system

Abstract

The system according to the embodiment aims to help users understand the content of their dreams and interpret their meaning. [Solution] A system according to an embodiment includes a receiving unit, an analysis unit, an interpretation unit, and a providing unit. The receiving unit receives dream content from a user in text form. The analysis unit analyzes the text received by the receiving unit to analyze the dream content. The interpretation unit analyzes the meaning of the dream based on the content analyzed by the analysis unit. The providing unit provides the user with the interpretation result obtained by the interpretation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to understand the content of their dreams and interpret their meaning.

[0005] The system according to the embodiment aims to help users understand the content of their dreams and interpret their meaning. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, an interpreting unit, and a providing unit. The receiving unit receives dream content from a user in text form. The analyzing unit analyzes the text received by the receiving unit and analyzes the dream content. The interpreting unit analyzes the meaning of the dream based on the content analyzed by the analyzing unit. The providing unit provides the user with the interpretation result obtained by the interpreting unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to understand the content of their dreams and interpret their meaning. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) A dream interpretation system according to an embodiment of the present invention uses a generation AI to interpret a user's dreams. In this dream interpretation system, a user inputs the content of their dream as text, and the generation AI analyzes the text and interprets the dream. The interpretation results are provided to the user. For example, if a user inputs content such as "Last night, I had a dream about flying," this text is input into the generation AI. The generation AI then analyzes the input text, understands the content of the dream, and interprets its meaning. For example, it may interpret the dream as "Dreaming about flying represents a desire for freedom." The interpretation results are then provided to the user. For example, a message such as "Your dream represents a desire for freedom" may be displayed. In this way, the user can understand the meaning of their dream. This system allows users to easily understand the meaning of their dreams. For example, by simply inputting the content of their dream, the generation AI interprets its meaning and provides the results, making dream interpretation possible even without specialized knowledge. Furthermore, the generation AI interprets dreams based on a large amount of data, resulting in highly reliable results. Furthermore, this service can analyze the user's dream patterns and identify long-term trends. For example, analyzing the content of a user's frequent dreams and providing information on their trends can help understand the user's psychological state. In this way, the user can deepen their self-understanding through their dreams. This allows the dream interpretation system to analyze the content of the user's dreams and provide an interpretation of their meaning.

[0029] A dream interpretation system according to an embodiment includes a receiving unit, an analysis unit, an interpretation unit, and a providing unit. The receiving unit receives dream content from a user in text form. When a user inputs the content of a dream, the user may input, for example, the content of the dream they had last night. The receiving unit passes the input text to a generation AI. The analysis unit uses the generation AI to analyze the text received by the receiving unit. The analysis unit analyzes the content of the dream using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit divides the text into words using morphological analysis, analyzes the structure of the sentence using grammatical analysis, and understands the meaning of the text using semantic analysis. The interpretation unit interprets the meaning of the dream based on the content analyzed by the analysis unit. The interpretation unit performs, for example, psychological and cultural interpretations. For example, if the content of the dream is "a dream of flying," the interpretation unit interprets it as representing a desire for freedom. The providing unit provides the interpretation result obtained by the interpretation unit to the user. The providing unit displays the interpretation result to the user as a text message, for example. For example, a message such as "Your dream represents your desire for freedom" is displayed. This allows the dream interpretation system according to the embodiment to analyze the content of the user's dream, interpret its meaning, and provide it.

[0030] The analysis unit can analyze the content of dreams using natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, etc. For example, the analysis unit divides text into words using morphological analysis, analyzes the structure of sentences using grammatical analysis, and understands the meaning of the text using semantic analysis. This improves the accuracy of analyzing dream content by using natural language processing technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input text data into a generation AI, which then analyzes the text.

[0031] The interpretation unit can interpret the meaning of the dream based on the results obtained from the analysis unit. The interpretation unit performs, for example, psychological interpretation or cultural interpretation. For example, if the content of the dream is "a dream of flying," the interpretation unit interprets it as representing a desire for freedom. This allows for a more accurate interpretation by interpreting the meaning of the dream based on the analysis results. Some or all of the above-mentioned processing in the interpretation unit may be performed using, or without, the generation AI. For example, the interpretation unit can input the analysis results into the generation AI, which then interprets the meaning of the dream.

[0032] The providing unit can provide the interpretation result obtained from the interpretation unit to the user. The providing unit, for example, displays the interpretation result to the user as a text message. For example, a message such as "Your dream represents a desire for freedom" is displayed. By providing the interpretation result to the user, the user can understand the meaning of the dream. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the interpretation result to the generation AI, which then generates a message to be provided to the user.

[0033] The dream interpretation system includes a pattern analysis unit that analyzes the patterns of a user's dreams and identifies trends. The pattern analysis unit analyzes the content of the user's dreams and provides the trends. For example, the pattern analysis unit analyzes the content of dreams that the user frequently has and provides the trends. By analyzing the dream patterns, the user's long-term psychological state can be understood. Some or all of the above-described processing in the pattern analysis unit may be performed using, or without, a generation AI. For example, the pattern analysis unit can input the content of a dream into a generation AI, which then analyzes the dream pattern.

[0034] The pattern analysis unit can analyze the content of a user's dreams and provide the trends. For example, the pattern analysis unit can analyze the content of dreams that the user frequently has and provide the trends. By analyzing the content of dreams and providing the trends, the user's psychological state can be understood. Some or all of the above-mentioned processing in the pattern analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the pattern analysis unit can input the content of dreams into a generation AI, which can analyze the trends of dreams.

[0035] The dream interpretation system includes a privacy protection unit that protects the user's privacy. The privacy protection unit provides functions for protecting the user's privacy. For example, the privacy protection unit anonymizes data and restricts access. This protects the user's privacy, allowing the user to use the service with peace of mind. Some or all of the above-described processing in the privacy protection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the privacy protection unit inputs the user's data into the generation AI, which then anonymizes the data.

[0036] The reception unit can refer to the content of the user's past dreams and provide an auto-completion function when the user is entering data. For example, the reception unit can display auto-completion candidates while the user is entering data based on the content of dreams the user has entered in the past. The reception unit can also analyze the patterns of dreams the user frequently has and suggest related keywords when the user is entering data. Furthermore, the reception unit can learn the content of dreams the user has entered in the past and automatically complete similar content. This enables auto-completion when the user is entering data by referring to the content of past dreams. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input data of past dreams into a generation AI, which can then generate auto-completion candidates.

