system

Abstract

The system according to this embodiment aims to provide mnemonics for efficiently memorizing strings of characters or numbers that are difficult to remember. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input of a string of characters to be memorized. The generation unit analyzes the string of characters received by the reception unit and generates a mnemonic. The provision unit provides the mnemonic generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional technologies do not sufficiently provide an efficient way to memorize difficult-to-remember character strings and numbers, leaving room for improvement.

[0005] The system according to an embodiment aims to provide a mnemonic for efficiently memorizing difficult-to-remember character strings and numbers.

Means for Solving the Problems

[0006] The system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives an input of a character string to be memorized. The generation unit analyzes the character string received by the reception unit and generates a mnemonic. The provision unit provides the mnemonic generated by the generation unit to the user.

Effects of the Invention

[0007] The system according to this embodiment can provide mnemonics for efficiently memorizing strings of characters or numbers that are difficult to remember. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The memorable string generation system according to an embodiment of the present invention is a system for making difficult-to-remember numbers and strings easier to remember using "wordplay," a uniquely Japanese cultural practice. This system begins with the user inputting a string they want to remember (for example, a historical year, a phone number, a PIN, a bank account number, pi, element symbols, a password, etc.). Next, the generation AI analyzes the input string and generates a wordplay that is optimal for the user. In this process, information such as the user's age group, gender, season / era, and hobbies is also taken into consideration. For example, if the user wants to remember the year 794, wordplays such as "Nakuyo (794) Uguisu Heiankyo" (The nightingale sings in Heiankyo) or "Nakushi (794) nakenai Heiankyo" (Don't lose Heiankyo) are generated. The generated wordplays are not only provided to the user but can also be published on a platform called the "Wordplay Sharing Platform." This allows users to share them with other users and use them as a communication tool. In addition, interesting wordplays are published as "Public Wordplays" and displayed in a ranking format. This system makes it easy to remember difficult-to-remember strings and can be greatly utilized in educational settings. In particular, it is expected to become a standard service for junior and senior high school students, and by generating many trending words, it is anticipated to be actively used as a communication tool. This means that the easy-to-remember string generation system can make difficult-to-remember strings easier to remember through mnemonics.

[0029] The memorable string generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input of a string to be memorized. Examples of strings to be memorized include, but are not limited to, historical dates, telephone numbers, PINs, bank account numbers, pi, element symbols, and passwords. The reception unit receives the string entered by the user in digital format, for example. The reception unit can also receive strings using voice input. For example, it converts a string entered by the user by voice into text data using speech recognition technology. The generation unit uses a generation AI to analyze the string received by the reception unit and generate a mnemonic. The generation unit analyzes the string using, for example, string pattern recognition technology. The generation unit can also analyze the string using natural language processing technology. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the string and generate a mnemonic. The generation unit can also generate mnemonics considering information such as the user's age group, gender, season / era, and hobbies. For example, the generation unit generates mnemonics for young people and mnemonics for the elderly according to the user's age group. The provision unit provides the mnemonics generated by the generation unit to the user. The provision unit can, for example, display the generated mnemonics on the user's device. The provision unit can also publish the generated mnemonics on a "mnemonic sharing platform". For example, the provision unit can share the generated mnemonics on the platform and share them with other users. The provision unit can also display the generated mnemonics in a ranking format. For example, the provision unit can display popular mnemonics in a ranking format so that users can check them. In this way, the easy-to-remember string generation system according to the embodiment can make difficult-to-remember strings easier to remember with mnemonics. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input a string entered by the user into the generation AI and have the generation AI perform the generation of mnemonics. Some or all of the above processing in the provision unit may be performed using, for example, an AI, or without an AI.For example, the service provider can use an AI model to provide users with the generated mnemonics.

[0030] The reception desk accepts input of strings that users wish to remember. These strings may include, but are not limited to, historical dates, phone numbers, PINs, bank account numbers, pi, element symbols, and passwords. The reception desk accepts user input in digital format. Specifically, when a user inputs a string using a keyboard or touchscreen, the input data is immediately captured by the system. The reception desk can also accept strings using voice input. For example, it converts a user's voice input into text data using speech recognition technology. Speech recognition technology involves analyzing the speech waveform and breaking it down into phonemes and words. This ensures that what the user speaks is accurately captured as text data by the system. Furthermore, the reception desk can refer to the user's input history and past data to complete or correct inputs. For example, if content similar to previously entered strings is entered, the system automatically suggests completion candidates to help the user complete input efficiently. This allows the reception desk to support diverse input methods and provide an environment where users can easily input strings.

[0031] The generation unit uses a generation AI to analyze the string received by the reception unit and generate mnemonics. The generation unit analyzes the string using, for example, string pattern recognition technology. Specifically, it detects patterns of numbers and characters contained within the string and generates mnemonic candidates based on them. The generation unit can also analyze the string using natural language processing technology. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the string and generate mnemonics. The generation AI receives the input string as a prompt and executes an algorithm to generate mnemonics related to that string. The generation unit can also generate mnemonics considering information such as the user's age group, gender, season / era, and hobbies. For example, the generation unit can generate mnemonics for young people and mnemonics for older people depending on the user's age group. Mnemonics for young people might incorporate trendy words and catchphrases, while mnemonics for older people might use nostalgic words and concise expressions. Furthermore, the generation unit can provide customized mnemonics based on the user's hobbies and interests. For example, for a user who likes sports, the system generates mnemonics related to sports, and for a user who likes music, it generates mnemonics related to music. This allows the generation unit to provide mnemonics tailored to the user's individual needs, improving memorability.

