system

Abstract

The system according to the embodiment aims to efficiently create materials. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a voice reception unit, and a provision unit. The reception unit inputs text. The generation unit generates materials based on the text input by the reception unit. The voice reception unit accepts voice input. The provision unit provides the materials generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies presented a challenge: creating documents was time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently create materials. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a voice reception unit, and a provision unit. The reception unit inputs a sentence. The generation unit generates a document based on the sentence input by the reception unit. The voice reception unit receives voice input. The provision unit provides the document generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create materials. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The document creation support system according to an embodiment of the present invention is a system in which a generating AI instantly creates 200 patterns of documents simply by the user inputting text, and also accepts voice input. This document creation support system instantly creates 200 patterns of documents simply by the user inputting text. Regarding the structure of the documents, the generating AI automatically processes the data based on the user's chosen image, such as black aesthetic, pop art, or antique style. Users can also input their preferences by voice. For example, if a user voice-inputs "I want a presentation document," the generating AI will create the document based on that request. This service significantly reduces the time required for document creation, allowing for more efficient work. First, the user inputs text. At this time, the user specifically inputs the content of the desired document. For example, they might input "I want a sales report." This information is input to the generating AI. Next, the generating AI analyzes the input information and generates 200 patterns of documents. The generating AI generates the optimal document based on past data and algorithms. For example, if a user requests a sales report, various styles of reports will be generated based on that content. The generated documents are processed to match the image desired by the user. For example, by setting an image such as "black aesthetic," "POP," or "antique," the generating AI automatically processes the materials. Furthermore, users can input their preferences by voice. For example, if a user voice-inputs "I want you to create a presentation," the generating AI will create materials based on that preference. In this process, speech recognition technology and natural language processing technology are used to accurately analyze the user's preferences. The generated materials are provided to the user. For example, the materials are provided in the format the user requests. This allows the user to use the generated materials immediately. This service significantly reduces the time required to create materials and enables efficient work. Thus, the material creation support system significantly reduces the time users spend creating materials and enables efficient work.

[0029] The document creation support system according to this embodiment comprises a reception unit, a generation unit, a voice reception unit, and a provision unit. The reception unit is the part where the user inputs text. The text input by the user includes, but is not limited to, business documents, technical documents, and creative writing. The reception unit can accept text by methods such as keyboard input, touch input, and voice input. The generation unit is the part that generates documents based on the text input by the reception unit using a generation AI. The generation unit, for example, generates 200 patterns of documents using the generation AI. The generation AI generates the optimal document based on past data and algorithms. For example, when generating a business document, the generation AI refers to data from past business documents and generates the document in the optimal format. The generation unit can also process documents by setting images such as black taste, POP, and old-fashioned. For example, the generation AI adjusts the design and layout of the document to match the image desired by the user. The voice reception unit is the part where the user inputs their wishes by voice. The voice reception unit analyzes the user's wishes using speech recognition technology and natural language processing technology. For example, when a user voice-inputs a request such as "I want you to create a presentation," the voice reception unit analyzes the request and transmits it to the generation unit. The delivery unit is responsible for providing the user with the materials generated by the generation unit. The delivery unit provides the materials in the format the user desires. For example, the delivery unit can provide materials in PDF, Word, HTML, etc. As a result, the document creation support system according to this embodiment can significantly reduce the time users spend creating documents and allow them to work more efficiently.

[0030] The generation unit can generate 200 patterns of materials using a generation AI. The generation unit generates 200 patterns of materials using, for example, a generation AI. The generation AI generates optimal materials based on past data and algorithms. For example, when generating a business document, the generation AI references data from past business documents and generates materials in the optimal format. The generation AI can also adjust the design and layout of the materials to match the image desired by the user. This allows the generation AI to generate a variety of materials. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI, for example. For example, the generation unit can generate 200 patterns of materials based on data input to the generation AI.

[0031] The generation unit can process materials by setting a black theme, POP, or old-fashioned image. The generation unit processes materials by setting, for example, a black theme, POP, or old-fashioned image. For example, the generation AI adjusts the design and layout of the materials to match the image desired by the user. Black theme materials, for example, have a chic design based on black and are suitable for business documents and presentation materials. POP materials, for example, use bright colors and a pop design and are suitable for advertising and marketing materials. Old-fashioned materials, for example, use traditional designs and fonts and are suitable for historical documents and cultural materials. This allows the materials to be processed to match the image desired by the user. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can process materials to match the image desired by the user based on data input to the generation AI.

[0032] The voice receiving unit can analyze the user's wishes using voice recognition technology or natural language processing technology. The voice receiving unit analyzes the user's wishes using, for example, voice recognition technology. Voice recognition technologies include, for example, deep learning and HMM (hidden Markov model). Deep learning is a technology that learns from large amounts of voice data to achieve highly accurate voice recognition. HMM is a technology that models temporal changes in voice and performs voice recognition. The voice receiving unit can also analyze the user's wishes using natural language processing technology. Natural language processing technologies include, for example, morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis is a technology that divides sentences into words and analyzes the parts of speech of each word. Grammatical analysis is a technology that analyzes the grammatical structure of sentences and understands their meaning. Semantic analysis is a technology that analyzes the meaning of sentences and understands the user's intent. This makes it possible to analyze voice input and generate materials based on the user's wishes. Some or all of the processing described above in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can use speech recognition technology or natural language processing technology to analyze the user's wishes and transmit them to the generation unit.

[0033] The providing unit can provide materials in a format desired by the user. The providing unit provides materials in, for example, a format desired by the user. Formats desired by the user include, for example, PDF format, Word format, and HTML format. The PDF format preserves the layout and design of the document and allows for consistent display across different devices and platforms. The Word format makes it easy to edit and modify documents and is suitable for collaborative work. The HTML format is a format that can be displayed in a web browser and is suitable for sharing and publishing on the Internet. This allows materials to be provided in a format desired by the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide materials generated by the generating unit in a format desired by the user.