[0037] The reception unit can receive the content of a dream as a voice input and convert it into text using voice recognition technology. For example, the reception unit allows a user to input the content of a dream by voice and converts it into text using voice recognition technology. The reception unit can also remove background noise during voice input to perform accurate text conversion. Furthermore, the reception unit can improve the accuracy of text conversion by taking into account the user's pronunciation and accent during voice input. As a result, using voice input eliminates the need for text input, improving convenience. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input voice data into a generation AI, which can convert the voice data into text data.

[0038] The reception unit can receive the content of a dream as an image or picture and convert it into text using image analysis technology. For example, the reception unit allows a user to draw a picture of the content of a dream and converts it into text using image analysis technology. The reception unit can also recognize key objects when inputting an image and convert it into text. Furthermore, the reception unit can analyze a photo taken by the user and convert the content of the dream into text. By using images or pictures, text input is no longer necessary, improving convenience. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input image data into a generation AI, which then converts the image data into text data.

[0039] The reception unit can provide a region-specific dream interpretation by taking into account the user's geographical location information. For example, the reception unit can provide a dream interpretation related to the culture and customs specific to the region based on the user's location information. The reception unit can also reflect region-specific symbols and symbols in the dream interpretation by taking into account the geographical location information. Furthermore, the reception unit can provide a dream interpretation related to the history and legends of the region based on the user's location information. This makes it possible to provide a region-specific dream interpretation by taking into account the geographical location information. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input location information data into the generation AI, which can then generate a region-specific dream interpretation.

[0040] The analysis unit can improve the accuracy of the analysis by taking into account the chronological order of the dream content during analysis. For example, the analysis unit can organize the dream content in chronological order to improve the accuracy of the analysis. The analysis unit can also provide analysis results by taking into account the order of events in the dream. Furthermore, the analysis unit can analyze the chronological flow of the dream content to provide a more accurate interpretation. This improves the accuracy of the analysis by taking into account the chronological order of the dream content. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input chronological data of the dream content into the generation AI, and the generation AI can perform the analysis by taking into account the chronological order.

[0041] During analysis, the analysis unit can apply different analysis methods to different categories of dream content. For example, if the dream content is adventure-related, the analysis unit can apply a specific analysis method. Furthermore, if the dream content is fear-related, the analysis unit can apply a different analysis method. Furthermore, if the dream content is related to everyday life, the analysis unit can apply a different analysis method. In this way, by applying different analysis methods to different categories of dream content, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input category data of dream content into a generation AI, which can then apply different analysis methods to each category.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the dream content. For example, the analysis unit can improve the accuracy of the analysis by referring to academic literature related to the dream content. The analysis unit can also perform the analysis by referring to psychological research related to the dream content. Furthermore, the analysis unit can also perform the analysis by taking into account the cultural background related to the dream content. In this way, by referring to related literature, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input related literature data into the generation AI, and the generation AI can perform the analysis by referring to the literature.

[0043] During analysis, the analysis unit can analyze audio data of the dream content and integrate it with text data for analysis. For example, the analysis unit may input the dream content by voice, and analyze the audio data. The analysis unit may also integrate the audio data and text data to perform a more accurate analysis. Furthermore, the analysis unit may also reflect the emotional nuances of the audio data in the analysis. In this way, by integrating the audio data and text data, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input audio data into a generation AI, which analyzes the audio data and integrates it with text data for analysis.

[0044] During interpretation, the interpretation unit can adjust the level of detail of the interpretation based on the importance of the dream content. For example, the interpretation unit provides a detailed interpretation for important dream content. The interpretation unit can also provide a concise interpretation for general dream content. Furthermore, the interpretation unit can adjust the level of detail of the interpretation according to the importance of the dream content. In this way, by adjusting the level of detail of the interpretation according to the importance of the dream content, a detailed interpretation is provided for important content. Some or all of the above-mentioned processing in the interpretation unit may be performed using, or without, a generation AI. For example, the interpretation unit can input importance data of the dream content into the generation AI, and the generation AI can adjust the level of detail of the interpretation.

[0045] During interpretation, the interpretation unit can apply different interpretation algorithms depending on the category of the dream content. For example, if the dream content is adventure-related, the interpretation unit can apply a particular interpretation algorithm. Furthermore, if the dream content is fear-related, the interpretation unit can also apply a different interpretation algorithm. Furthermore, if the dream content is related to everyday life, the interpretation unit can apply a different interpretation algorithm. By applying different interpretation algorithms depending on the category of the dream content, a more accurate interpretation can be provided. Some or all of the above-described processing in the interpretation unit can be performed using, or without, a generation AI. For example, the interpretation unit can input category data of the dream content into the generation AI, which can then apply different interpretation algorithms for each category.

[0046] During interpretation, the interpretation unit can determine the priority of interpretations based on the time of submission of the dream content. For example, the interpretation unit can prioritize interpretation of the content of recently submitted dreams. The interpretation unit can also prioritize interpretation of the content of dreams submitted before an important event. Furthermore, the interpretation unit can prioritize interpretation of the content of dreams that the user frequently has. In this way, by determining the priority of interpretations based on the time of submission of the dream content, interpretations can be provided at more appropriate times. Some or all of the above-mentioned processing in the interpretation unit may be performed using, or without, the generation AI. For example, the interpretation unit can input data on the time of submission of the dream content into the generation AI, and the generation AI can determine the priority of interpretations.

[0047] During interpretation, the interpretation unit can adjust the order of interpretations based on the relevance of the dream content. For example, if the dream content is related, the interpretation unit provides interpretations consecutively. Also, if the dream content is different, the interpretation unit can provide interpretations by category. Furthermore, the interpretation unit can adjust the order of interpretations based on the relevance of the dream content. As a result, related content is interpreted consecutively by adjusting the order of interpretations based on the relevance of the dream content. Some or all of the above-mentioned processing in the interpretation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the interpretation unit can input relevance data of the dream content into the generation AI, and the generation AI can adjust the order of interpretations.

[0048] When providing the results, the providing unit can select the optimal display method by referring to the user's past interpretation results. The providing unit provides the interpretation results based on, for example, display methods that the user has preferred in the past. The providing unit can also analyze the user's past interpretation results and select the optimal display method. Furthermore, the providing unit can also provide a customized display method by referring to the user's past interpretation results. In this way, the optimal display method is provided by referring to the past interpretation results. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input past interpretation result data into the generation AI, which can then select the optimal display method.