[0032] The provider unit provides users with mnemonics generated by the generator unit. The provider unit can, for example, display the generated mnemonics on the user's device. Specifically, it can display the generated mnemonics as notifications or pop-up messages on devices such as smartphones, tablets, and personal computers. The provider unit can also publish the generated mnemonics on a "mnemonic sharing platform." For example, the provider unit can share the generated mnemonics on the platform and share them with other users. On the sharing platform, users can rate the generated mnemonics and leave comments. This allows other users to refer to them and find better mnemonics. The provider unit can also display the generated mnemonics in a ranking format. For example, the provider unit can display popular mnemonics in a ranking format for users to check. The ranking is updated based on user ratings and frequency of use, always providing the latest information. Furthermore, the provider unit can collect user feedback and provide it to the generator unit, continuously improving the quality of the mnemonics. This allows the provider unit to provide users with effective and engaging mnemonics, improving their memorability.

[0033] The generation unit can generate mnemonics by considering information such as the user's age group, gender, season / era, and hobbies. For example, the generation unit can generate mnemonics for young people or for the elderly depending on the user's age group. The generation unit can also generate mnemonics for men or for women depending on the user's gender. The generation unit can also generate mnemonics that have a seasonal feel or mnemonics that reflect the historical context depending on the season or era. The generation unit can also generate mnemonics related to the user's hobbies depending on the user's hobbies. In this way, the generation unit can provide the user with the most suitable mnemonics. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information such as the user's age group, gender, season / era, and hobbies into a generation AI and have the generation AI perform the generation of mnemonics.

[0034] The provider can publish the generated mnemonics on a "mnemonic sharing platform." For example, the provider can share the generated mnemonics on the platform and share them with other users. For example, the provider can provide a user registration function so that users can publish their own mnemonics. For example, the provider can provide a search function so that users can search for other mnemonics. For example, the provider can provide a rating function so that users can rate other mnemonics. In this way, the provider can share mnemonics with other users. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can publish the generated mnemonics on the platform using an AI model.

[0035] The service provider can display the generated mnemonics in a ranking format. For example, the service provider can display popular mnemonics in a ranking format for users to review. The service provider can also display mnemonics in a ranking format based on evaluation scores, for example. The service provider can also display mnemonics in a ranking format based on frequency of use, for example. This allows the service provider to review popular mnemonics in a ranking format. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can display the generated mnemonics in a ranking format using an AI model.

[0036] The reception unit can accept strings such as historical dates, telephone numbers, PINs, bank account numbers, pi, element symbols, and passwords. For example, the reception unit can accept historical dates entered by the user. The reception unit can also accept telephone numbers entered by the user. The reception unit can also accept PINs entered by the user. The reception unit can also accept bank account numbers entered by the user. The reception unit can also accept pi entered by the user. The reception unit can also accept element symbols entered by the user. The reception unit can also accept passwords entered by the user. This allows the reception unit to accept a wide variety of strings. Some or all of the processing described above in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can accept strings entered by the user using an AI model.

[0037] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can automatically display strings that the user has frequently entered in the past as suggestions. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest strings that the user will use during a specific time period based on the user's past input history. In this way, the reception unit can provide the user with the optimal input method. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past input history into an AI model and have the AI ​​model select the optimal input method.

[0038] The input field can filter the input string based on the user's current learning status and areas of interest. For example, the input field may prioritize displaying strings related to the subject the user is currently studying. The input field may also filter and display relevant strings based on the user's areas of interest. The input field may also suggest strings of appropriate difficulty level according to the user's learning progress. In this way, the input field can provide strings that are tailored to the user's learning status and areas of interest. Some or all of the above processing in the input field may be performed using AI, for example, or not. For example, the input field may input data on the user's learning status and areas of interest into an AI model and have the AI ​​model perform the filtering.

[0039] The reception unit can prioritize input of highly relevant strings when a user enters text, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize displaying strings related to that region. For example, if the user is traveling, the reception unit can also prioritize displaying strings related to their travel destination. For example, if the user is at home, the reception unit can also prioritize displaying strings related to their home. In this way, the reception unit can provide strings based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into an AI model and have the AI ​​model select highly relevant strings.

[0040] The reception unit can analyze the user's social media activity when a string is entered and input relevant strings. For example, the reception unit can prioritize displaying strings that the user frequently uses on social media. The reception unit can also suggest relevant strings based on the content of the user's social media posts. The reception unit can also suggest strings based on the strings used by the user's social media followers. In this way, the reception unit can provide strings based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into an AI model and have the AI ​​model select relevant strings.