[0034] The generation unit can generate appropriate materials based on past data and algorithms. For example, the generation unit generates appropriate materials based on past data and algorithms. Past data includes, for example, past project data and user history data. Past project data refers to materials and project deliverables created in the past, and the generation AI refers to this data to generate the optimal materials. User history data refers to materials created and templates used by the user in the past, and the generation AI uses this data to generate materials tailored to the user's preferences. Algorithms include, for example, machine learning algorithms and optimization algorithms. Machine learning algorithms are techniques for learning from large amounts of data and finding patterns and trends. Optimization algorithms are techniques for finding the optimal solution within given conditions and constraints. This allows for the generation of optimal materials based on past data and algorithms. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without using the generation AI. For example, the generation unit can generate materials desired by the user based on past data and algorithms.

[0035] The reception unit can analyze the user's past input history and select the optimal input method. The reception unit, for example, analyzes the user's past input history and selects the optimal input method. The past input history includes, for example, input content, input frequency, and input pattern. The input content refers to the content of sentences entered by the user in the past, and the reception unit analyzes it to select the optimal input method. The input frequency indicates how frequently the user uses a particular input method, and the reception unit suggests the optimal input method based on this. The input pattern indicates what input method the user uses in a particular time period or situation, and the reception unit selects the optimal input method based on this. For example, the reception unit prioritizes suggesting input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a particular time period based on the user's past input history. Furthermore, the reception unit can suggest similar input methods based on content entered by the user in the past. This enables efficient input by suggesting the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input past input history into the generation AI and have the generation AI select the optimal input method.

[0036] The input field can filter text input based on the user's current projects and areas of interest. For example, the input field can filter text input based on the user's current projects and areas of interest. Current projects include, for example, ongoing projects and tasks. Areas of interest include, for example, topics and themes that the user is interested in. For example, it can prioritize displaying keywords related to the user's current projects. It can also filter and display relevant information based on the user's areas of interest. Furthermore, it can filter input based on topics the user has shown interest in in the past. This enables efficient input by prioritizing the display of relevant information based on the user's projects and areas of interest. Some or all of the above processing in the input field may be performed using, for example, AI, or not using AI. For example, the input field can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0037] The reception unit can prioritize inputting highly relevant text based on the user's geographical location information when text is entered. For example, when text is entered, the reception unit prioritizes inputting highly relevant text based on the user's geographical location information. Geographical location information includes, for example, GPS data and location services. GPS data is data that indicates the user's current location, and the reception unit displays relevant information based on this. Location services are services that identify the user's location, and the reception unit uses this to display relevant information. For example, if the user is in a specific region, information related to that region is prioritized for input. Also, if the user is traveling, information related to the travel destination can be prioritized for input. Furthermore, if the user is at home, information related to home can be prioritized for input. This enables efficient input by prioritizing the input of relevant information based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI prioritize the relevant information.

[0038] The reception unit can analyze the user's social media activity and input relevant text when text is entered. For example, the reception unit analyzes the user's social media activity and inputs relevant text when text is entered. Social media activity includes, for example, posts, the number of likes, and the number of followers. Posts refer to the content that the user has posted on social media, and the reception unit analyzes this and displays relevant information. The number of likes refers to the number of likes the user has given to other posts, and the reception unit displays relevant information based on this. The number of followers refers to the number of followers the user has, and the reception unit displays relevant information based on this. For example, keywords that the user frequently uses on social media are prioritized for input. It can also input relevant information based on the user's social media activity. Furthermore, it can determine the input content based on the topics the user follows on social media. This enables efficient input by inputting relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input data on the user's social media activity into a generating AI and have the generating AI input relevant information.

[0039] The generation unit can adjust the level of detail of the generated document based on the importance of the sentence when generating the document. For example, the generation unit adjusts the level of detail of the generated document based on the importance of the sentence when generating the document. Methods for evaluating the importance of a sentence include, for example, the frequency of keyword appearance and the importance of the context. The frequency of keyword appearance indicates how frequently a particular keyword appears in a sentence, and the generation unit adjusts the level of detail based on this. The importance of the context indicates how important a particular part is in the context of the sentence, and the generation unit adjusts the level of detail based on this. For example, detailed explanations can be added to sentences with high importance. Concise explanations can also be added to sentences with low importance. Furthermore, the layout and design of the document can be adjusted according to the importance. This enables efficient document generation by adjusting the level of detail of the generated document based on the importance of the sentence. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the sentence into the generation AI and have the generation AI adjust the level of detail.

[0040] The generation unit can apply different generation algorithms depending on the category of text when generating materials. For example, the generation unit applies different generation algorithms depending on the category of text when generating materials. Text categories include, for example, technical documents, business documents, and creative documents. Technical documents are documents containing technical content, and the generation unit applies a generation algorithm suitable for them. Business documents are documents containing business-related content, and the generation unit applies a generation algorithm suitable for them. Creative documents are documents containing creative content, and the generation unit applies a generation algorithm suitable for them. For example, a business-oriented generation algorithm can be applied to business documents. Also, an academic generation algorithm can be applied to academic papers. Furthermore, a presentation-oriented generation algorithm can be applied to presentation materials. This enables efficient document generation by applying an appropriate generation algorithm depending on the category of text. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the category of text into the generation AI and have the generation AI apply the generation algorithm.