[0049] The providing unit can add a function to provide the interpretation result by voice when providing it. The providing unit, for example, enables the user to receive the interpretation result by voice. When providing the interpretation result by voice, the providing unit can also provide it in a tone that matches the user's emotions. Furthermore, when providing the interpretation result by voice, the providing unit can select a voice that matches the user's preferences. In this way, providing the interpretation result by voice reduces visual burden. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input interpretation result data to a generation AI, which can generate voice data.

[0050] When providing the interpretation results, the providing unit can provide the interpretation results in an optimal format based on the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a personal computer, the providing unit can also provide a display method that includes detailed information. This allows the interpretation results to be provided in an optimal format based on the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input device information into the generation AI, which can then generate an optimal display format.

[0051] The providing unit can add a function to share the interpretation result on the user's social media when providing the interpretation result. The providing unit, for example, enables the user to easily share the interpretation result on social media. The providing unit can also enable the user to select the sharing range taking privacy settings into consideration when sharing the interpretation result. Furthermore, the providing unit can automatically generate a comment that matches the user's emotions when sharing the interpretation result. This allows information to be shared with other users by sharing the interpretation result on social media. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the interpretation result data to a generation AI, which can generate a comment to be shared.

[0052] During pattern analysis, the pattern analysis unit can predict the trend of a current dream by referring to the content of past dreams. The pattern analysis unit can predict the trend of a current dream, for example, based on the content of dreams the user has had in the past. The pattern analysis unit can also analyze the patterns of past dreams and provide the trend of a current dream. Furthermore, the pattern analysis unit can learn the content of the user's past dreams and predict the trend of a current dream. In this way, the trend of a current dream can be predicted by referring to the content of past dreams. Some or all of the above-mentioned processing in the pattern analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the pattern analysis unit can input data of past dreams into a generation AI, which can then predict the trend of a current dream.

[0053] During pattern analysis, the pattern analysis unit can apply different analysis methods to different categories of dream content. For example, if the dream content is adventure-related, the pattern analysis unit can apply a specific analysis method. Furthermore, if the dream content is fear-related, the pattern analysis unit can also apply a different analysis method. Furthermore, if the dream content is related to everyday life, the pattern analysis unit can apply a different analysis method. In this way, by applying different analysis methods to different categories of dream content, the analysis accuracy is improved. Some or all of the above-mentioned processing in the pattern analysis unit can be performed using, or without, a generation AI. For example, the pattern analysis unit can input category data of dream content into a generation AI, which can then apply different analysis methods to each category.

[0054] During pattern analysis, the pattern analysis unit can analyze changes in trends based on the time when the dream content was submitted. For example, the pattern analysis unit analyzes changes in trends based on the time when the dream content was submitted. The pattern analysis unit can also analyze seasonal trends based on the time when the dream content was submitted. Furthermore, the pattern analysis unit can analyze long-term trends taking into account the time when the dream content was submitted. In this way, by analyzing changes in trends based on the time when the dream content was submitted, long-term trends can be grasped. Some or all of the above-mentioned processing in the pattern analysis unit may be performed using, or without, a generation AI. For example, the pattern analysis unit can input data on the time when the dream content was submitted into the generation AI, which can then analyze changes in trends.

[0055] During pattern analysis, the pattern analysis unit can analyze trends by referring to market data related to the content of the dream. For example, the pattern analysis unit can analyze trends by referring to market data related to the content of the dream. The pattern analysis unit can also integrate the content of the dream with market data to perform more accurate trend analysis. Furthermore, the pattern analysis unit can analyze trends by taking into account market trends related to the content of the dream. This enables more accurate trend analysis by referring to related market data. Some or all of the above-described processing in the pattern analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the pattern analysis unit can input market data into the generation AI, which can then analyze trends by referring to the market data.

[0056] During privacy protection, the privacy protection unit can select an optimal protection method by referring to the user's past privacy settings. The privacy protection unit, for example, provides an optimal protection method based on the user's past privacy settings. The privacy protection unit can also analyze the user's past privacy settings and select an optimal protection level. Furthermore, the privacy protection unit can provide a customized protection method by referring to the user's past privacy settings. This allows optimal privacy protection to be provided by referring to the past privacy settings. Some or all of the above-described processing in the privacy protection unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the privacy protection unit can input past privacy setting data into the generation AI, which can then select an optimal protection method.

[0057] During privacy protection, the privacy protection unit can select the optimal protection method by taking into account the user's device information. For example, if the user is using a smartphone, the privacy protection unit can provide privacy protection optimized for the device. Furthermore, if the user is using a tablet, the privacy protection unit can also provide privacy protection optimized for a large screen. Furthermore, if the user is using a personal computer, the privacy protection unit can also provide privacy protection including detailed information. In this way, optimal privacy protection is provided by taking into account the user's device information. Some or all of the above-described processing in the privacy protection unit may be performed using, or without, a generation AI. For example, the privacy protection unit can input device information into the generation AI, which can then select the optimal protection method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When the user inputs the content of their dreams, the reception unit can provide input assistance by referring to the user's past input history. For example, the reception unit can display auto-completion candidates while the user is inputting based on the content of dreams previously input by the user. The reception unit can also analyze the patterns of dreams frequently seen by the user and suggest related keywords as the user inputs. Furthermore, the reception unit can learn the content of dreams previously input by the user and automatically complete similar content. This enables auto-completion during input by referring to the content of past dreams. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input data of past dreams into a generation AI, which can then generate auto-completion candidates.

[0060] When interpreting the content of a dream, the interpretation unit can improve the accuracy of the interpretation by referring to related academic literature and research data. For example, the interpretation can be performed by referring to psychological research related to the content of the dream. The interpretation can also be performed by taking into account the cultural background related to the content of the dream. Furthermore, the interpretation can be performed by referring to historical cases related to the content of the dream. In this way, by referring to related literature, the accuracy of the interpretation can be improved. Some or all of the above-mentioned processing in the interpretation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the interpretation unit can input related literature data into the generation AI, and the generation AI can perform the interpretation by referring to the literature.

[0061] When inputting the content of a user's dream, the reception unit can receive voice input and convert it into text using voice recognition technology. For example, the user can input the content of their dream by voice and convert it into text using voice recognition technology. The reception unit can also remove background noise during voice input to perform accurate text conversion. Furthermore, the reception unit can improve the accuracy of text conversion by taking into account the user's pronunciation and accent during voice input. As a result, using voice input eliminates the need for text input, improving convenience. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input voice data into a generation AI, which can convert the voice data into text data.