[0041] The generation unit can adjust the level of detail of mnemonics based on the importance of the string when generating them. For example, the generation unit generates detailed mnemonics for important strings. For example, the generation unit can also generate concise mnemonics for less important strings. For example, the generation unit can generate special mnemonics for strings that the user has specifically requested to remember. In this way, the generation unit can provide mnemonics according to the importance of the strings. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input string importance data into a generation AI and have the generation AI adjust the level of detail of the mnemonics.

[0042] The generation unit can apply different generation algorithms depending on the category of the string when generating mnemonics. For example, in the case of historical dates, the generation unit can generate mnemonics related to historical events. For example, in the case of phone numbers, the generation unit can also generate mnemonics based on the sequence of numbers. For example, in the case of passwords, the generation unit can also generate mnemonics that take security into consideration. In this way, the generation unit can provide mnemonics according to the category of the string. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input string category data into a generation AI and cause the generation AI to execute the application of different generation algorithms.

[0043] The generation unit can determine the priority of mnemonics based on the submission date of the string when generating mnemonics. For example, the generation unit will prioritize generating mnemonics when the deadline is approaching. The generation unit can also postpone generating mnemonics when the submission date is far away. For example, if the submission date is unknown, the generation unit can determine the priority by considering other factors. This allows the generation unit to provide mnemonics appropriate to the submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input string submission date data into a generation AI and have the generation AI determine the priority of mnemonics.

[0044] The generation unit can adjust the order of mnemonics based on the relevance of the strings when generating them. For example, the generation unit can prioritize incorporating highly relevant strings into the mnemonics. The generation unit can also, for example, postpone incorporating less relevant strings into the mnemonics. The generation unit can also, for example, group related strings and incorporate them into the mnemonics. In this way, the generation unit can provide mnemonics according to their relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input string relevance data into a generation AI and have the generation AI perform the adjustment of the mnemonic order.

[0045] The service provider can select the optimal display method by referring to the user's past usage history when providing mnemonics. For example, the service provider can prioritize providing display methods that the user has preferred to use in the past. For example, the service provider can also suggest the optimal display method based on the user's past usage history. For example, the service provider can analyze the user's past usage history and provide a display method with high visibility. In this way, the service provider can provide a display method based on past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past usage history data into an AI model and have the AI ​​model select the optimal display method.

[0046] The service provider can customize the displayed content based on the user's current learning status when providing mnemonics. For example, the service provider can prioritize displaying mnemonics related to the subject the user is currently studying. The service provider can also provide mnemonics of appropriate difficulty level according to the user's learning progress. The service provider can also customize and display relevant mnemonics based on the user's learning status. This allows the service provider to provide displayed content that is appropriate to the learning status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's learning status data into an AI model and have the AI ​​model perform the customization of the displayed content.

[0047] The service provider can select the optimal display method when providing mnemonics, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize displaying mnemonics related to that region. For example, if the user is traveling, the service provider can prioritize displaying mnemonics related to the travel destination. For example, if the user is at home, the service provider can prioritize displaying mnemonics related to home. In this way, the service provider can provide a display method based on geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into an AI model and have the AI ​​model select the optimal display method.

[0048] The service provider can analyze the user's social media activity and customize the displayed content when providing mnemonics. For example, the service provider can prioritize displaying mnemonics that the user frequently uses on social media. The service provider can also suggest relevant mnemonics based on the user's social media posts. The service provider can also suggest mnemonics based on the mnemonics used by the user's social media followers. This allows the service provider to provide displayed content based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into an AI model and have the AI ​​model perform the customization of the displayed content.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The reception desk can provide real-time feedback on the string entered by the user. For example, if the string entered by the user is incomplete, the reception desk can immediately suggest corrections. Furthermore, the reception desk can also provide relevant information and hints for the string entered by the user. For example, if the user enters a historical date, the reception desk can display information about events and people associated with that date. This allows the reception desk to support the user's input and promote the entry of more accurate strings. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's input data into an AI model and have the AI ​​model perform the provision of real-time feedback.

[0051] The service provider can include a function to notify the user's device of the generated mnemonics. For example, it can send the generated mnemonics via push notification at a time set by the user. Furthermore, the service provider can also link with the user's calendar or schedule to remind the user of mnemonics before important events or exams. This allows the service provider to support the user in efficiently memorizing mnemonics. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's schedule data into an AI model and have the AI ​​model execute the timing of notifications.

[0052] The service provider can display the generated mnemonics in a visually appealing format. For example, it can use animations or illustrations to display the mnemonics, making them easier for users to remember. Furthermore, the service provider can also include a function that allows users to select customizable themes and fonts. This allows the service provider to provide a display method that suits the user's preferences, making mnemonic learning more enjoyable. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user customization data into an AI model and have the AI ​​model select a visual display method.

[0053] The reception unit can provide voice feedback for the text entered by the user. For example, it can read aloud the text entered by the user to prompt confirmation. Furthermore, if the user uses voice input, the reception unit can analyze the voice and provide appropriate feedback. In this way, the reception unit can support user input by providing feedback using not only visual but also auditory senses. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's voice data into an AI model and have the AI ​​model perform the provision of voice feedback.

