system

Abstract

The system according to the embodiment aims to check whether text created by a user infringes the copyright of a third party. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a reporting unit. The reception unit inputs text created by a user. The analysis unit analyzes the text input by the reception unit and checks for similarity with existing copyrighted works. The identification unit identifies parts that may be copyright infringements based on the results of the analysis by the analysis unit. The reporting unit reports the parts that may be copyright infringements identified by the identification unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that text created by users may unintentionally infringe the copyrights of third parties.

[0005] The system according to the embodiment aims to check whether text created by a user infringes the copyright of a third party. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, an identifying unit, and a reporting unit. The receiving unit inputs text created by a user. The analyzing unit analyzes the text input by the receiving unit and checks for similarity with existing copyrighted works. The identifying unit identifies portions that may be copyright infringements based on the results of the analysis by the analyzing unit. The reporting unit reports the portions that may be copyright infringements identified by the identifying unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can check whether the text created by the user infringes the copyright of a third party. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A copyright infringement check system according to an embodiment of the present invention is a system that utilizes a generation AI to reduce the risk of a user creating a novel or other work unintentionally infringing a third party's copyright. In this system, the user inputs the text of the novel or other work into an AI, which analyzes the input text and checks for similarity with existing copyrighted works. The AI ​​identifies any potential copyright infringements and reports them to the user. This allows the user to confirm whether their work infringes third-party copyrights. For example, a "reception unit" is required to input the text created by the user. Next, an "analysis unit" is required to analyze the input text. This analysis unit uses AI to analyze the text and check for similarity with existing copyrighted works. Based on the results of the analysis by the analysis unit, a "identification unit" is also required to identify potential copyright infringements. This identification unit uses AI to identify potential copyright infringements and report them to the user. Furthermore, a "reporting unit" is also required to report the identified potential copyright infringements to the user. The reporting unit reports the identified potential copyright infringements to the user. This allows the copyright infringement check system to confirm whether the text created by the user infringes third-party copyrights.

[0029] A copyright infringement check system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a reporting unit. The reception unit inputs text created by a user. The text created by a user includes, but is not limited to, sentences, code, and reports. The reception unit provides, for example, an interface for directly inputting the text created by the user. The reception unit also has a file upload function, allowing users to upload text files created by the user. The reception unit can also support multiple input methods, such as voice input and handwriting input. The analysis unit analyzes the text input by the reception unit and checks its similarity with existing copyrighted works. The analysis unit analyzes the text using, for example, text mining technology. The analysis unit can also evaluate the similarity between the input text and existing copyrighted works using pattern matching technology. The analysis unit can also evaluate the semantic similarity of the text using natural language processing technology. The identification unit identifies portions that may be infringing copyright based on the results of the analysis by the analysis unit. The identification unit identifies portions that may be infringing copyright, for example, based on specific phrases, paragraphs, chapters, etc. The identification unit can also evaluate the possibility of copyright infringement by referring to legal standards and past precedents. Furthermore, the identification unit can automatically identify parts that may be copyright infringing using AI. The reporting unit reports the parts that may be copyright infringing identified by the identification unit to a user. The reporting unit reports in the form of, for example, email, dashboard display, or PDF report. The reporting unit can also provide an interface that allows a user to check the report in real time. Furthermore, the reporting unit has a function to visually display the report content, allowing the user to intuitively understand it. This allows the copyright infringement check system according to the embodiment to check whether text created by a user infringes the copyright of a third party.

[0030] The analysis unit can check the similarity using a database of publicly available copyrighted works. Examples of the database of publicly available copyrighted works include, but are not limited to, specific online libraries and government databases. For example, the analysis unit obtains copyrighted work data from a specific online library and checks the similarity with the input text. The analysis unit can also obtain copyrighted work data from a government database and check the similarity with the input text. Furthermore, the analysis unit can periodically update the database of publicly available copyrighted works and check the similarity using the latest copyrighted work data. Thus, using the database of publicly available copyrighted works improves the accuracy of the similarity check.