[0041] The generation unit can determine the generation priority based on the submission date of the document when generating the document. For example, the generation unit determines the generation priority based on the submission date of the document when generating the document. The submission date includes, for example, a deadline date, a project progress status, etc. The deadline date refers to the date by which the document is required to be submitted, and the generation unit determines the priority based on this. The project progress status indicates the progress of the project, and the generation unit determines the priority based on this. For example, documents with an approaching deadline can be generated with priority. Also, documents with a more distant submission date can be postponed. Furthermore, the level of detail of the generation can be adjusted depending on the submission date. This enables efficient document generation by determining the generation priority based on the submission date. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the submission date into the generation AI and have the generation AI determine the priority.

[0042] The generation unit can adjust the order of generation based on the relevance of the sentences when generating materials. For example, the generation unit adjusts the order of generation based on the relevance of the sentences when generating materials. Methods for evaluating the relevance of sentences include, for example, keyword consistency and context consistency. Keyword consistency indicates the degree to which a specific keyword matches within a sentence, and the generation unit adjusts the order based on this. Context consistency indicates the degree to which a specific part matches within the context of the sentence, and the generation unit adjusts the order based on this. For example, highly relevant sentences are generated preferentially. Less relevant sentences can also be postponed. Furthermore, the level of detail of generation can be adjusted according to the relevance. This enables efficient material generation by adjusting the order of generation based on the relevance of the sentences. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the sentences into the generation AI and have the generation AI adjust the order.

[0043] The voice input unit can select the optimal analysis method by referring to the user's past voice input history during voice input. For example, the voice input unit selects the optimal analysis method by referring to the user's past voice input history during voice input. Past voice input history includes, for example, the content of the voice, the frequency of input, and the input pattern. The content of the voice refers to the content of the voice input that the user has previously entered, and the voice input unit analyzes this to select the optimal analysis method. The frequency of input indicates how often the user uses a particular voice input method, and the voice input unit proposes the optimal analysis method based on this. The input pattern indicates what kind of voice input method the user uses at a particular time or in a particular situation, and the voice input unit selects the optimal analysis method based on this. For example, it prioritizes analyzing voice input methods that the user has used in the past. It can also predict and propose analysis methods to be used at a particular time based on the user's past voice input history. Furthermore, it can propose similar analysis methods by referring to the content of the voice input that the user has previously entered. This enables efficient analysis by proposing the optimal analysis method based on past voice input history. Some or all of the above-described processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input past voice input history into a generating AI and have the generating AI select the optimal analysis method.

[0044] The voice accepting unit can improve the accuracy of analysis based on the user's speaking rate and volume when voice is input. For example, the voice accepting unit improves the accuracy of analysis based on the user's speaking rate and volume when voice is input. Speaking rate refers to the speed at which the user speaks, and the voice accepting unit adjusts the accuracy of analysis based on this. Volume refers to the volume at which the user speaks, and the voice accepting unit adjusts the accuracy of analysis based on this. For example, if the user speaks slowly, the voice accepting unit adjusts the accuracy of analysis to match the speaking rate. Also, if the user speaks loudly, the voice accepting unit can adjust the accuracy of analysis to match the volume. Furthermore, if the user speaks quickly, the voice accepting unit can improve the accuracy of analysis to match the speaking rate. This improves the accuracy of analysis based on the user's speaking rate and volume, enabling more accurate analysis. Some or all of the above-described processing in the voice accepting unit may be performed using, for example, AI, or may be performed without AI. For example, the voice accepting unit can input data on the user's speaking rate and volume to the generation AI and cause the generation AI to improve the accuracy of analysis.

[0045] The voice reception unit can prioritize the analysis of highly relevant voice data when voice input is received, taking into account the user's geographical location information. For example, when voice input is received, the voice reception unit prioritizes the analysis of highly relevant voice data, taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data is data that indicates the user's current location, and the voice reception unit displays relevant information based on this. Location information services are services that identify the user's location, and the voice reception unit uses this to display relevant information. For example, if the user is in a specific region, information related to that region is prioritized for analysis. Also, if the user is traveling, information related to the travel destination can be prioritized for analysis. Furthermore, if the user is at home, information related to home can be prioritized for analysis. This enables efficient analysis by prioritizing the analysis of relevant information based on the user's geographical location information. Some or all of the above processing in the voice reception unit may be performed using, for example, AI, or not using AI. For example, the voice reception unit can input the user's geographical location information into a generating AI and have the generating AI prioritize the relevant information.

[0046] The voice reception unit can analyze the user's social media activity and analyze relevant audio when voice input is received. For example, the voice reception unit analyzes the user's social media activity and analyzes relevant audio when voice input is received. Social media activity includes, for example, posts, the number of likes, and the number of followers. Posts refer to the content that the user has posted on social media, and the voice reception unit analyzes this and displays relevant information. The number of likes refers to the number of likes the user has given to other posts, and the voice reception unit displays relevant information based on this. The number of followers refers to the number of followers the user has, and the voice reception unit displays relevant information based on this. For example, it prioritizes analyzing keywords that the user frequently uses on social media. It can also analyze relevant information based on the user's social media activity. Furthermore, it can analyze audio content based on the topics the user follows on social media. This enables efficient analysis by analyzing relevant information based on the user's social media activity. Some or all of the above processing in the voice reception unit may be performed using, for example, AI, or not using AI. For example, the voice reception unit can input data on the user's social media activity into a generating AI, which can then perform analysis of the relevant information.

[0047] The providing unit can select the optimal delivery method by referring to the user's past material usage history when providing materials. For example, when providing materials, the providing unit selects the optimal delivery method by referring to the user's past material usage history. The past material usage history includes, for example, usage frequency, usage content, and usage pattern. Usage frequency indicates how frequently a user uses a particular material format, and the providing unit proposes the optimal delivery method based on this. Usage content refers to the content of materials used by the user in the past, and the providing unit analyzes this to select the optimal delivery method. Usage pattern indicates what material format a user uses in a particular time period or situation, and the providing unit selects the optimal delivery method based on this. For example, the providing unit may prioritize the delivery of material formats used by the user in the past. Furthermore, the providing unit may predict and propose a delivery method to be used in a particular time period based on the user's past material usage history. Furthermore, the providing unit may propose a similar delivery method by referring to the content of materials used by the user in the past. This enables efficient material delivery by proposing the optimal delivery method based on the user's past material usage history. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the provision department can input past material usage history into the generation AI and have the generation AI select the optimal provision method.