[0054] The service provider can deliver the generated mnemonics in stages according to the user's learning progress. For example, it can provide basic mnemonics to users learning for the first time and increase the difficulty as they progress. Furthermore, the service provider can analyze the user's learning history and provide review mnemonics at the optimal timing. In this way, the service provider can provide support to maximize the user's learning effectiveness. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's learning progress data into an AI model and have the AI ​​model perform the staged delivery.

[0055] The reception unit can provide relevant visual content in response to text entered by the user. For example, if the user enters a historical date, it can display images or videos related to that date. Furthermore, the reception unit can also provide interactive content in response to text entered by the user. For example, if the user enters a chemical element symbol, it can display an interactive graph showing the properties and uses of that element. This allows the reception unit to deepen the user's understanding through visual information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user input data into an AI model and have the AI ​​model provide the relevant visual content.

[0056] The service provider may include a function to save the generated mnemonics to the user's device. For example, the user could save their favorite mnemonics as favorites for easy access later. Furthermore, the service provider may also provide a function to share the generated mnemonics with other applications or devices. This allows the service provider to support the user in efficiently managing and utilizing mnemonics. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider could input the user's saved data into an AI model and have the AI ​​model perform the saving and sharing functions.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk accepts input of a string of characters to be memorized. Examples of strings to be memorized include, but are not limited to, historical dates, phone numbers, PINs, bank account numbers, pi, element symbols, and passwords. The reception desk accepts the user's input in digital format, for example. The reception desk can also accept strings using voice input. For example, it can convert a user's voice input into text data using speech recognition technology. Step 2: The generation unit uses a generation AI to analyze the string received by the reception unit and generate a mnemonic. The generation unit analyzes the string using, for example, string pattern recognition technology or natural language processing technology. The generation unit can also generate mnemonics considering information such as the user's age group, gender, season / era, and hobbies. For example, the generation unit can generate mnemonics for young people or for the elderly depending on the user's age group. Step 3: The provider provides the user with the mnemonics generated by the generator. The provider, for example, displays the generated mnemonics on the user's device. The provider can also publish the generated mnemonics on a "mnemonic sharing platform." For example, the provider shares the generated mnemonics on the platform and shares them with other users. The provider can also display the generated mnemonics in a ranking format. For example, the provider displays popular mnemonics in a ranking format for users to check.

[0059] (Example of form 2) The memorable string generation system according to an embodiment of the present invention is a system for making difficult-to-remember numbers and strings easier to remember using "wordplay," a uniquely Japanese cultural practice. This system begins with the user inputting a string they want to remember (for example, a historical year, a phone number, a PIN, a bank account number, pi, element symbols, a password, etc.). Next, the generation AI analyzes the input string and generates a wordplay that is optimal for the user. In this process, information such as the user's age group, gender, season / era, and hobbies is also taken into consideration. For example, if the user wants to remember the year 794, wordplays such as "Nakuyo (794) Uguisu Heiankyo" (The nightingale sings in Heiankyo) or "Nakushi (794) nakenai Heiankyo" (Don't lose Heiankyo) are generated. The generated wordplays are not only provided to the user but can also be published on a platform called the "Wordplay Sharing Platform." This allows users to share them with other users and use them as a communication tool. In addition, interesting wordplays are published as "Public Wordplays" and displayed in a ranking format. This system makes it easy to remember difficult-to-remember strings and can be greatly utilized in educational settings. In particular, it is expected to become a standard service for junior and senior high school students, and by generating many trending words, it is anticipated to be actively used as a communication tool. This means that the easy-to-remember string generation system can make difficult-to-remember strings easier to remember through mnemonics.

[0060] The memorable string generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input of a string to be memorized. Examples of strings to be memorized include, but are not limited to, historical dates, telephone numbers, PINs, bank account numbers, pi, element symbols, and passwords. The reception unit receives the string entered by the user in digital format, for example. The reception unit can also receive strings using voice input. For example, it converts a string entered by the user by voice into text data using speech recognition technology. The generation unit uses a generation AI to analyze the string received by the reception unit and generate a mnemonic. The generation unit analyzes the string using, for example, string pattern recognition technology. The generation unit can also analyze the string using natural language processing technology. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the string and generate a mnemonic. The generation unit can also generate mnemonics considering information such as the user's age group, gender, season / era, and hobbies. For example, the generation unit generates mnemonics for young people and mnemonics for the elderly according to the user's age group. The provision unit provides the mnemonics generated by the generation unit to the user. The provision unit can, for example, display the generated mnemonics on the user's device. The provision unit can also publish the generated mnemonics on a "mnemonic sharing platform". For example, the provision unit can share the generated mnemonics on the platform and share them with other users. The provision unit can also display the generated mnemonics in a ranking format. For example, the provision unit can display popular mnemonics in a ranking format so that users can check them. In this way, the easy-to-remember string generation system according to the embodiment can make difficult-to-remember strings easier to remember with mnemonics. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input a string entered by the user into the generation AI and have the generation AI perform the generation of mnemonics. Some or all of the above processing in the provision unit may be performed using, for example, an AI, or without an AI.For example, the service provider can use an AI model to provide users with the generated mnemonics.