[0031] The identification unit can identify portions that may be copyright infringements. The identification unit identifies portions that may be copyright infringements, for example, based on a specific phrase, paragraph, chapter, or the like. The identification unit can, for example, identify phrases in the input text that match existing works. The identification unit can also identify paragraphs in the input text that are similar to existing works. Furthermore, the identification unit can also identify chapters in the input text that are similar to existing works. By identifying portions that may be copyright infringements, the user can take appropriate action.

[0032] The reporting unit can report the portions of possible copyright infringement identified by the identifying unit to the user. The reporting unit reports, for example, in the form of email, dashboard display, PDF report, or the like. For example, the reporting unit notifies the user of the portions of possible copyright infringement identified by the identifying unit by email. The reporting unit can also display the portions of possible copyright infringement identified by the identifying unit on a dashboard so that the user can check them in real time. Furthermore, the reporting unit can generate a PDF report of the portions of possible copyright infringement identified by the identifying unit and provide it to the user. This allows the user to check the portions of possible copyright infringement and take appropriate action.

[0033] The reception unit can analyze the user's past text input history and select the optimal input method. For example, the reception unit preferentially suggests an input method (voice input, handwriting input, etc.) that the user has frequently used in the past. For example, the reception unit preferentially suggests an input method (voice input, handwriting input, etc.) that the user has frequently used in the past. The reception unit can also provide an optimal input interface based on the user's input speed. Furthermore, the reception unit can also suggest an input method suitable for a specific time period based on the user's past input history. This improves input efficiency by providing the optimal input method based on the user's past input history.

[0034] The reception unit can filter the text based on the user's current project or area of ​​interest when the text is input. For example, the reception unit prioritizes input of keywords related to the project the user is currently working on. For example, the reception unit prioritizes input of keywords related to the project the user is currently working on. The reception unit can also automatically suggest related text based on the user's area of ​​interest. Furthermore, the reception unit can filter appropriate input content according to the user's project progress. This improves input accuracy by inputting appropriate text based on the user's project or area of ​​interest.

[0035] When inputting text, the reception unit can prioritize inputting highly relevant text in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting text related to that area. For example, when the user is in a specific area, the reception unit prioritizes inputting text related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize inputting text related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize inputting text related to the user's home. This improves the accuracy of input by inputting highly relevant text based on the user's geographical location information.

[0036] The reception unit can analyze the user's social media activity and input related text when inputting text. The reception unit can input related text based on, for example, content recently shared by the user on social media. The reception unit can also input related text based on topics in which the user's social media followers are interested. Furthermore, the reception unit can also input related text based on hashtags frequently used by the user on social media. This improves the accuracy of input by inputting related text based on the user's social media activity.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the text. For example, the analysis unit performs a detailed analysis on text with high importance. For example, the analysis unit performs a detailed analysis on text with high importance. The analysis unit can also perform a simplified analysis on text with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the text. This improves the efficiency of the analysis by adjusting the level of detail of the analysis according to the importance of the text.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the text. For example, the analysis unit applies a narrative structure analysis algorithm to the text of a novel. For example, the analysis unit applies a narrative structure analysis algorithm to the text of a novel. The analysis unit can also apply a technical terminology analysis algorithm to the text of a technical document. Furthermore, the analysis unit can also apply a poetic expression analysis algorithm to the text of a poem. In this way, by applying an appropriate analysis algorithm depending on the text category, the accuracy of the analysis is improved.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the text. For example, the analysis unit prioritizes analysis of text with an upcoming submission deadline. For example, the analysis unit prioritizes analysis of text with an upcoming submission deadline. The analysis unit can also postpone analysis of text with a more distant submission deadline. Furthermore, the analysis unit can adjust the priority of analysis depending on the submission time. In this way, efficient analysis is possible by determining the priority of analysis based on the submission time of the text.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the text. For example, the analysis unit prioritizes analysis of highly relevant text. For example, the analysis unit prioritizes analysis of highly relevant text. The analysis unit can also postpone analysis of less relevant text. Furthermore, the analysis unit can also adjust the order of analysis according to the relevance of the text. As a result, adjusting the order of analysis based on the relevance of the text enables efficient analysis.