[0048] The service provider can customize the content provided based on the user's current projects and areas of interest when providing materials. For example, the service provider can customize the content provided based on the user's current projects and areas of interest when providing materials. Current projects include, for example, ongoing projects and tasks. Areas of interest include, for example, topics and themes that the user is interested in. For example, it can prioritize providing materials related to the user's current projects. It can also provide relevant materials based on the user's areas of interest. Furthermore, it can customize the content provided based on topics the user has shown interest in in the past. This enables efficient material provision by providing relevant materials based on the user's projects and areas of interest. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the customization of the content provided.

[0049] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing materials. For example, when providing materials, the providing unit selects the optimal delivery method by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data is data indicating the user's current location, and the providing unit displays related information based on this data. Location information services are services for identifying the user's location, and the providing unit uses this data to display related information. For example, if the user is in a specific area, materials related to that area can be provided preferentially. Furthermore, if the user is traveling, materials related to the user's travel destination can be provided preferentially. Furthermore, if the user is at home, materials related to the user's home can be provided preferentially. This enables efficient material provision by providing related materials based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's geographical location information into a generation AI and cause the generation AI to select the optimal delivery method.

[0050] The providing unit can analyze the user's social media activity and provide related materials when providing the materials. For example, the providing unit can analyze the user's social media activity and provide related materials when providing the materials. Social media activity includes, for example, post content, the number of likes, and the number of followers. Post content refers to content posted by the user on social media, and the providing unit analyzes the content and displays related information. The number of likes refers to the number of likes the user has given to other posts, and the providing unit displays related information based on the number of likes. The number of followers refers to the number of followers the user has, and the providing unit displays related information based on the number of likes. For example, keywords frequently used by the user on social media can be provided preferentially. Related information can also be provided based on the user's social media activity. Furthermore, material content can be provided based on topics the user follows on social media. This enables efficient material provision by providing related materials based on the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input data on the user's social media activity into a generation AI and cause the generation AI to provide related information.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can automatically search for and display related reference materials based on the user's input. For example, when a user creates a "sales report," past sales reports and related statistical data can be automatically displayed. When a user creates a "technical document," related technical papers and patent information can be displayed. Furthermore, when a user creates a "presentation document," past presentation documents and related visual materials can be displayed. This allows users to quickly obtain the information they need and create documents efficiently.

[0053] The generation unit can automatically select and apply a document template based on the user's input. For example, when a user creates a "sales report," it can automatically select and apply the most suitable sales report template. Similarly, when a user creates a "technical document," it can select and apply a template appropriate for a technical document. Furthermore, when a user creates "presentation materials," it can select and apply a template appropriate for presentation materials. This eliminates the need for the user to manually select templates, allowing them to create documents efficiently.

[0054] The generation unit can automatically summarize the content of documents and extract key points based on user input. For example, when a user creates a "sales report," it can automatically summarize the lengthy content and extract the important points. Similarly, when a user creates a "technical document," it can summarize the technical details and extract the main conclusions. Furthermore, when a user creates "presentation materials," it can automatically extract the key points of the presentation and reflect them in the slides. This allows users to efficiently create documents and effectively communicate important information.

[0055] The voice input unit can automatically generate and present relevant questions based on the user's voice input. For example, when a user is creating a "sales report," it can automatically generate and present relevant questions (e.g., "What are this month's sales?"). When a user is creating a "technical document," it can generate and present questions about technical details (e.g., "What technologies were used?"). Furthermore, when a user is creating "presentation materials," it can generate and present questions about the key points of the presentation (e.g., "What are the main messages?"). This allows users to efficiently input the necessary information and create documents.

[0056] The providing unit can suggest the optimal document format based on the user's document usage history. For example, if the user has frequently used PDF documents in the past, the providing unit can suggest the PDF format. Also, if the user has frequently used Word documents in the past, the providing unit can suggest the Word format. Furthermore, if the user has used HTML documents in the past, the providing unit can suggest the HTML format. This allows the user to receive documents in the optimal format and use them efficiently.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit is a section where the user inputs text. Texts input by the user include business documents, technical documents, creative documents, etc. The reception unit can accept text by keyboard input, touch input, voice input, etc. Step 2: The generation unit uses the generation AI to generate materials based on the text entered by the reception unit. In the generation unit, the generation AI generates 200 patterns of materials. The generation AI generates optimal materials based on past data and algorithms. For example, when generating a business document, the generation AI references data from past business documents and generates materials in the optimal format. The generation unit can also process the materials by setting images such as black taste, POP, and old-fashioned. For example, the generation AI adjusts the design and layout of the materials to match the image desired by the user. Step 3: The voice reception unit is the part where the user can input their requests by voice. The voice reception unit analyzes the user's requests using voice recognition technology and natural language processing technology. For example, if the user inputs by voice, "I want you to create presentation materials," the voice reception unit analyzes the request and conveys it to the generation unit. Step 4: The providing unit provides the data generated by the generating unit to the user. The providing unit provides the data in a format desired by the user. For example, the providing unit can provide the data in PDF format, Word format, HTML format, etc.