[0062] When analyzing the content of a dream, the analysis unit can improve the accuracy of the analysis by taking into account the chronological order. For example, the analysis unit can organize the content of the dream in chronological order to improve the accuracy of the analysis. The analysis unit can also provide analysis results by taking into account the order of events in the dream. Furthermore, the analysis unit can analyze the chronological flow of the content of the dream to provide a more accurate interpretation. This improves the accuracy of the analysis by taking into account the chronological order of the content of the dream. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input chronological data of the content of the dream into the generation AI, and the generation AI can perform the analysis by taking into account the chronological order.

[0063] When interpreting the content of a dream, the interpretation unit can apply different interpretation algorithms to each category. For example, if the content of the dream is about adventure, a specific interpretation algorithm can be applied. If the content of the dream is about fear, a different interpretation algorithm can be applied. Furthermore, if the content of the dream is about everyday life, a different interpretation algorithm can be applied. In this way, by applying different interpretation algorithms depending on the category of the dream content, a more accurate interpretation can be provided. Some or all of the above-mentioned processing in the interpretation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the interpretation unit can input category data of the content of the dream into the generation AI, which can then apply different interpretation algorithms to each category.

[0064] The providing unit can add a function to provide the interpretation result to the user by voice. For example, the providing unit can enable the user to receive the interpretation result by voice. When providing the interpretation result by voice, the providing unit can also provide it in a tone that matches the user's emotions. Furthermore, when providing the interpretation result by voice, the providing unit can select a voice that matches the user's preferences. In this way, providing the interpretation result by voice reduces the visual burden. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input interpretation result data to a generation AI, which can generate voice data.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives the user's dream content in text form. When the user inputs the content of their dream, they can enter the content of the dream they had last night in sentences, for example. The reception unit passes the input text to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the text received by the reception unit. The analysis unit analyzes the content of the dream using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit uses morphological analysis to divide the text into words, grammatical analysis to analyze the structure of the sentence, and semantic analysis to understand the meaning of the text. Step 3: The interpretation unit interprets the meaning of the dream based on the content analyzed by the analysis unit. The interpretation unit performs psychological and cultural interpretations, for example. For example, if the content of the dream is "a dream of flying in the sky," the interpretation unit may interpret it as representing a desire for freedom. Step 4: The providing unit provides the interpretation result obtained by the interpretation unit to the user. The providing unit displays the interpretation result to the user as a text message, for example. For example, a message such as "Your dream represents your desire for freedom" is displayed.

[0067] (Example 2) A dream interpretation system according to an embodiment of the present invention uses a generation AI to interpret a user's dreams. In this dream interpretation system, a user inputs the content of their dream as text, and the generation AI analyzes the text and interprets the dream. The interpretation results are provided to the user. For example, if a user inputs content such as "Last night, I had a dream about flying," this text is input into the generation AI. The generation AI then analyzes the input text, understands the content of the dream, and interprets its meaning. For example, it may interpret the dream as "Dreaming about flying represents a desire for freedom." The interpretation results are then provided to the user. For example, a message such as "Your dream represents a desire for freedom" may be displayed. In this way, the user can understand the meaning of their dream. This system allows users to easily understand the meaning of their dreams. For example, by simply inputting the content of their dream, the generation AI interprets its meaning and provides the results, making dream interpretation possible even without specialized knowledge. Furthermore, the generation AI interprets dreams based on a large amount of data, resulting in highly reliable results. Furthermore, this service can analyze the user's dream patterns and identify long-term trends. For example, analyzing the content of a user's frequent dreams and providing information on their trends can help understand the user's psychological state. In this way, the user can deepen their self-understanding through their dreams. This allows the dream interpretation system to analyze the content of the user's dreams and provide an interpretation of their meaning.

[0068] A dream interpretation system according to an embodiment includes a receiving unit, an analysis unit, an interpretation unit, and a providing unit. The receiving unit receives dream content from a user in text form. When a user inputs the content of a dream, the user may input, for example, the content of the dream they had last night. The receiving unit passes the input text to a generation AI. The analysis unit uses the generation AI to analyze the text received by the receiving unit. The analysis unit analyzes the content of the dream using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit divides the text into words using morphological analysis, analyzes the structure of the sentence using grammatical analysis, and understands the meaning of the text using semantic analysis. The interpretation unit interprets the meaning of the dream based on the content analyzed by the analysis unit. The interpretation unit performs, for example, psychological and cultural interpretations. For example, if the content of the dream is "a dream of flying," the interpretation unit interprets it as representing a desire for freedom. The providing unit provides the interpretation result obtained by the interpretation unit to the user. The providing unit displays the interpretation result to the user as a text message, for example. For example, a message such as "Your dream represents your desire for freedom" is displayed. This allows the dream interpretation system according to the embodiment to analyze the content of the user's dream, interpret its meaning, and provide it.

[0069] The analysis unit can analyze the content of dreams using natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, etc. For example, the analysis unit divides text into words using morphological analysis, analyzes the structure of sentences using grammatical analysis, and understands the meaning of the text using semantic analysis. This improves the accuracy of analyzing dream content by using natural language processing technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input text data into a generation AI, which then analyzes the text.

[0070] The interpretation unit can interpret the meaning of the dream based on the results obtained from the analysis unit. The interpretation unit performs, for example, psychological interpretation or cultural interpretation. For example, if the content of the dream is "a dream of flying," the interpretation unit interprets it as representing a desire for freedom. This allows for a more accurate interpretation by interpreting the meaning of the dream based on the analysis results. Some or all of the above-mentioned processing in the interpretation unit may be performed using, or without, the generation AI. For example, the interpretation unit can input the analysis results into the generation AI, which then interprets the meaning of the dream.

[0071] The providing unit can provide the interpretation result obtained from the interpretation unit to the user. The providing unit, for example, displays the interpretation result to the user as a text message. For example, a message such as "Your dream represents a desire for freedom" is displayed. By providing the interpretation result to the user, the user can understand the meaning of the dream. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the interpretation result to the generation AI, which then generates a message to be provided to the user.

[0072] The dream interpretation system includes a pattern analysis unit that analyzes the patterns of a user's dreams and identifies trends. The pattern analysis unit analyzes the content of the user's dreams and provides the trends. For example, the pattern analysis unit analyzes the content of dreams that the user frequently has and provides the trends. By analyzing the dream patterns, the user's long-term psychological state can be understood. Some or all of the above-described processing in the pattern analysis unit may be performed using, or without, a generation AI. For example, the pattern analysis unit can input the content of a dream into a generation AI, which then analyzes the dream pattern.