[0041] The identification unit may improve the accuracy of identification by taking into account interrelationships between texts during identification. For example, the identification unit may improve the accuracy of identification by taking into account interrelationships between characters in the text. For example, the identification unit may improve the accuracy of identification by taking into account interrelationships between characters in the text. The identification unit may also improve the accuracy of identification by taking into account the order of events in the text. Furthermore, the identification unit may improve the accuracy of identification by taking into account themes and motifs in the text. In this way, the accuracy of identification is improved by taking into account interrelationships between texts.

[0042] When identifying, the identification unit can perform the identification taking into consideration attribute information of the person who submitted the text. The identification unit performs the identification taking into consideration, for example, the age and gender of the person who submitted the text. The identification unit can also perform the identification taking into consideration, for example, the age and gender of the person who submitted the text. The identification unit can also perform the identification taking into consideration the occupation and field of expertise of the person who submitted the text. Furthermore, the identification unit can also perform the identification taking into consideration the submitter's past work history. In this way, by taking into consideration the attribute information of the submitter, the accuracy of the identification is improved.

[0043] The identification unit can perform identification by taking into consideration the geographical distribution of the text. For example, the identification unit preferentially identifies text related to a specific region. For example, the identification unit preferentially identifies text related to a specific region. The identification unit can also group geographically close texts for identification. Furthermore, the identification unit can improve the accuracy of identification based on the geographical distribution. In this way, the accuracy of identification is improved by taking into consideration the geographical distribution of the text.

[0044] During identification, the identification unit can improve the accuracy of identification by referring to related literature of the text. For example, the identification unit improves the accuracy of identification by referring to related literature. For example, the identification unit improves the accuracy of identification by referring to related literature. The identification unit can also improve the accuracy of identification by taking into account citations of related literature. Furthermore, the identification unit can also improve the accuracy of identification by analyzing the content of related literature. In this way, the accuracy of identification is improved by referring to related literature.

[0045] When reporting, the reporting unit can select the optimal display method by referring to the user's past operation history. The reporting unit, for example, prioritizes providing a display method that the user has used preferentially in the past. The reporting unit, for example, prioritizes providing a display method that the user has used preferentially in the past. The reporting unit can also suggest the optimal display method based on the user's operation history. Furthermore, the reporting unit can also customize a specific display method from the user's past operation history. This improves the accuracy of the report by providing the optimal display method based on the user's past operation history.

[0046] When reporting, the reporting unit can select the optimal display method taking into account the user's device information. For example, if the user is using a smartphone, the reporting unit provides a display method that matches the screen size. For example, if the user is using a smartphone, the reporting unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the reporting unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the reporting unit can also provide a display method that includes detailed information. This improves the accuracy of reporting by providing the optimal display method based on the user's device information.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can prioritize and suggest input methods (such as voice input or handwriting input) that the user has frequently used in the past. The reception unit can also provide the optimal input interface based on the user's input speed. Furthermore, the reception unit can also suggest an input method suitable for a specific time period based on the user's past input history. This improves input efficiency by providing the optimal input method based on the user's past input history.

[0049] The analysis unit can adjust the level of detail of the analysis based on the importance of the text. For example, a detailed analysis can be performed on text with high importance. Also, a simplified analysis can be performed on text with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the text. This improves the efficiency of the analysis by adjusting the level of detail of the analysis according to the importance of the text.

[0050] The identification unit can improve the accuracy of identification by taking into account the interrelationships between texts. For example, the accuracy of identification can be improved by taking into account the relationships between characters in the text. The accuracy of identification can also be improved by taking into account the order of events in the text. Furthermore, the identification unit can improve the accuracy of identification by taking into account themes and motifs in the text. In this way, the accuracy of identification can be improved by taking into account the interrelationships between texts.

[0051] The reporting unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the reporting unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the reporting unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the reporting unit can provide a display method that includes detailed information. This improves the accuracy of reporting by providing the optimal display method based on the user's device information.