[0061] The reception desk accepts input of strings that users wish to remember. These strings may include, but are not limited to, historical dates, phone numbers, PINs, bank account numbers, pi, element symbols, and passwords. The reception desk accepts user input in digital format. Specifically, when a user inputs a string using a keyboard or touchscreen, the input data is immediately captured by the system. The reception desk can also accept strings using voice input. For example, it converts a user's voice input into text data using speech recognition technology. Speech recognition technology involves analyzing the speech waveform and breaking it down into phonemes and words. This ensures that what the user speaks is accurately captured as text data by the system. Furthermore, the reception desk can refer to the user's input history and past data to complete or correct inputs. For example, if content similar to previously entered strings is entered, the system automatically suggests completion candidates to help the user complete input efficiently. This allows the reception desk to support diverse input methods and provide an environment where users can easily input strings.

[0062] The generation unit uses a generation AI to analyze the string received by the reception unit and generate mnemonics. The generation unit analyzes the string using, for example, string pattern recognition technology. Specifically, it detects patterns of numbers and characters contained within the string and generates mnemonic candidates based on them. The generation unit can also analyze the string using natural language processing technology. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the string and generate mnemonics. The generation AI receives the input string as a prompt and executes an algorithm to generate mnemonics related to that string. The generation unit can also generate mnemonics considering information such as the user's age group, gender, season / era, and hobbies. For example, the generation unit can generate mnemonics for young people and mnemonics for older people depending on the user's age group. Mnemonics for young people might incorporate trendy words and catchphrases, while mnemonics for older people might use nostalgic words and concise expressions. Furthermore, the generation unit can provide customized mnemonics based on the user's hobbies and interests. For example, for a user who likes sports, the system generates mnemonics related to sports, and for a user who likes music, it generates mnemonics related to music. This allows the generation unit to provide mnemonics tailored to the user's individual needs, improving memorability.

[0063] The provider unit provides users with mnemonics generated by the generator unit. The provider unit can, for example, display the generated mnemonics on the user's device. Specifically, it can display the generated mnemonics as notifications or pop-up messages on devices such as smartphones, tablets, and personal computers. The provider unit can also publish the generated mnemonics on a "mnemonic sharing platform." For example, the provider unit can share the generated mnemonics on the platform and share them with other users. On the sharing platform, users can rate the generated mnemonics and leave comments. This allows other users to refer to them and find better mnemonics. The provider unit can also display the generated mnemonics in a ranking format. For example, the provider unit can display popular mnemonics in a ranking format for users to check. The ranking is updated based on user ratings and frequency of use, always providing the latest information. Furthermore, the provider unit can collect user feedback and provide it to the generator unit, continuously improving the quality of the mnemonics. This allows the provider unit to provide users with effective and engaging mnemonics, improving their memorability.

[0064] The generation unit can generate mnemonics by considering information such as the user's age group, gender, season / era, and hobbies. For example, the generation unit can generate mnemonics for young people or for the elderly depending on the user's age group. The generation unit can also generate mnemonics for men or for women depending on the user's gender. The generation unit can also generate mnemonics that have a seasonal feel or mnemonics that reflect the historical context depending on the season or era. The generation unit can also generate mnemonics related to the user's hobbies depending on the user's hobbies. In this way, the generation unit can provide the user with the most suitable mnemonics. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information such as the user's age group, gender, season / era, and hobbies into a generation AI and have the generation AI perform the generation of mnemonics.

[0065] The provider can publish the generated mnemonics on a "mnemonic sharing platform." For example, the provider can share the generated mnemonics on the platform and share them with other users. For example, the provider can provide a user registration function so that users can publish their own mnemonics. For example, the provider can provide a search function so that users can search for other mnemonics. For example, the provider can provide a rating function so that users can rate other mnemonics. In this way, the provider can share mnemonics with other users. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can publish the generated mnemonics on the platform using an AI model.

[0066] The service provider can display the generated mnemonics in a ranking format. For example, the service provider can display popular mnemonics in a ranking format for users to review. The service provider can also display mnemonics in a ranking format based on evaluation scores, for example. The service provider can also display mnemonics in a ranking format based on frequency of use, for example. This allows the service provider to review popular mnemonics in a ranking format. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can display the generated mnemonics in a ranking format using an AI model.

[0067] The reception unit can accept strings such as historical dates, telephone numbers, PINs, bank account numbers, pi, element symbols, and passwords. For example, the reception unit can accept historical dates entered by the user. The reception unit can also accept telephone numbers entered by the user. The reception unit can also accept PINs entered by the user. The reception unit can also accept bank account numbers entered by the user. The reception unit can also accept pi entered by the user. The reception unit can also accept element symbols entered by the user. The reception unit can also accept passwords entered by the user. This allows the reception unit to accept a wide variety of strings. Some or all of the processing described above in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can accept strings entered by the user using an AI model.

[0068] The reception unit can estimate the user's emotions and adjust the timing of text input based on the estimated emotions. For example, if the user is stressed, the reception unit can simplify the input interface and minimize the input steps. If the user is relaxed, for example, the reception unit can provide detailed input options and suggest customizable input methods. If the user is in a hurry, for example, the reception unit can prioritize voice input to allow for quick text input. This allows the reception unit to provide input timing that is appropriate to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into an AI model and have the AI ​​model perform emotion estimation.