[0073] The pattern analysis unit can analyze the content of a user's dreams and provide the trends. For example, the pattern analysis unit can analyze the content of dreams that the user frequently has and provide the trends. By analyzing the content of dreams and providing the trends, the user's psychological state can be understood. Some or all of the above-mentioned processing in the pattern analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the pattern analysis unit can input the content of dreams into a generation AI, which can analyze the trends of dreams.

[0074] The dream interpretation system includes a privacy protection unit that protects the user's privacy. The privacy protection unit provides functions for protecting the user's privacy. For example, the privacy protection unit anonymizes data and restricts access. This protects the user's privacy, allowing the user to use the service with peace of mind. Some or all of the above-described processing in the privacy protection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the privacy protection unit inputs the user's data into the generation AI, which then anonymizes the data.

[0075] The reception unit can estimate the user's emotions and adjust the input method for dream content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of dream content. This allows for more appropriate input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0076] The reception unit can refer to the content of the user's past dreams and provide an auto-completion function when the user is entering data. For example, the reception unit can display auto-completion candidates while the user is entering data based on the content of dreams the user has entered in the past. The reception unit can also analyze the patterns of dreams the user frequently has and suggest related keywords when the user is entering data. Furthermore, the reception unit can learn the content of dreams the user has entered in the past and automatically complete similar content. This enables auto-completion when the user is entering data by referring to the content of past dreams. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input data of past dreams into a generation AI, which can then generate auto-completion candidates.

[0077] The reception unit can receive the content of a dream as a voice input and convert it into text using voice recognition technology. For example, the reception unit allows a user to input the content of a dream by voice and converts it into text using voice recognition technology. The reception unit can also remove background noise during voice input to perform accurate text conversion. Furthermore, the reception unit can improve the accuracy of text conversion by taking into account the user's pronunciation and accent during voice input. As a result, using voice input eliminates the need for text input, improving convenience. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input voice data into a generation AI, which can convert the voice data into text data.

[0078] The reception unit can estimate the user's emotions and prioritize the input content based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can prioritize inputting important dream content. Furthermore, if the user is relaxed, the reception unit can also prioritize inputting detailed dream content. Furthermore, if the user is in a hurry, the reception unit can also prioritize inputting only key keywords. This allows the input content to be prioritized according to the user's emotions, thereby prioritizing important content. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0079] The reception unit can receive the content of a dream as an image or picture and convert it into text using image analysis technology. For example, the reception unit allows a user to draw a picture of the content of a dream and converts it into text using image analysis technology. The reception unit can also recognize key objects when inputting an image and convert it into text. Furthermore, the reception unit can analyze a photo taken by the user and convert the content of the dream into text. By using images or pictures, text input is no longer necessary, improving convenience. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input image data into a generation AI, which then converts the image data into text data.

[0080] The reception unit can provide a region-specific dream interpretation by taking into account the user's geographical location information. For example, the reception unit can provide a dream interpretation related to the culture and customs specific to the region based on the user's location information. The reception unit can also reflect region-specific symbols and symbols in the dream interpretation by taking into account the geographical location information. Furthermore, the reception unit can provide a dream interpretation related to the history and legends of the region based on the user's location information. This makes it possible to provide a region-specific dream interpretation by taking into account the geographical location information. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input location information data into the generation AI, which can then generate a region-specific dream interpretation.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit applies an analysis algorithm that takes emotions into consideration. Furthermore, if the user is relaxed, the analysis unit can apply an algorithm that performs detailed analysis. Furthermore, if the user is excited, the analysis unit can apply an algorithm that performs quick analysis. This enables more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0082] The analysis unit can improve the accuracy of the analysis by taking into account the chronological order of the dream content during analysis. For example, the analysis unit can organize the dream content in chronological order to improve the accuracy of the analysis. The analysis unit can also provide analysis results by taking into account the order of events in the dream. Furthermore, the analysis unit can analyze the chronological flow of the dream content to provide a more accurate interpretation. This improves the accuracy of the analysis by taking into account the chronological order of the dream content. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input chronological data of the dream content into the generation AI, and the generation AI can perform the analysis by taking into account the chronological order.

[0083] During analysis, the analysis unit can apply different analysis methods to different categories of dream content. For example, if the dream content is adventure-related, the analysis unit can apply a specific analysis method. Furthermore, if the dream content is fear-related, the analysis unit can apply a different analysis method. Furthermore, if the dream content is related to everyday life, the analysis unit can apply a different analysis method. In this way, by applying different analysis methods to different categories of dream content, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input category data of dream content into a generation AI, which can then apply different analysis methods to each category.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple, reassuring display method. Furthermore, if the user is relaxed, the analysis unit can also display detailed analysis results. Furthermore, if the user is excited, the analysis unit can also provide a visually stimulating display method. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the dream content. For example, the analysis unit can improve the accuracy of the analysis by referring to academic literature related to the dream content. The analysis unit can also perform the analysis by referring to psychological research related to the dream content. Furthermore, the analysis unit can also perform the analysis by taking into account the cultural background related to the dream content. In this way, by referring to related literature, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input related literature data into the generation AI, and the generation AI can perform the analysis by referring to the literature.

[0086] During analysis, the analysis unit can analyze audio data of the dream content and integrate it with text data for analysis. For example, the analysis unit may input the dream content by voice, and analyze the audio data. The analysis unit may also integrate the audio data and text data to perform a more accurate analysis. Furthermore, the analysis unit may also reflect the emotional nuances of the audio data in the analysis. In this way, by integrating the audio data and text data, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input audio data into a generation AI, which analyzes the audio data and integrates it with text data for analysis.