[0052] During analysis, the analysis unit can apply different analysis algorithms depending on the text category. For example, a narrative structure analysis algorithm can be applied to the text of a novel. The analysis unit can also apply a technical terminology analysis algorithm to the text of a technical document. Furthermore, the analysis unit can apply a poetic expression analysis algorithm to the text of a poem. In this way, the accuracy of the analysis can be improved by applying an appropriate analysis algorithm depending on the text category.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit inputs text created by the user. The text created by the user can be a sentence, code, report, etc. The reception unit provides an interface for direct input of the text created by the user and also has a file upload function. In addition, it supports multiple input methods such as voice input and handwriting input. Step 2: The analysis unit analyzes the text input by the reception unit and checks for similarity with existing copyrighted works. The analysis unit uses text mining technology, pattern matching technology, and natural language processing technology to evaluate the similarity between the input text and existing copyrighted works. Step 3: The Identification Unit identifies parts that may be infringing copyright based on the results of the analysis by the Analysis Unit. The Identification Unit identifies parts based on specific phrases, paragraphs, chapters, etc., and evaluates them with reference to legal standards and past court decisions. Automatic identification can also be performed using AI. Step 4: The reporting unit reports the potential copyright infringement identified by the identification unit to the user. The reporting unit reports in the form of email, dashboard display, PDF report, etc., and provides an interface that allows the user to check the report in real time. It also has a function to visually display the report content.

[0055] (Example 2) A copyright infringement check system according to an embodiment of the present invention is a system that utilizes a generation AI to reduce the risk of a user creating a novel or other work unintentionally infringing a third party's copyright. In this system, the user inputs the text of the novel or other work into an AI, which analyzes the input text and checks for similarity with existing copyrighted works. The AI ​​identifies any potential copyright infringements and reports them to the user. This allows the user to confirm whether their work infringes third-party copyrights. For example, a "reception unit" is required to input the text created by the user. Next, an "analysis unit" is required to analyze the input text. This analysis unit uses AI to analyze the text and check for similarity with existing copyrighted works. Based on the results of the analysis by the analysis unit, a "identification unit" is also required to identify potential copyright infringements. This identification unit uses AI to identify potential copyright infringements and report them to the user. Furthermore, a "reporting unit" is also required to report the identified potential copyright infringements to the user. The reporting unit reports the identified potential copyright infringements to the user. This allows the copyright infringement check system to confirm whether the text created by the user infringes third-party copyrights.

[0056] A copyright infringement check system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a reporting unit. The reception unit inputs text created by a user. The text created by a user includes, but is not limited to, sentences, code, and reports. The reception unit provides, for example, an interface for directly inputting the text created by the user. The reception unit also has a file upload function, allowing users to upload text files created by the user. The reception unit can also support multiple input methods, such as voice input and handwriting input. The analysis unit analyzes the text input by the reception unit and checks its similarity with existing copyrighted works. The analysis unit analyzes the text using, for example, text mining technology. The analysis unit can also evaluate the similarity between the input text and existing copyrighted works using pattern matching technology. The analysis unit can also evaluate the semantic similarity of the text using natural language processing technology. The identification unit identifies portions that may be infringing copyright based on the results of the analysis by the analysis unit. The identification unit identifies portions that may be infringing copyright, for example, based on specific phrases, paragraphs, chapters, etc. The identification unit can also evaluate the possibility of copyright infringement by referring to legal standards and past precedents. Furthermore, the identification unit can automatically identify parts that may be copyright infringing using AI. The reporting unit reports the parts that may be copyright infringing identified by the identification unit to a user. The reporting unit reports in the form of, for example, email, dashboard display, or PDF report. The reporting unit can also provide an interface that allows a user to check the report in real time. Furthermore, the reporting unit has a function to visually display the report content, allowing the user to intuitively understand it. This allows the copyright infringement check system according to the embodiment to check whether text created by a user infringes the copyright of a third party.

[0057] The analysis unit can check the similarity using a database of publicly available copyrighted works. Examples of the database of publicly available copyrighted works include, but are not limited to, specific online libraries and government databases. For example, the analysis unit obtains copyrighted work data from a specific online library and checks the similarity with the input text. The analysis unit can also obtain copyrighted work data from a government database and check the similarity with the input text. Furthermore, the analysis unit can periodically update the database of publicly available copyrighted works and check the similarity using the latest copyrighted work data. Thus, using the database of publicly available copyrighted works improves the accuracy of the similarity check.