[0069] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can automatically display strings that the user has frequently entered in the past as suggestions. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest strings that the user will use during a specific time period based on the user's past input history. In this way, the reception unit can provide the user with the optimal input method. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past input history into an AI model and have the AI ​​model select the optimal input method.

[0070] The input field can filter the input string based on the user's current learning status and areas of interest. For example, the input field may prioritize displaying strings related to the subject the user is currently studying. The input field may also filter and display relevant strings based on the user's areas of interest. The input field may also suggest strings of appropriate difficulty level according to the user's learning progress. In this way, the input field can provide strings that are tailored to the user's learning status and areas of interest. Some or all of the above processing in the input field may be performed using AI, for example, or not. For example, the input field may input data on the user's learning status and areas of interest into an AI model and have the AI ​​model perform the filtering.

[0071] The reception unit can estimate the user's emotions and determine the priority of input strings based on the estimated emotions. For example, if the user is stressed, the reception unit may prioritize displaying simple strings. For example, if the user is relaxed, the reception unit may prioritize displaying more difficult strings. For example, if the user is in a hurry, the reception unit may prioritize displaying shorter strings. In this way, the reception unit can provide string prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user emotion data into an AI model and have the AI ​​model perform emotion estimation.

[0072] The reception unit can prioritize input of highly relevant strings when a user enters text, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize displaying strings related to that region. For example, if the user is traveling, the reception unit can also prioritize displaying strings related to their travel destination. For example, if the user is at home, the reception unit can also prioritize displaying strings related to their home. In this way, the reception unit can provide strings based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into an AI model and have the AI ​​model select highly relevant strings.

[0073] The reception unit can analyze the user's social media activity when a string is entered and input relevant strings. For example, the reception unit can prioritize displaying strings that the user frequently uses on social media. The reception unit can also suggest relevant strings based on the content of the user's social media posts. The reception unit can also suggest strings based on the strings used by the user's social media followers. In this way, the reception unit can provide strings based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into an AI model and have the AI ​​model select relevant strings.

[0074] The generation unit can estimate the user's emotions and adjust the expression of the mnemonic based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a humorous mnemonic. If the user is in a hurry, the generation unit can also generate a concise and easy-to-remember mnemonic. If the user is excited, the generation unit can also generate a visually stimulating mnemonic. In this way, the generation unit can provide a mnemonic expression that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the expression of the mnemonic.

[0075] The generation unit can adjust the level of detail of mnemonics based on the importance of the string when generating them. For example, the generation unit generates detailed mnemonics for important strings. For example, the generation unit can also generate concise mnemonics for less important strings. For example, the generation unit can generate special mnemonics for strings that the user has specifically requested to remember. In this way, the generation unit can provide mnemonics according to the importance of the strings. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input string importance data into a generation AI and have the generation AI adjust the level of detail of the mnemonics.

[0076] The generation unit can apply different generation algorithms depending on the category of the string when generating mnemonics. For example, in the case of historical dates, the generation unit can generate mnemonics related to historical events. For example, in the case of phone numbers, the generation unit can also generate mnemonics based on the sequence of numbers. For example, in the case of passwords, the generation unit can also generate mnemonics that take security into consideration. In this way, the generation unit can provide mnemonics according to the category of the string. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input string category data into a generation AI and cause the generation AI to execute the application of different generation algorithms.

[0077] The generation unit can estimate the user's emotions and adjust the length of the mnemonic based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a longer mnemonic. If the user is in a hurry, the generation unit can also generate a short, concise mnemonic. If the user is excited, the generation unit can also generate a visually stimulating mnemonic. In this way, the generation unit can provide mnemonic lengths that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the length of the mnemonic.

[0078] The generation unit can determine the priority of mnemonics based on the submission date of the string when generating mnemonics. For example, the generation unit will prioritize generating mnemonics when the deadline is approaching. The generation unit can also postpone generating mnemonics when the submission date is far away. For example, if the submission date is unknown, the generation unit can determine the priority by considering other factors. This allows the generation unit to provide mnemonics appropriate to the submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input string submission date data into a generation AI and have the generation AI determine the priority of mnemonics.

[0079] The generation unit can adjust the order of mnemonics based on the relevance of the strings when generating them. For example, the generation unit can prioritize incorporating highly relevant strings into the mnemonics. The generation unit can also, for example, postpone incorporating less relevant strings into the mnemonics. The generation unit can also, for example, group related strings and incorporate them into the mnemonics. In this way, the generation unit can provide mnemonics according to their relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input string relevance data into a generation AI and have the generation AI perform the adjustment of the mnemonic order.

[0080] The service provider can estimate the user's emotions and adjust the display method of the mnemonics based on the estimated user emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, the service provider can also provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can also provide a display method that gets straight to the point. In this way, the service provider can provide a display method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into an AI model and have the AI ​​model perform the adjustment of the display method.