[0059] (Example 2) The document creation support system according to an embodiment of the present invention is a system in which a generating AI instantly creates 200 patterns of documents simply by the user inputting text, and also accepts voice input. This document creation support system instantly creates 200 patterns of documents simply by the user inputting text. Regarding the structure of the documents, the generating AI automatically processes the data based on the user's chosen image, such as black aesthetic, pop art, or antique style. Users can also input their preferences by voice. For example, if a user voice-inputs "I want a presentation document," the generating AI will create the document based on that request. This service significantly reduces the time required for document creation, allowing for more efficient work. First, the user inputs text. At this time, the user specifically inputs the content of the desired document. For example, they might input "I want a sales report." This information is input to the generating AI. Next, the generating AI analyzes the input information and generates 200 patterns of documents. The generating AI generates the optimal document based on past data and algorithms. For example, if a user requests a sales report, various styles of reports will be generated based on that content. The generated documents are processed to match the image desired by the user. For example, by setting an image such as "black aesthetic," "POP," or "antique," the generating AI automatically processes the materials. Furthermore, users can input their preferences by voice. For example, if a user voice-inputs "I want you to create a presentation," the generating AI will create materials based on that preference. In this process, speech recognition technology and natural language processing technology are used to accurately analyze the user's preferences. The generated materials are provided to the user. For example, the materials are provided in the format the user requests. This allows the user to use the generated materials immediately. This service significantly reduces the time required to create materials and enables efficient work. Thus, the material creation support system significantly reduces the time users spend creating materials and enables efficient work.

[0060] The document creation support system according to this embodiment comprises a reception unit, a generation unit, a voice reception unit, and a provision unit. The reception unit is the part where the user inputs text. The text input by the user includes, but is not limited to, business documents, technical documents, and creative writing. The reception unit can accept text by methods such as keyboard input, touch input, and voice input. The generation unit is the part that generates documents based on the text input by the reception unit using a generation AI. The generation unit, for example, generates 200 patterns of documents using the generation AI. The generation AI generates the optimal document based on past data and algorithms. For example, when generating a business document, the generation AI refers to data from past business documents and generates the document in the optimal format. The generation unit can also process documents by setting images such as black taste, POP, and old-fashioned. For example, the generation AI adjusts the design and layout of the document to match the image desired by the user. The voice reception unit is the part where the user inputs their wishes by voice. The voice reception unit analyzes the user's wishes using speech recognition technology and natural language processing technology. For example, when a user voice-inputs a request such as "I want you to create a presentation," the voice reception unit analyzes the request and transmits it to the generation unit. The delivery unit is responsible for providing the user with the materials generated by the generation unit. The delivery unit provides the materials in the format the user desires. For example, the delivery unit can provide materials in PDF, Word, HTML, etc. As a result, the document creation support system according to this embodiment can significantly reduce the time users spend creating documents and allow them to work more efficiently.

[0061] The generation unit can generate 200 patterns of materials using a generation AI. The generation unit generates 200 patterns of materials using, for example, a generation AI. The generation AI generates optimal materials based on past data and algorithms. For example, when generating a business document, the generation AI references data from past business documents and generates materials in the optimal format. The generation AI can also adjust the design and layout of the materials to match the image desired by the user. This allows the generation AI to generate a variety of materials. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI, for example. For example, the generation unit can generate 200 patterns of materials based on data input to the generation AI.

[0062] The generation unit can process materials by setting a black theme, POP, or old-fashioned image. The generation unit processes materials by setting, for example, a black theme, POP, or old-fashioned image. For example, the generation AI adjusts the design and layout of the materials to match the image desired by the user. Black theme materials, for example, have a chic design based on black and are suitable for business documents and presentation materials. POP materials, for example, use bright colors and a pop design and are suitable for advertising and marketing materials. Old-fashioned materials, for example, use traditional designs and fonts and are suitable for historical documents and cultural materials. This allows the materials to be processed to match the image desired by the user. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can process materials to match the image desired by the user based on data input to the generation AI.

[0063] The voice receiving unit can analyze the user's wishes using voice recognition technology or natural language processing technology. The voice receiving unit analyzes the user's wishes using, for example, voice recognition technology. Voice recognition technologies include, for example, deep learning and HMM (hidden Markov model). Deep learning is a technology that learns from large amounts of voice data to achieve highly accurate voice recognition. HMM is a technology that models temporal changes in voice and performs voice recognition. The voice receiving unit can also analyze the user's wishes using natural language processing technology. Natural language processing technologies include, for example, morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis is a technology that divides sentences into words and analyzes the parts of speech of each word. Grammatical analysis is a technology that analyzes the grammatical structure of sentences and understands their meaning. Semantic analysis is a technology that analyzes the meaning of sentences and understands the user's intent. This makes it possible to analyze voice input and generate materials based on the user's wishes. Some or all of the processing described above in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can use speech recognition technology or natural language processing technology to analyze the user's wishes and transmit them to the generation unit.

[0064] The providing unit can provide materials in a format desired by the user. The providing unit provides materials in, for example, a format desired by the user. Formats desired by the user include, for example, PDF format, Word format, and HTML format. The PDF format preserves the layout and design of the document and allows for consistent display across different devices and platforms. The Word format makes it easy to edit and modify documents and is suitable for collaborative work. The HTML format is a format that can be displayed in a web browser and is suitable for sharing and publishing on the Internet. This allows materials to be provided in a format desired by the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide materials generated by the generating unit in a format desired by the user.

[0065] The generation unit can generate appropriate materials based on past data and algorithms. For example, the generation unit generates appropriate materials based on past data and algorithms. Past data includes, for example, past project data and user history data. Past project data refers to materials and project deliverables created in the past, and the generation AI refers to this data to generate the optimal materials. User history data refers to materials created and templates used by the user in the past, and the generation AI uses this data to generate materials tailored to the user's preferences. Algorithms include, for example, machine learning algorithms and optimization algorithms. Machine learning algorithms are techniques for learning from large amounts of data and finding patterns and trends. Optimization algorithms are techniques for finding the optimal solution within given conditions and constraints. This allows for the generation of optimal materials based on past data and algorithms. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without using the generation AI. For example, the generation unit can generate materials desired by the user based on past data and algorithms.