[0058] The identification unit can identify portions that may be copyright infringements. The identification unit identifies portions that may be copyright infringements, for example, based on a specific phrase, paragraph, chapter, or the like. The identification unit can, for example, identify phrases in the input text that match existing works. The identification unit can also identify paragraphs in the input text that are similar to existing works. Furthermore, the identification unit can also identify chapters in the input text that are similar to existing works. By identifying portions that may be copyright infringements, the user can take appropriate action.

[0059] The reporting unit can report the portions of possible copyright infringement identified by the identifying unit to the user. The reporting unit reports, for example, in the form of email, dashboard display, PDF report, or the like. For example, the reporting unit notifies the user of the portions of possible copyright infringement identified by the identifying unit by email. The reporting unit can also display the portions of possible copyright infringement identified by the identifying unit on a dashboard so that the user can check them in real time. Furthermore, the reporting unit can generate a PDF report of the portions of possible copyright infringement identified by the identifying unit and provide it to the user. This allows the user to check the portions of possible copyright infringement and take appropriate action.

[0060] The reception unit can estimate the user's emotions and adjust the timing of text input based on the estimated user's emotions. For example, if the user is impatient, the reception unit delays the input timing to allow the user to input calmly. For example, if the user is impatient, the reception unit delays the input timing to allow the user to input calmly. Furthermore, if the user is relaxed, the reception unit can also advance the input timing to allow the user to input smoothly. Furthermore, if the user is tired, the reception unit can adjust the input timing to encourage the user to take a break. In this way, a more appropriate input environment can be provided by adjusting the input timing according to the user's emotions.

[0061] The reception unit can analyze the user's past text input history and select the optimal input method. For example, the reception unit preferentially suggests an input method (voice input, handwriting input, etc.) that the user has frequently used in the past. For example, the reception unit preferentially suggests an input method (voice input, handwriting input, etc.) that the user has frequently used in the past. The reception unit can also provide an optimal input interface based on the user's input speed. Furthermore, the reception unit can also suggest an input method suitable for a specific time period based on the user's past input history. This improves input efficiency by providing the optimal input method based on the user's past input history.

[0062] The reception unit can filter the text based on the user's current project or area of ​​interest when the text is input. For example, the reception unit prioritizes input of keywords related to the project the user is currently working on. For example, the reception unit prioritizes input of keywords related to the project the user is currently working on. The reception unit can also automatically suggest related text based on the user's area of ​​interest. Furthermore, the reception unit can filter appropriate input content according to the user's project progress. This improves input accuracy by inputting appropriate text based on the user's project or area of ​​interest.

[0063] The reception unit can estimate the user's emotions and determine the priority of text to be input based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit postpones input of text with low importance. For example, when the user is feeling stressed, the reception unit postpones input of text with low importance. Furthermore, when the user is relaxed, the reception unit can also preferentially input text with high importance. Furthermore, when the user is in a hurry, the reception unit can also input the most important text first. In this way, by determining the priority of text according to the user's emotions, a more appropriate input order can be provided.

[0064] When inputting text, the reception unit can prioritize inputting highly relevant text in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting text related to that area. For example, when the user is in a specific area, the reception unit prioritizes inputting text related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize inputting text related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize inputting text related to the user's home. This improves the accuracy of input by inputting highly relevant text based on the user's geographical location information.

[0065] The reception unit can analyze the user's social media activity and input related text when inputting text. The reception unit can input related text based on, for example, content recently shared by the user on social media. The reception unit can also input related text based on topics in which the user's social media followers are interested. Furthermore, the reception unit can also input related text based on hashtags frequently used by the user on social media. This improves the accuracy of input by inputting related text based on the user's social media activity.

[0066] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible analysis result. For example, if the user is nervous, the analysis unit provides a simple and highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can also provide an analysis result that focuses on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0067] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the text. For example, the analysis unit performs a detailed analysis on text with high importance. For example, the analysis unit performs a detailed analysis on text with high importance. The analysis unit can also perform a simplified analysis on text with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the text. This improves the efficiency of the analysis by adjusting the level of detail of the analysis according to the importance of the text.