[0081] The service provider can select the optimal display method by referring to the user's past usage history when providing mnemonics. For example, the service provider can prioritize providing display methods that the user has preferred to use in the past. For example, the service provider can also suggest the optimal display method based on the user's past usage history. For example, the service provider can analyze the user's past usage history and provide a display method with high visibility. In this way, the service provider can provide a display method based on past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past usage history data into an AI model and have the AI ​​model select the optimal display method.

[0082] The service provider can customize the displayed content based on the user's current learning status when providing mnemonics. For example, the service provider can prioritize displaying mnemonics related to the subject the user is currently studying. The service provider can also provide mnemonics of appropriate difficulty level according to the user's learning progress. The service provider can also customize and display relevant mnemonics based on the user's learning status. This allows the service provider to provide displayed content that is appropriate to the learning status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's learning status data into an AI model and have the AI ​​model perform the customization of the displayed content.

[0083] The service provider can estimate the user's emotions and adjust the display order of the mnemonics based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display order. For example, if the user is relaxed, the service provider can also provide a display order that includes detailed information. For example, if the user is in a hurry, the service provider can also provide a display order that gets straight to the point. In this way, the service provider can provide a display order that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into an AI model and have the AI ​​model perform the adjustment of the display order.

[0084] The service provider can select the optimal display method when providing mnemonics, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize displaying mnemonics related to that region. For example, if the user is traveling, the service provider can prioritize displaying mnemonics related to the travel destination. For example, if the user is at home, the service provider can prioritize displaying mnemonics related to home. In this way, the service provider can provide a display method based on geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into an AI model and have the AI ​​model select the optimal display method.

[0085] The service provider can analyze the user's social media activity and customize the displayed content when providing mnemonics. For example, the service provider can prioritize displaying mnemonics that the user frequently uses on social media. The service provider can also suggest relevant mnemonics based on the user's social media posts. The service provider can also suggest mnemonics based on the mnemonics used by the user's social media followers. This allows the service provider to provide displayed content based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into an AI model and have the AI ​​model perform the customization of the displayed content.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can provide real-time feedback on the string entered by the user. For example, if the string entered by the user is incomplete, the reception desk can immediately suggest corrections. Furthermore, the reception desk can also provide relevant information and hints for the string entered by the user. For example, if the user enters a historical date, the reception desk can display information about events and people associated with that date. This allows the reception desk to support the user's input and promote the entry of more accurate strings. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's input data into an AI model and have the AI ​​model perform the provision of real-time feedback.

[0088] The generation unit can estimate the user's emotions and adjust the difficulty of the mnemonics based on the estimated emotions. For example, if the user is stressed, it can generate simple and easy-to-remember mnemonics. Conversely, if the user is relaxed, it can generate slightly more difficult mnemonics. Furthermore, if the user is excited, it can generate challenging mnemonics. In this way, the generation unit can provide the optimal mnemonics according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the difficulty of the mnemonics.

[0089] The service provider can include a function to notify the user's device of the generated mnemonics. For example, it can send the generated mnemonics via push notification at a time set by the user. Furthermore, the service provider can also link with the user's calendar or schedule to remind the user of mnemonics before important events or exams. This allows the service provider to support the user in efficiently memorizing mnemonics. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's schedule data into an AI model and have the AI ​​model execute the timing of notifications.

[0090] The service provider can display the generated mnemonics in a visually appealing format. For example, it can use animations or illustrations to display the mnemonics, making them easier for users to remember. Furthermore, the service provider can also include a function that allows users to select customizable themes and fonts. This allows the service provider to provide a display method that suits the user's preferences, making mnemonic learning more enjoyable. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user customization data into an AI model and have the AI ​​model select a visual display method.

[0091] The reception unit can provide voice feedback for the text entered by the user. For example, it can read aloud the text entered by the user to prompt confirmation. Furthermore, if the user uses voice input, the reception unit can analyze the voice and provide appropriate feedback. In this way, the reception unit can support user input by providing feedback using not only visual but also auditory senses. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's voice data into an AI model and have the AI ​​model perform the provision of voice feedback.

[0092] The generation unit can estimate the user's emotions and select mnemonic themes based on the estimated emotions. For example, if the user is relaxed, it can generate mnemonics related to nature or scenery. Conversely, if the user is excited, it can generate mnemonics related to sports or action. Furthermore, if the user is sad, it can generate mnemonics with encouraging or uplifting themes. In this way, the generation unit can provide mnemonics with themes that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI select mnemonic themes.

[0093] The service provider can deliver the generated mnemonics in stages according to the user's learning progress. For example, it can provide basic mnemonics to users learning for the first time and increase the difficulty as they progress. Furthermore, the service provider can analyze the user's learning history and provide review mnemonics at the optimal timing. In this way, the service provider can provide support to maximize the user's learning effectiveness. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's learning progress data into an AI model and have the AI ​​model perform the staged delivery.

[0094] The reception unit can provide relevant visual content in response to text entered by the user. For example, if the user enters a historical date, it can display images or videos related to that date. Furthermore, the reception unit can also provide interactive content in response to text entered by the user. For example, if the user enters a chemical element symbol, it can display an interactive graph showing the properties and uses of that element. This allows the reception unit to deepen the user's understanding through visual information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user input data into an AI model and have the AI ​​model provide the relevant visual content.

