allowing the user to gain a deeper understanding of the meaning of the dream. Similarly, if a user inputs "a dream of being surprised by big waves," related meditation and relaxation methods can be provided to complement the interpretation of the dream. In this way, the dream interpretation system can provide a deeper understanding of the meaning of the dream through related meditation and relaxation methods based on the content of the user's dream.

[0079] The dream interpretation system can also have a function to recommend related travel destinations and tourist attractions based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," related travel destinations and tourist attractions can be recommended, and visiting those places can help the user gain a deeper understanding of the meaning of the dream. Similarly, if a user inputs "a dream of being surprised by big waves," related travel destinations and tourist attractions can be recommended, and visiting those places can help complement the interpretation of the dream. In this way, the dream interpretation system can provide a deeper understanding of the meaning of the dream through related travel destinations and tourist attractions based on the content of the user's dream.

[0080] The dream interpretation system can further include a function to recommend related hobbies and activities based on the content of the user's dream. For example, if a user inputs "a dream of swimming in the ocean," related hobbies and activities can be recommended, and the meaning of the dream can be more deeply understood through those activities. Similarly, if a user inputs "a dream of being surprised by big waves," related hobbies and activities can be similarly recommended, and the interpretation of the dream can be complemented through those activities. In this way, the dream interpretation system can more deeply understand the meaning of the dream through related hobbies and activities based on the content of the user's dream.

[0081] The dream interpretation system can further include a function to provide related learning resources and educational content based on the content of the user's dream. For example, if a user inputs "I dreamed of swimming in the ocean," related learning resources and educational content can be provided, through which the meaning of the dream can be more deeply understood. Similarly, if a user inputs "I dreamed of being surprised by big waves," related learning resources and educational content can be provided, through which the interpretation of the dream can be supplemented. In this way, the dream interpretation system can provide a deeper understanding of the meaning of the dream through related learning resources and educational content based on the content of the user's dream.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The input unit accepts the content and emotions of the user's dream. For example, the user can input the content of the dream in text format. The input unit can also record the content of the dream using voice input. Furthermore, the user can upload illustrations or sketches of the dream. Step 2: The analysis unit analyzes the dream content and emotions received by the input unit. For example, the generation AI analyzes the dream content and emotions input by the user and interprets the meaning and message of the dream. The analysis unit can also use an emotion estimation function to analyze facial expressions and tone of voice when the user inputs the dream content, automatically completing the intensity and type of emotions. Step 3: The provider provides the dream meaning and message analyzed by the analyzer. For example, the generation AI provides the user with the dream meaning and message based on the analysis results. The provider can also refer to the user's past dream interpretation results and feedback to provide a more personalized message.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit that receives the content and emotions of a user's dream; an analysis unit that analyzes the content and emotions of the dream received by the input unit; a providing unit that provides the meaning and message of the dream analyzed by the analyzing unit. A system characterized by:

2. The input unit Record the content of your dreams using voice input, The analysis unit Analyzing audio data 2. The system of claim 1.

3. The input unit Users can upload illustrations and sketches of their dreams, The analysis unit Analyzing Visual Data 2. The system of claim 1.

4. The input unit The system analyzes facial expressions and tone of voice when users input their dream content, and automatically completes the intensity and type of emotions.

2. The system of claim 1.

5. The input unit Provides an interface that allows users to select dream scenes and characters in a choice format, reducing the effort required for input.

2. The system of claim 1.

6. The input unit Add a function to refer to similar dream cases of other users to help you input.

2. The system of claim 1.

7. The input unit Providing real-time emotional feedback based on what you type to encourage typing 2. The system of claim 1.

8. The analysis unit Refer to the user's past dream data and psychological state history to provide a more accurate interpretation 2. The system of claim 1.

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