[0068] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the text. For example, the analysis unit applies a narrative structure analysis algorithm to the text of a novel. For example, the analysis unit applies a narrative structure analysis algorithm to the text of a novel. The analysis unit can also apply a technical terminology analysis algorithm to the text of a technical document. Furthermore, the analysis unit can also apply a poetic expression analysis algorithm to the text of a poem. In this way, by applying an appropriate analysis algorithm depending on the text category, the accuracy of the analysis is improved.

[0069] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, the analysis unit can also provide a visually stimulating analysis result if the user is excited. In this way, by adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0070] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the text. For example, the analysis unit prioritizes analysis of text with an upcoming submission deadline. For example, the analysis unit prioritizes analysis of text with an upcoming submission deadline. The analysis unit can also postpone analysis of text with a more distant submission deadline. Furthermore, the analysis unit can adjust the priority of analysis depending on the submission time. In this way, efficient analysis is possible by determining the priority of analysis based on the submission time of the text.

[0071] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the text. For example, the analysis unit prioritizes analysis of highly relevant text. For example, the analysis unit prioritizes analysis of highly relevant text. The analysis unit can also postpone analysis of less relevant text. Furthermore, the analysis unit can also adjust the order of analysis according to the relevance of the text. As a result, adjusting the order of analysis based on the relevance of the text enables efficient analysis.

[0072] The identification unit can estimate the user's emotions and adjust the identification criteria based on the estimated user's emotions. For example, if the user is nervous, the identification unit relaxes the identification criteria to reduce stress. For example, if the user is nervous, the identification unit relaxes the identification criteria to reduce stress. Furthermore, if the user is relaxed, the identification unit can tighten the identification criteria to improve accuracy. Furthermore, if the user is in a hurry, the identification unit can simplify the identification criteria to perform identification quickly. In this way, by adjusting the identification criteria according to the user's emotions, more appropriate identification results can be provided.

[0073] The identification unit may improve the accuracy of identification by taking into account interrelationships between texts during identification. For example, the identification unit may improve the accuracy of identification by taking into account interrelationships between characters in the text. For example, the identification unit may improve the accuracy of identification by taking into account interrelationships between characters in the text. The identification unit may also improve the accuracy of identification by taking into account the order of events in the text. Furthermore, the identification unit may improve the accuracy of identification by taking into account themes and motifs in the text. In this way, the accuracy of identification is improved by taking into account interrelationships between texts.

[0074] When identifying, the identification unit can perform the identification taking into consideration attribute information of the person who submitted the text. The identification unit performs the identification taking into consideration, for example, the age and gender of the person who submitted the text. The identification unit can also perform the identification taking into consideration, for example, the age and gender of the person who submitted the text. The identification unit can also perform the identification taking into consideration the occupation and field of expertise of the person who submitted the text. Furthermore, the identification unit can also perform the identification taking into consideration the submitter's past work history. In this way, by taking into consideration the attribute information of the submitter, the accuracy of the identification is improved.

[0075] The identification unit can estimate the user's emotions and adjust the order in which specific results are displayed based on the estimated user's emotions. For example, if the user is nervous, the identification unit can first display results of lower importance to reassure the user. For example, if the user is nervous, the identification unit can first display results of lower importance to reassure the user. Furthermore, if the user is relaxed, the identification unit can also display results of higher importance first. Furthermore, if the user is in a hurry, the identification unit can display the most important results first. In this way, by adjusting the order in which specific results are displayed according to the user's emotions, more appropriate result display is possible.

[0076] The identification unit can perform identification by taking into consideration the geographical distribution of the text. For example, the identification unit preferentially identifies text related to a specific region. For example, the identification unit preferentially identifies text related to a specific region. The identification unit can also group geographically close texts for identification. Furthermore, the identification unit can improve the accuracy of identification based on the geographical distribution. In this way, the accuracy of identification is improved by taking into consideration the geographical distribution of the text.