[0066] The reception desk can estimate the user's emotions and adjust the timing of text input based on those emotions. For example, the reception desk can estimate the user's emotions and adjust the timing of text input based on those emotions. Methods for estimating user emotions include, for example, facial recognition, speech analysis, and text analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera and estimates their emotions. Speech analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. Text analysis is a technology that analyzes the content of the text entered by the user and estimates their emotions. For example, if the user is stressed, the reception desk can delay the input timing to help them relax. If the user is relaxed, the reception desk can speed up the input timing to allow for more efficient input. Furthermore, if the user is in a hurry, the reception desk can optimize the input timing to allow for quick completion. This allows for more appropriate input by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0067] The reception unit can analyze the user's past input history and select the optimal input method. The reception unit, for example, analyzes the user's past input history and selects the optimal input method. The past input history includes, for example, input content, input frequency, and input pattern. The input content refers to the content of sentences entered by the user in the past, and the reception unit analyzes it to select the optimal input method. The input frequency indicates how frequently the user uses a particular input method, and the reception unit suggests the optimal input method based on this. The input pattern indicates what input method the user uses in a particular time period or situation, and the reception unit selects the optimal input method based on this. For example, the reception unit prioritizes suggesting input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a particular time period based on the user's past input history. Furthermore, the reception unit can suggest similar input methods based on content entered by the user in the past. This enables efficient input by suggesting the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input past input history into the generation AI and have the generation AI select the optimal input method.

[0068] The input field can filter text input based on the user's current projects and areas of interest. For example, the input field can filter text input based on the user's current projects and areas of interest. Current projects include, for example, ongoing projects and tasks. Areas of interest include, for example, topics and themes that the user is interested in. For example, it can prioritize displaying keywords related to the user's current projects. It can also filter and display relevant information based on the user's areas of interest. Furthermore, it can filter input based on topics the user has shown interest in in the past. This enables efficient input by prioritizing the display of relevant information based on the user's projects and areas of interest. Some or all of the above processing in the input field may be performed using, for example, AI, or not using AI. For example, the input field can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0069] The reception desk can estimate the user's emotions and determine the priority of the text to be entered based on those emotions. For example, the reception desk can estimate the user's emotions and determine the priority of the text to be entered based on those emotions. Methods for estimating user emotions include, for example, facial recognition, speech analysis, and text analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera and estimates their emotions. Speech analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. Text analysis is a technology that analyzes the content of the text entered by the user and estimates their emotions. For example, if the user is stressed, the reception desk will postpone less important text. Conversely, if the user is relaxed, the reception desk can prioritize the input of more important text. Furthermore, if the user is in a hurry, the reception desk can prioritize the input of the most important text. This allows for efficient input by determining the priority of text according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0070] The reception unit can prioritize inputting highly relevant text based on the user's geographical location information when text is entered. For example, when text is entered, the reception unit prioritizes inputting highly relevant text based on the user's geographical location information. Geographical location information includes, for example, GPS data and location services. GPS data is data that indicates the user's current location, and the reception unit displays relevant information based on this. Location services are services that identify the user's location, and the reception unit uses this to display relevant information. For example, if the user is in a specific region, information related to that region is prioritized for input. Also, if the user is traveling, information related to the travel destination can be prioritized for input. Furthermore, if the user is at home, information related to home can be prioritized for input. This enables efficient input by prioritizing the input of relevant information based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI prioritize the relevant information.

[0071] The reception unit can analyze the user's social media activity and input relevant text when text is entered. For example, the reception unit analyzes the user's social media activity and inputs relevant text when text is entered. Social media activity includes, for example, posts, the number of likes, and the number of followers. Posts refer to the content that the user has posted on social media, and the reception unit analyzes this and displays relevant information. The number of likes refers to the number of likes the user has given to other posts, and the reception unit displays relevant information based on this. The number of followers refers to the number of followers the user has, and the reception unit displays relevant information based on this. For example, keywords that the user frequently uses on social media are prioritized for input. It can also input relevant information based on the user's social media activity. Furthermore, it can determine the input content based on the topics the user follows on social media. This enables efficient input by inputting relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input data on the user's social media activity into a generating AI and have the generating AI input relevant information.

[0072] The generation unit can estimate the user's emotions and adjust the material generation method based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the material generation method based on the estimated user emotions. Methods for estimating user emotions include, for example, facial expression recognition, voice analysis, and text analysis. Facial expression recognition is a technology that analyzes the user's facial expressions captured with a camera to estimate emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice to estimate emotions. Text analysis is a technology that analyzes the content of text entered by the user to estimate emotions. For example, if the user is relaxed, the generation unit can generate detailed materials. If the user is in a hurry, the generation unit can generate concise materials. Furthermore, if the user is excited, the generation unit can generate visually appealing materials. This allows for more appropriate materials to be generated by adjusting the material generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0073] The generation unit can adjust the level of detail of the generated document based on the importance of the sentence when generating the document. For example, the generation unit adjusts the level of detail of the generated document based on the importance of the sentence when generating the document. Methods for evaluating the importance of a sentence include, for example, the frequency of keyword appearance and the importance of the context. The frequency of keyword appearance indicates how frequently a particular keyword appears in a sentence, and the generation unit adjusts the level of detail based on this. The importance of the context indicates how important a particular part is in the context of the sentence, and the generation unit adjusts the level of detail based on this. For example, detailed explanations can be added to sentences with high importance. Concise explanations can also be added to sentences with low importance. Furthermore, the layout and design of the document can be adjusted according to the importance. This enables efficient document generation by adjusting the level of detail of the generated document based on the importance of the sentence. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the sentence into the generation AI and have the generation AI adjust the level of detail.