[0095] The generation unit can estimate the user's emotions and adjust the expression style of the mnemonics based on the estimated emotions. For example, if the user is relaxed, it can generate humorous and fun mnemonics. Conversely, if the user is tense, it can generate simple and intuitive mnemonics. Furthermore, if the user is excited, it can generate visually stimulating mnemonics. In this way, the generation unit can provide mnemonics with the optimal expression style according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the expression style of the mnemonics.

[0096] The service provider may include a function to save the generated mnemonics to the user's device. For example, the user could save their favorite mnemonics as favorites for easy access later. Furthermore, the service provider may also provide a function to share the generated mnemonics with other applications or devices. This allows the service provider to support the user in efficiently managing and utilizing mnemonics. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider could input the user's saved data into an AI model and have the AI ​​model perform the saving and sharing functions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk accepts input of a string of characters to be memorized. Examples of strings to be memorized include, but are not limited to, historical dates, phone numbers, PINs, bank account numbers, pi, element symbols, and passwords. The reception desk accepts the user's input in digital format, for example. The reception desk can also accept strings using voice input. For example, it can convert a user's voice input into text data using speech recognition technology. Step 2: The generation unit uses a generation AI to analyze the string received by the reception unit and generate a mnemonic. The generation unit analyzes the string using, for example, string pattern recognition technology or natural language processing technology. The generation unit can also generate mnemonics considering information such as the user's age group, gender, season / era, and hobbies. For example, the generation unit can generate mnemonics for young people or for the elderly depending on the user's age group. Step 3: The provider provides the user with the mnemonics generated by the generator. The provider, for example, displays the generated mnemonics on the user's device. The provider can also publish the generated mnemonics on a "mnemonic sharing platform." For example, the provider shares the generated mnemonics on the platform and shares them with other users. The provider can also display the generated mnemonics in a ranking format. For example, the provider displays popular mnemonics in a ranking format for users to check.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can receive user input using the reception device 38 of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates mnemonics using generation AI. The provision unit can provide the generated mnemonics to the user using the output device 40 of the smart device 14. The provision unit can also publish the generated mnemonics to the "mnemonic sharing platform" via the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can receive voice input from the user using the microphone 238 of the smart glasses 214. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates mnemonics using a generation AI. The provision unit can provide the generated mnemonics to the user using the speaker 240 of the smart glasses 214. The provision unit can also publish the generated mnemonics to the "mnemonic sharing platform" via the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can receive voice input from the user using the microphone 238 of the headset terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates mnemonics using a generation AI. The provision unit can provide the generated mnemonics to the user using the display 343 of the headset terminal 314. The provision unit can also publish the generated mnemonics to the "mnemonic sharing platform" via the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit can receive voice input from the user using the microphone 238 of the robot 414. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates mnemonics using a generation AI. The provision unit can provide the generated mnemonics to the user using the speaker 240 of the robot 414. The provision unit can also publish the generated mnemonics to the "mnemonic sharing platform" via the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A reception unit that accepts input of the string to be memorized, A generation unit analyzes the string received by the reception unit and generates a mnemonic, The system includes a providing unit that provides the user with the mnemonics generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The system generates mnemonics by considering information such as the user's age group, gender, season / era, and hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The generated mnemonics will be published on the "Mnemonic Sharing Platform". The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The generated mnemonics are displayed in a ranking format. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It accepts strings of characters such as historical dates, phone numbers, PINs, bank account numbers, pi, element symbols, and passwords. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of text input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When a string is entered, filtering is performed based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input strings based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering text, the system prioritizes inputting text that is highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a user enters text, the system analyzes their social media activity and inputs relevant text. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the wordplay based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating mnemonics, adjust the level of detail of the mnemonics based on the importance of the strings. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating mnemonics, different generation algorithms are applied depending on the category of the string. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates the user's emotions and adjusts the length of the mnemonic based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating mnemonics, the priority of the mnemonics is determined based on when the strings were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating mnemonics, the order of the mnemonics is adjusted based on the relationships between the strings. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way the mnemonics are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing mnemonics, the system selects the most suitable display method by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing mnemonics, the displayed content is customized based on the user's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the display order of the mnemonics based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing mnemonics, the optimal display method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing mnemonics, the system analyzes the user's social media activity to customize the displayed content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception unit that accepts input of the string of characters to be memorized, A generation unit analyzes the string received by the reception unit and generates a mnemonic, The system includes a providing unit that provides the user with the mnemonics generated by the generation unit. A system characterized by the following features.

2. The generating unit is The system generates mnemonics by considering information such as the user's age group, gender, season / era, and hobbies. The system according to feature 1.

3. The aforementioned supply unit is, The generated mnemonics will be published on a sharing platform. The system according to feature 1.

4. The aforementioned supply unit is, The generated mnemonics are displayed in a ranking format. The system according to feature 1.

5. The aforementioned reception unit is It accepts strings of characters such as historical dates, phone numbers, PINs, bank account numbers, pi, element symbols, and passwords. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of text input based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When a string is entered, filtering is performed based on the user's current learning status and areas of interest. The system according to feature 1.

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