[0077] During identification, the identification unit can improve the accuracy of identification by referring to related literature of the text. For example, the identification unit improves the accuracy of identification by referring to related literature. For example, the identification unit improves the accuracy of identification by referring to related literature. The identification unit can also improve the accuracy of identification by taking into account citations of related literature. Furthermore, the identification unit can also improve the accuracy of identification by analyzing the content of related literature. In this way, the accuracy of identification is improved by referring to related literature.

[0078] The reporting unit can estimate the user's emotions and adjust the display method of the report based on the estimated user's emotions. For example, when the user is nervous, the reporting unit provides a simple, highly visible display method. For example, when the user is nervous, the reporting unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the reporting unit can also provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the reporting unit can also provide a display method that focuses on the main points. This allows for more appropriate reporting by adjusting the display method of the report according to the user's emotions.

[0079] When reporting, the reporting unit can select the optimal display method by referring to the user's past operation history. The reporting unit, for example, prioritizes providing a display method that the user has used preferentially in the past. The reporting unit, for example, prioritizes providing a display method that the user has used preferentially in the past. The reporting unit can also suggest the optimal display method based on the user's operation history. Furthermore, the reporting unit can also customize a specific display method from the user's past operation history. This improves the accuracy of the report by providing the optimal display method based on the user's past operation history.

[0080] The reporting unit can estimate the user's emotions and adjust the reported operation procedures based on the estimated user's emotions. For example, if the user is nervous, the reporting unit simplifies the operation procedures to reduce stress. For example, if the user is nervous, the reporting unit simplifies the operation procedures to reduce stress. The reporting unit can also provide detailed operation procedures when the user is relaxed. Furthermore, the reporting unit can also provide procedures that allow the user to operate quickly when the user is in a hurry. This allows for more appropriate operation by adjusting the reported operation procedures according to the user's emotions.

[0081] When reporting, the reporting unit can select the optimal display method taking into account the user's device information. For example, if the user is using a smartphone, the reporting unit provides a display method that matches the screen size. For example, if the user is using a smartphone, the reporting unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the reporting unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the reporting unit can also provide a display method that includes detailed information. This improves the accuracy of reporting by providing the optimal display method based on the user's device information. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, identification unit, and reporting unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for inputting text created by a user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the input text and checks for similarity with existing copyrighted works. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and identifies portions that may be copyright infringing based on the analysis results. The reporting unit is implemented, for example, by the control unit 46A of the smart device 14 and reports the identified portions that may be copyright infringing to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, identification unit, and reporting unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for inputting text created by a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the input text and checks for similarity with existing copyrighted works. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies portions that may be copyright infringing based on the analysis results. The reporting unit is realized, for example, by the control unit 46A of the smart glasses 214 and reports the identified portions that may be copyright infringing to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and reporting unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and provides an interface for inputting text created by a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the input text and checks for similarity with existing copyrighted works. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies parts that may be copyright infringing based on the analysis results. The reporting unit is realized, for example, by the control unit 46A of the headset type terminal 314 and reports the identified parts that may be copyright infringing to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and reporting unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for inputting text created by a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the input text and checks for similarity with existing copyrighted works. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies parts that may be copyright infringing based on the analysis results. The reporting unit is realized, for example, by the control unit 46A of the robot 414 and reports the identified parts that may be copyright infringing to the user.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can prioritize and suggest input methods (such as voice input or handwriting input) that the user has frequently used in the past. The reception unit can also provide the optimal input interface based on the user's input speed. Furthermore, the reception unit can also suggest an input method suitable for a specific time period based on the user's past input history. This improves input efficiency by providing the optimal input method based on the user's past input history.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the text. For example, a detailed analysis can be performed on text with high importance. Also, a simplified analysis can be performed on text with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the text. This improves the efficiency of the analysis by adjusting the level of detail of the analysis according to the importance of the text.

[0085] The identification unit can improve the accuracy of identification by taking into account the interrelationships between texts. For example, the accuracy of identification can be improved by taking into account the relationships between characters in the text. The accuracy of identification can also be improved by taking into account the order of events in the text. Furthermore, the identification unit can improve the accuracy of identification by taking into account themes and motifs in the text. In this way, the accuracy of identification can be improved by taking into account the interrelationships between texts.