[0087] The interpretation unit can estimate the user's emotions and adjust the way the interpretation is expressed based on the estimated user's emotions. For example, if the user is feeling anxious, the interpretation unit can provide an interpretation in a reassuring way. Furthermore, if the user is relaxed, the interpretation unit can provide a detailed interpretation. Furthermore, if the user is excited, the interpretation unit can provide an interpretation in a visually stimulating way. This allows for adjusting the way the interpretation is expressed according to the user's emotions, thereby providing a more appropriate interpretation. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the interpretation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the interpretation unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0088] During interpretation, the interpretation unit can adjust the level of detail of the interpretation based on the importance of the dream content. For example, the interpretation unit provides a detailed interpretation for important dream content. The interpretation unit can also provide a concise interpretation for general dream content. Furthermore, the interpretation unit can adjust the level of detail of the interpretation according to the importance of the dream content. In this way, by adjusting the level of detail of the interpretation according to the importance of the dream content, a detailed interpretation is provided for important content. Some or all of the above-mentioned processing in the interpretation unit may be performed using, or without, a generation AI. For example, the interpretation unit can input importance data of the dream content into the generation AI, and the generation AI can adjust the level of detail of the interpretation.

[0089] During interpretation, the interpretation unit can apply different interpretation algorithms depending on the category of the dream content. For example, if the dream content is adventure-related, the interpretation unit can apply a particular interpretation algorithm. Furthermore, if the dream content is fear-related, the interpretation unit can also apply a different interpretation algorithm. Furthermore, if the dream content is related to everyday life, the interpretation unit can apply a different interpretation algorithm. By applying different interpretation algorithms depending on the category of the dream content, a more accurate interpretation can be provided. Some or all of the above-described processing in the interpretation unit can be performed using, or without, a generation AI. For example, the interpretation unit can input category data of the dream content into the generation AI, which can then apply different interpretation algorithms for each category.

[0090] The interpretation unit can estimate the user's emotions and prioritize interpretation results based on the estimated user emotions. For example, if the user is feeling anxious, the interpretation unit can prioritize reassuring interpretation results. Furthermore, if the user is relaxed, the interpretation unit can prioritize detailed interpretation results. Furthermore, if the user is excited, the interpretation unit can prioritize visually stimulating interpretation results. Thus, by prioritizing interpretation results according to the user's emotions, more appropriate interpretation results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the interpretation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the interpretation unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0091] During interpretation, the interpretation unit can determine the priority of interpretations based on the time of submission of the dream content. For example, the interpretation unit can prioritize interpretation of the content of recently submitted dreams. The interpretation unit can also prioritize interpretation of the content of dreams submitted before an important event. Furthermore, the interpretation unit can prioritize interpretation of the content of dreams that the user frequently has. In this way, by determining the priority of interpretations based on the time of submission of the dream content, interpretations can be provided at more appropriate times. Some or all of the above-mentioned processing in the interpretation unit may be performed using, or without, the generation AI. For example, the interpretation unit can input data on the time of submission of the dream content into the generation AI, and the generation AI can determine the priority of interpretations.

[0092] During interpretation, the interpretation unit can adjust the order of interpretations based on the relevance of the dream content. For example, if the dream content is related, the interpretation unit provides interpretations consecutively. Also, if the dream content is different, the interpretation unit can provide interpretations by category. Furthermore, the interpretation unit can adjust the order of interpretations based on the relevance of the dream content. As a result, related content is interpreted consecutively by adjusting the order of interpretations based on the relevance of the dream content. Some or all of the above-mentioned processing in the interpretation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the interpretation unit can input relevance data of the dream content into the generation AI, and the generation AI can adjust the order of interpretations.

[0093] The providing unit can estimate the user's emotions and adjust the display method of the interpretation result based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide the interpretation result in a reassuring display method. Furthermore, if the user is relaxed, the providing unit can also display a detailed interpretation result. Furthermore, if the user is excited, the providing unit can also provide the interpretation result in a visually stimulating display method. This allows for more appropriate display by adjusting the display method of the interpretation result according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which then estimates the emotion.

[0094] When providing the results, the providing unit can select the optimal display method by referring to the user's past interpretation results. The providing unit provides the interpretation results based on, for example, display methods that the user has preferred in the past. The providing unit can also analyze the user's past interpretation results and select the optimal display method. Furthermore, the providing unit can also provide a customized display method by referring to the user's past interpretation results. In this way, the optimal display method is provided by referring to the past interpretation results. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input past interpretation result data into the generation AI, which can then select the optimal display method.

[0095] The providing unit can add a function to provide the interpretation result by voice when providing it. The providing unit, for example, enables the user to receive the interpretation result by voice. When providing the interpretation result by voice, the providing unit can also provide it in a tone that matches the user's emotions. Furthermore, when providing the interpretation result by voice, the providing unit can select a voice that matches the user's preferences. In this way, providing the interpretation result by voice reduces visual burden. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input interpretation result data to a generation AI, which can generate voice data.

[0096] The providing unit can estimate the user's emotions and adjust the display order of the interpretation results based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can prioritize displaying reassuring interpretation results. Furthermore, if the user is relaxed, the providing unit can also prioritize displaying detailed interpretation results. Furthermore, if the user is excited, the providing unit can also prioritize displaying visually stimulating interpretation results. By adjusting the display order of the interpretation results according to the user's emotions, the interpretation results are provided in a more appropriate order. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which then estimates the emotion.

[0097] When providing the interpretation results, the providing unit can provide the interpretation results in an optimal format based on the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a personal computer, the providing unit can also provide a display method that includes detailed information. This allows the interpretation results to be provided in an optimal format based on the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input device information into the generation AI, which can then generate an optimal display format.

[0098] The providing unit can add a function to share the interpretation result on the user's social media when providing the interpretation result. The providing unit, for example, enables the user to easily share the interpretation result on social media. The providing unit can also enable the user to select the sharing range taking privacy settings into consideration when sharing the interpretation result. Furthermore, the providing unit can automatically generate a comment that matches the user's emotions when sharing the interpretation result. This allows information to be shared with other users by sharing the interpretation result on social media. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the interpretation result data to a generation AI, which can generate a comment to be shared.

[0099] The pattern analysis unit can estimate the user's emotions and adjust the pattern analysis method based on the estimated user emotions. For example, if the user is feeling anxious, the pattern analysis unit can perform pattern analysis that takes emotions into consideration. Furthermore, if the user is relaxed, the pattern analysis unit can also perform detailed pattern analysis. Furthermore, if the user is excited, the pattern analysis unit can also perform rapid pattern analysis. This allows for more appropriate analysis by adjusting the pattern analysis method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the pattern analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the pattern analysis unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0100] During pattern analysis, the pattern analysis unit can predict the trend of a current dream by referring to the content of past dreams. The pattern analysis unit can predict the trend of a current dream, for example, based on the content of dreams the user has had in the past. The pattern analysis unit can also analyze the patterns of past dreams and provide the trend of a current dream. Furthermore, the pattern analysis unit can learn the content of the user's past dreams and predict the trend of a current dream. In this way, the trend of a current dream can be predicted by referring to the content of past dreams. Some or all of the above-mentioned processing in the pattern analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the pattern analysis unit can input data of past dreams into a generation AI, which can then predict the trend of a current dream.