[0074] The generation unit can apply different generation algorithms depending on the category of text when generating materials. For example, the generation unit applies different generation algorithms depending on the category of text when generating materials. Text categories include, for example, technical documents, business documents, and creative documents. Technical documents are documents containing technical content, and the generation unit applies a generation algorithm suitable for them. Business documents are documents containing business-related content, and the generation unit applies a generation algorithm suitable for them. Creative documents are documents containing creative content, and the generation unit applies a generation algorithm suitable for them. For example, a business-oriented generation algorithm can be applied to business documents. Also, an academic generation algorithm can be applied to academic papers. Furthermore, a presentation-oriented generation algorithm can be applied to presentation materials. This enables efficient document generation by applying an appropriate generation algorithm depending on the category of text. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the category of text into the generation AI and have the generation AI apply the generation algorithm.

[0075] The generation unit can estimate the user's emotions and adjust the length of the generated material based on the estimated emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the generated material based on the estimated emotions. Methods for estimating user emotions include, for example, facial recognition, speech analysis, and text analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera and estimates their emotions. Speech analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. Text analysis is a technology that analyzes the content of text entered by the user and estimates their emotions. For example, if the user is in a hurry, the generation unit can generate short, concise material. If the user is relaxed, the generation unit can generate longer material with detailed explanations. Furthermore, if the user is excited, the generation unit can generate material with visually stimulating effects. This allows for the generation of more appropriate material by adjusting the length of the material according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0076] The generation unit can determine the generation priority based on the submission date of the document when generating the document. For example, the generation unit determines the generation priority based on the submission date of the document when generating the document. The submission date includes, for example, a deadline date, a project progress status, etc. The deadline date refers to the date by which the document is required to be submitted, and the generation unit determines the priority based on this. The project progress status indicates the progress of the project, and the generation unit determines the priority based on this. For example, documents with an approaching deadline can be generated with priority. Also, documents with a more distant submission date can be postponed. Furthermore, the level of detail of the generation can be adjusted depending on the submission date. This enables efficient document generation by determining the generation priority based on the submission date. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the submission date into the generation AI and have the generation AI determine the priority.

[0077] The generation unit can adjust the order of generation based on the relevance of the sentences when generating materials. For example, the generation unit adjusts the order of generation based on the relevance of the sentences when generating materials. Methods for evaluating the relevance of sentences include, for example, keyword consistency and context consistency. Keyword consistency indicates the degree to which a specific keyword matches within a sentence, and the generation unit adjusts the order based on this. Context consistency indicates the degree to which a specific part matches within the context of the sentence, and the generation unit adjusts the order based on this. For example, highly relevant sentences are generated preferentially. Less relevant sentences can also be postponed. Furthermore, the level of detail of generation can be adjusted according to the relevance. This enables efficient material generation by adjusting the order of generation based on the relevance of the sentences. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the sentences into the generation AI and have the generation AI adjust the order.

[0078] The voice receiving unit can estimate the user's emotions and adjust the analysis method of the voice input based on the estimated user emotions. The voice receiving unit, for example, estimates the user's emotions and adjusts the analysis method of the voice input based on the estimated user emotions. Methods for estimating user emotions include, for example, facial expression recognition, voice analysis, and text analysis. Facial expression recognition is a technology that analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice to estimate emotions. Text analysis is a technology that analyzes the content of text entered by the user to estimate emotions. For example, if the user is nervous, the voice receiving unit analyzes the voice input at a slow pace. On the other hand, if the user is relaxed, the voice receiving unit can analyze the voice input at a normal pace. Furthermore, if the user is in a hurry, the voice receiving unit can analyze the voice input quickly. This allows for more appropriate analysis by adjusting the analysis method of the voice input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the voice receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice receiving unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0079] The voice input unit can select the optimal analysis method by referring to the user's past voice input history during voice input. For example, the voice input unit selects the optimal analysis method by referring to the user's past voice input history during voice input. Past voice input history includes, for example, the content of the voice, the frequency of input, and the input pattern. The content of the voice refers to the content of the voice input that the user has previously entered, and the voice input unit analyzes this to select the optimal analysis method. The frequency of input indicates how often the user uses a particular voice input method, and the voice input unit proposes the optimal analysis method based on this. The input pattern indicates what kind of voice input method the user uses at a particular time or in a particular situation, and the voice input unit selects the optimal analysis method based on this. For example, it prioritizes analyzing voice input methods that the user has used in the past. It can also predict and propose analysis methods to be used at a particular time based on the user's past voice input history. Furthermore, it can propose similar analysis methods by referring to the content of the voice input that the user has previously entered. This enables efficient analysis by proposing the optimal analysis method based on past voice input history. Some or all of the above-described processing in the voice reception unit may be performed using AI, for example, or without AI. For example, the voice reception unit can input past voice input history into a generating AI and have the generating AI select the optimal analysis method.

[0080] The voice accepting unit can improve the accuracy of analysis based on the user's speaking rate and volume when voice is input. For example, the voice accepting unit improves the accuracy of analysis based on the user's speaking rate and volume when voice is input. Speaking rate refers to the speed at which the user speaks, and the voice accepting unit adjusts the accuracy of analysis based on this. Volume refers to the volume at which the user speaks, and the voice accepting unit adjusts the accuracy of analysis based on this. For example, if the user speaks slowly, the voice accepting unit adjusts the accuracy of analysis to match the speaking rate. Also, if the user speaks loudly, the voice accepting unit can adjust the accuracy of analysis to match the volume. Furthermore, if the user speaks quickly, the voice accepting unit can improve the accuracy of analysis to match the speaking rate. This improves the accuracy of analysis based on the user's speaking rate and volume, enabling more accurate analysis. Some or all of the above-described processing in the voice accepting unit may be performed using, for example, AI, or may be performed without AI. For example, the voice accepting unit can input data on the user's speaking rate and volume to the generation AI and cause the generation AI to improve the accuracy of analysis.