[0086] The reporting unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the reporting unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the reporting unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the reporting unit can provide a display method that includes detailed information. This improves the accuracy of reporting by providing the optimal display method based on the user's device information.

[0087] The reception unit can estimate the user's emotions and adjust the timing of text input based on the estimated user emotions. For example, if the user is in a hurry, the reception unit can delay the input timing to allow the user to input calmly. Also, if the user is relaxed, the reception unit can advance the input timing to allow the user to input smoothly. Furthermore, if the user is tired, the reception unit can adjust the input timing to encourage the user to take a break. In this way, a more appropriate input environment can be provided by adjusting the input timing according to the user's emotions.

[0088] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can also provide analysis results that focus on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0089] The identification unit can estimate the user's emotions and adjust the identification criteria based on the estimated user's emotions. For example, if the user is nervous, the identification criteria can be relaxed to reduce stress. The identification unit can also tighten the identification criteria to improve accuracy if the user is relaxed. Furthermore, if the user is in a hurry, the identification unit can simplify the identification criteria to perform identification quickly. In this way, by adjusting the identification criteria according to the user's emotions, more appropriate identification results can be provided.

[0090] The reporting unit can estimate the user's emotions and adjust the display method of the report based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, the reporting unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the reporting unit can also provide a display method that focuses on the main points. This allows for more appropriate reporting by adjusting the display method of the report according to the user's emotions.

[0091] The reporting unit can estimate the user's emotions and adjust the reported operation procedures based on the estimated user's emotions. For example, if the user is nervous, the operation procedures can be simplified to reduce stress. The reporting unit can also provide detailed operation procedures when the user is relaxed. Furthermore, the reporting unit can also provide procedures that allow the user to operate quickly when the user is in a hurry. In this way, adjusting the reported operation procedures according to the user's emotions enables more appropriate operation.

[0092] During analysis, the analysis unit can apply different analysis algorithms depending on the text category. For example, a narrative structure analysis algorithm can be applied to the text of a novel. The analysis unit can also apply a technical terminology analysis algorithm to the text of a technical document. Furthermore, the analysis unit can apply a poetic expression analysis algorithm to the text of a poem. In this way, the accuracy of the analysis can be improved by applying an appropriate analysis algorithm depending on the text category.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The reception unit inputs text created by the user. The text created by the user can be a sentence, code, report, etc. The reception unit provides an interface for direct input of the text created by the user and also has a file upload function. In addition, it supports multiple input methods such as voice input and handwriting input. Step 2: The analysis unit analyzes the text input by the reception unit and checks for similarity with existing copyrighted works. The analysis unit uses text mining technology, pattern matching technology, and natural language processing technology to evaluate the similarity between the input text and existing copyrighted works. Step 3: The Identification Unit identifies parts that may be infringing copyright based on the results of the analysis by the Analysis Unit. The Identification Unit identifies parts based on specific phrases, paragraphs, chapters, etc., and evaluates them with reference to legal standards and past court decisions. Automatic identification can also be performed using AI. Step 4: The reporting unit reports the potential copyright infringement identified by the identification unit to the user. The reporting unit reports in the form of email, dashboard display, PDF report, etc., and provides an interface that allows the user to check the report in real time. It also has a function to visually display the report content.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting text created by a user; an analysis unit that analyzes the text input by the reception unit and checks for similarity with existing copyrighted works; an identifying unit that identifies a portion that may be infringing copyright based on the analysis result by the analyzing unit; a reporting unit that reports to a user the portion identified by the identifying unit that may be infringing copyright. A system characterized by:

2. The analysis unit Check for similarity using a database of publicly available copyrighted works 2. The system of claim 1.

3. The identification unit Identifying potential copyright infringements 2. The system of claim 1.

4. The reporting unit Reporting to a user the portion of the content that may be infringing copyright identified by the identifying unit 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and adjust the timing of text input based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past text input history and select the optimal input method 2. The system of claim 1.

7. The reception unit As you type, filter based on your current projects and interests 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and prioritize the text to be entered based on the estimated user emotions.

2. The system of claim 1.

Images