[0101] During pattern analysis, the pattern analysis unit can apply different analysis methods to different categories of dream content. For example, if the dream content is adventure-related, the pattern analysis unit can apply a specific analysis method. Furthermore, if the dream content is fear-related, the pattern analysis unit can also apply a different analysis method. Furthermore, if the dream content is related to everyday life, the pattern analysis unit can apply a different analysis method. In this way, by applying different analysis methods to different categories of dream content, the analysis accuracy is improved. Some or all of the above-mentioned processing in the pattern analysis unit can be performed using, or without, a generation AI. For example, the pattern analysis unit can input category data of dream content into a generation AI, which can then apply different analysis methods to each category.

[0102] The pattern analysis unit can estimate the user's emotions and adjust the display method of the pattern analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the pattern analysis unit can provide the pattern analysis results in a reassuring display method. Furthermore, if the user is relaxed, the pattern analysis unit can also display detailed pattern analysis results. Furthermore, if the user is excited, the pattern analysis unit can also provide the pattern analysis results in a visually stimulating display method. This allows for more appropriate display by adjusting the display method of the pattern analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the pattern analysis unit can be performed using, for example, the generation AI. For example, the pattern analysis unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0103] During pattern analysis, the pattern analysis unit can analyze changes in trends based on the time when the dream content was submitted. For example, the pattern analysis unit analyzes changes in trends based on the time when the dream content was submitted. The pattern analysis unit can also analyze seasonal trends based on the time when the dream content was submitted. Furthermore, the pattern analysis unit can analyze long-term trends taking into account the time when the dream content was submitted. In this way, by analyzing changes in trends based on the time when the dream content was submitted, long-term trends can be grasped. Some or all of the above-mentioned processing in the pattern analysis unit may be performed using, or without, a generation AI. For example, the pattern analysis unit can input data on the time when the dream content was submitted into the generation AI, which can then analyze changes in trends.

[0104] During pattern analysis, the pattern analysis unit can analyze trends by referring to market data related to the content of the dream. For example, the pattern analysis unit can analyze trends by referring to market data related to the content of the dream. The pattern analysis unit can also integrate the content of the dream with market data to perform more accurate trend analysis. Furthermore, the pattern analysis unit can analyze trends by taking into account market trends related to the content of the dream. This enables more accurate trend analysis by referring to related market data. Some or all of the above-described processing in the pattern analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the pattern analysis unit can input market data into the generation AI, which can then analyze trends by referring to the market data.

[0105] The privacy protection unit can estimate the user's emotions and adjust the level of privacy protection based on the estimated user's emotions. For example, the privacy protection unit can increase the level of privacy protection when the user feels anxious. The privacy protection unit can also provide standard privacy protection when the user feels relaxed. Furthermore, the privacy protection unit can adjust the level of privacy protection when the user feels excited. This provides more appropriate protection by adjusting the level of privacy protection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the privacy protection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the privacy protection unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0106] During privacy protection, the privacy protection unit can select an optimal protection method by referring to the user's past privacy settings. The privacy protection unit, for example, provides an optimal protection method based on the user's past privacy settings. The privacy protection unit can also analyze the user's past privacy settings and select an optimal protection level. Furthermore, the privacy protection unit can provide a customized protection method by referring to the user's past privacy settings. This allows optimal privacy protection to be provided by referring to the past privacy settings. Some or all of the above-described processing in the privacy protection unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the privacy protection unit can input past privacy setting data into the generation AI, which can then select an optimal protection method.

[0107] The privacy protection unit can estimate the user's emotions and determine the priority of privacy protection based on the estimated user's emotions. For example, if the user is feeling anxious, the privacy protection unit can prioritize privacy protection. Furthermore, if the user is relaxed, the privacy protection unit can also provide standard privacy protection. Furthermore, if the user is excited, the privacy protection unit can adjust the priority of privacy protection. This provides more appropriate privacy protection by determining the priority of privacy protection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the privacy protection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the privacy protection unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0108] During privacy protection, the privacy protection unit can select the optimal protection method by taking into account the user's device information. For example, if the user is using a smartphone, the privacy protection unit can provide privacy protection optimized for the device. Furthermore, if the user is using a tablet, the privacy protection unit can also provide privacy protection optimized for a large screen. Furthermore, if the user is using a personal computer, the privacy protection unit can also provide privacy protection including detailed information. In this way, optimal privacy protection is provided by taking into account the user's device information. Some or all of the above-described processing in the privacy protection unit may be performed using, or without, a generation AI. For example, the privacy protection unit can input device information into the generation AI, which can then select the optimal protection method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, interpretation unit, provision unit, pattern analysis unit, and privacy protection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives the content of a dream from a user in text form. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the dream using a generation AI. The interpretation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and interprets the meaning of the dream based on the analyzed content. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the interpretation result to the user. The pattern analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's dream pattern. The privacy protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the user's privacy. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, interpretation unit, provision unit, pattern analysis unit, and privacy protection unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives the content of a dream from a user in text form. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the dream using a generative AI. The interpretation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and interprets the meaning of the dream based on the analyzed content. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the interpretation result to the user. The pattern analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's dream pattern. The privacy protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the user's privacy. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, interpretation unit, provision unit, pattern analysis unit, and privacy protection unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives the content of a dream from a user in text form. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the dream using a generation AI. The interpretation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and interprets the meaning of the dream based on the analyzed content. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the interpretation result to the user. The pattern analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's dream pattern. The privacy protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the user's privacy. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, interpretation unit, provision unit, pattern analysis unit, and privacy protection unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives the content of a dream from a user in text form. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the dream using a generative AI. The interpretation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and interprets the meaning of the dream based on the analyzed content. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the interpretation result to the user. The pattern analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's dream pattern. The privacy protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the user's privacy.

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

[0110] When the user inputs the content of their dreams, the reception unit can provide input assistance by referring to the user's past input history. For example, the reception unit can display auto-completion candidates while the user is inputting based on the content of dreams previously input by the user. The reception unit can also analyze the patterns of dreams frequently seen by the user and suggest related keywords as the user inputs. Furthermore, the reception unit can learn the content of dreams previously input by the user and automatically complete similar content. This enables auto-completion during input by referring to the content of past dreams. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input data of past dreams into a generation AI, which can then generate auto-completion candidates.

[0111] When analyzing the content of a dream, the analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is feeling anxious, an analysis algorithm that takes emotions into consideration can be applied. Furthermore, if the user is relaxed, an algorithm that performs detailed analysis can be applied. Furthermore, if the user is excited, an algorithm that performs quick analysis can be applied. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0112] When interpreting the content of a dream, the interpretation unit can improve the accuracy of the interpretation by referring to related academic literature and research data. For example, the interpretation can be performed by referring to psychological research related to the content of the dream. The interpretation can also be performed by taking into account the cultural background related to the content of the dream. Furthermore, the interpretation can be performed by referring to historical cases related to the content of the dream. In this way, by referring to related literature, the accuracy of the interpretation can be improved. Some or all of the above-mentioned processing in the interpretation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the interpretation unit can input related literature data into the generation AI, and the generation AI can perform the interpretation by referring to the literature.

[0113] When providing the interpretation result to the user, the providing unit can estimate the user's emotion and adjust the display method based on the estimated emotion. For example, if the user is feeling anxious, the interpretation result can be provided in a reassuring display method. Furthermore, if the user is relaxed, the interpretation result can be displayed in a detailed manner. Furthermore, if the user is excited, the interpretation result can be provided in a visually stimulating display method. This allows for more appropriate display by adjusting the display method of the interpretation result according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which then estimates the emotion.

[0114] When analyzing the user's dream patterns, the pattern analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling anxious, the pattern analysis can be performed taking the user's emotions into consideration. Furthermore, if the user is relaxed, detailed pattern analysis can be performed. Furthermore, if the user is excited, rapid pattern analysis can be performed. This allows for more appropriate analysis by adjusting the pattern analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the pattern analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the pattern analysis unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0115] When inputting the content of a user's dream, the reception unit can receive voice input and convert it into text using voice recognition technology. For example, the user can input the content of their dream by voice and convert it into text using voice recognition technology. The reception unit can also remove background noise during voice input to perform accurate text conversion. Furthermore, the reception unit can improve the accuracy of text conversion by taking into account the user's pronunciation and accent during voice input. As a result, using voice input eliminates the need for text input, improving convenience. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input voice data into a generation AI, which can convert the voice data into text data.

[0116] When analyzing the content of a dream, the analysis unit can improve the accuracy of the analysis by taking into account the chronological order. For example, the analysis unit can organize the content of the dream in chronological order to improve the accuracy of the analysis. The analysis unit can also provide analysis results by taking into account the order of events in the dream. Furthermore, the analysis unit can analyze the chronological flow of the content of the dream to provide a more accurate interpretation. This improves the accuracy of the analysis by taking into account the chronological order of the content of the dream. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input chronological data of the content of the dream into the generation AI, and the generation AI can perform the analysis by taking into account the chronological order.

[0117] When interpreting the content of a dream, the interpretation unit can apply different interpretation algorithms to each category. For example, if the content of the dream is about adventure, a specific interpretation algorithm can be applied. If the content of the dream is about fear, a different interpretation algorithm can be applied. Furthermore, if the content of the dream is about everyday life, a different interpretation algorithm can be applied. In this way, by applying different interpretation algorithms depending on the category of the dream content, a more accurate interpretation can be provided. Some or all of the above-mentioned processing in the interpretation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the interpretation unit can input category data of the content of the dream into the generation AI, which can then apply different interpretation algorithms to each category.

[0118] The providing unit can add a function to provide the interpretation result to the user by voice. For example, the providing unit can enable the user to receive the interpretation result by voice. When providing the interpretation result by voice, the providing unit can also provide it in a tone that matches the user's emotions. Furthermore, when providing the interpretation result by voice, the providing unit can select a voice that matches the user's preferences. In this way, providing the interpretation result by voice reduces the visual burden. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input interpretation result data to a generation AI, which can generate voice data.

[0119] The privacy protection unit can estimate the user's emotions and adjust the level of privacy protection based on the estimated emotions. For example, if the user feels anxious, the level of privacy protection can be increased. Also, if the user feels relaxed, standard privacy protection can be provided. Furthermore, if the user feels excited, the level of privacy protection can be adjusted. Thus, by adjusting the level of privacy protection according to the user's emotions, more appropriate protection can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the privacy protection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the privacy protection unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The reception unit receives the user's dream content in text form. When the user inputs the content of their dream, they can enter the content of the dream they had last night in sentences, for example. The reception unit passes the input text to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the text received by the reception unit. The analysis unit analyzes the content of the dream using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit uses morphological analysis to divide the text into words, grammatical analysis to analyze the structure of the sentence, and semantic analysis to understand the meaning of the text. Step 3: The interpretation unit interprets the meaning of the dream based on the content analyzed by the analysis unit. The interpretation unit performs psychological and cultural interpretations, for example. For example, if the content of the dream is "a dream of flying in the sky," the interpretation unit may interpret it as representing a desire for freedom. Step 4: The providing unit provides the interpretation result obtained by the interpretation unit to the user. The providing unit displays the interpretation result to the user as a text message, for example. For example, a message such as "Your dream represents your desire for freedom" is displayed.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0159] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] 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.

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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, in order to avoid confusion and to 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives the content of a dream from a user in text form; an analysis unit that analyzes the text received by the reception unit and analyzes the content of the dream; An interpretation unit that analyzes the meaning of the dream based on the content analyzed by the analysis unit; a providing unit that provides a user with an interpretation result obtained by the interpretation unit; Equipped with A system characterized by:

2. The analysis unit Analyzing dream content using natural language processing technology 2. The system of claim 1.

3. The interpretation unit Interpreting the meaning of the dream based on the results obtained from the analysis unit 2. The system of claim 1.

4. The providing unit The interpretation result obtained from the interpretation unit is provided to the user.

2. The system of claim 1.

5. Equipped with a pattern analysis unit that analyzes the user's dream patterns and identifies trends 2. The system of claim 1.

6. The pattern analysis unit Analyze the content of the user's dreams and provide trends 6. The system of claim 5.

7. Equipped with a privacy protection unit that protects user privacy 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and adjust the method of inputting dream content based on the estimated user emotions.

2. The system of claim 1.

9. The reception unit It references the user's past dreams and provides auto-completion functionality as they type.

2. The system of claim 1.

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