[0081] The voice reception unit can estimate the user's emotions and prioritize voice input based on those emotions. For example, the voice reception unit can estimate the user's emotions and prioritize voice input based on those emotions. Methods for estimating user emotions include, for example, facial recognition, voice analysis, and text analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. Text analysis is a technology that analyzes the content of text entered by the user and estimates their emotions. For example, if the user is stressed, the voice reception unit will postpone less important voice input. Conversely, if the user is relaxed, the voice reception unit can prioritize analyzing more important voice input. Furthermore, if the user is in a hurry, the voice reception unit can prioritize analyzing the most important voice input. This allows for efficient analysis by prioritizing voice input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the voice receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice receiving unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0082] The voice reception unit can prioritize the analysis of highly relevant voice data when voice input is received, taking into account the user's geographical location information. For example, when voice input is received, the voice reception unit prioritizes the analysis of highly relevant voice data, taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data is data that indicates the user's current location, and the voice reception unit displays relevant information based on this. Location information services are services that identify the user's location, and the voice reception unit uses this to display relevant information. For example, if the user is in a specific region, information related to that region is prioritized for analysis. Also, if the user is traveling, information related to the travel destination can be prioritized for analysis. Furthermore, if the user is at home, information related to home can be prioritized for analysis. This enables efficient analysis by prioritizing the analysis of relevant information based on the user's geographical location information. Some or all of the above processing in the voice reception unit may be performed using, for example, AI, or not using AI. For example, the voice reception unit can input the user's geographical location information into a generating AI and have the generating AI prioritize the relevant information.

[0083] The voice reception unit can analyze the user's social media activity and analyze relevant audio when voice input is received. For example, the voice reception unit analyzes the user's social media activity and analyzes relevant audio when voice input is received. Social media activity includes, for example, posts, the number of likes, and the number of followers. Posts refer to the content that the user has posted on social media, and the voice reception unit analyzes this and displays relevant information. The number of likes refers to the number of likes the user has given to other posts, and the voice reception unit displays relevant information based on this. The number of followers refers to the number of followers the user has, and the voice reception unit displays relevant information based on this. For example, it prioritizes analyzing keywords that the user frequently uses on social media. It can also analyze relevant information based on the user's social media activity. Furthermore, it can analyze audio content based on the topics the user follows on social media. This enables efficient analysis by analyzing relevant information based on the user's social media activity. Some or all of the above processing in the voice reception unit may be performed using, for example, AI, or not using AI. For example, the voice reception unit can input data on the user's social media activity into a generating AI, which can then perform analysis of the relevant information.

[0084] The service provider can estimate the user's emotions and adjust the method of providing materials based on those estimated emotions. For example, the service provider can estimate the user's emotions and adjust the method of providing materials based on those estimated emotions. Methods for estimating user emotions include, for example, facial recognition, voice analysis, and text analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. Text analysis is a technology that analyzes the content of text entered by the user and estimates their emotions. For example, if the user is relaxed, the service provider can provide detailed materials. If the user is in a hurry, the service provider can provide concise materials. Furthermore, if the user is excited, the service provider can provide visually appealing materials. This allows for the provision of more appropriate materials by adjusting the method of providing materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the service provider may be performed using AI, or not using AI. For example, the service provider can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0085] The providing unit can select the optimal delivery method by referring to the user's past material usage history when providing materials. For example, when providing materials, the providing unit selects the optimal delivery method by referring to the user's past material usage history. The past material usage history includes, for example, usage frequency, usage content, and usage pattern. Usage frequency indicates how frequently a user uses a particular material format, and the providing unit proposes the optimal delivery method based on this. Usage content refers to the content of materials used by the user in the past, and the providing unit analyzes this to select the optimal delivery method. Usage pattern indicates what material format a user uses in a particular time period or situation, and the providing unit selects the optimal delivery method based on this. For example, the providing unit may prioritize the delivery of material formats used by the user in the past. Furthermore, the providing unit may predict and propose a delivery method to be used in a particular time period based on the user's past material usage history. Furthermore, the providing unit may propose a similar delivery method by referring to the content of materials used by the user in the past. This enables efficient material delivery by proposing the optimal delivery method based on the user's past material usage history. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the provision department can input past material usage history into the generation AI and have the generation AI select the optimal provision method.

[0086] The service provider can customize the content provided based on the user's current projects and areas of interest when providing materials. For example, the service provider can customize the content provided based on the user's current projects and areas of interest when providing materials. Current projects include, for example, ongoing projects and tasks. Areas of interest include, for example, topics and themes that the user is interested in. For example, it can prioritize providing materials related to the user's current projects. It can also provide relevant materials based on the user's areas of interest. Furthermore, it can customize the content provided based on topics the user has shown interest in in the past. This enables efficient material provision by providing relevant materials based on the user's projects and areas of interest. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the customization of the content provided.

[0087] The service provider can estimate the user's emotions and determine the order in which materials are provided based on those emotions. For example, the service provider can estimate the user's emotions and determine the order in which materials are provided based on those emotions. Methods for estimating user emotions include, for example, facial recognition, voice analysis, and text analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. Text analysis is a technology that analyzes the content of text entered by the user and estimates their emotions. For example, if the user is stressed, the service provider will postpone providing less important materials. Conversely, if the user is relaxed, the service provider can prioritize providing more important materials. Furthermore, if the user is in a hurry, the service provider can prioritize providing the most important materials. This allows for efficient material provision by determining the order in which materials are provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the service provider may be performed using AI, or not using AI. For example, the service provider can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0088] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing materials. For example, when providing materials, the providing unit selects the optimal delivery method by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data is data indicating the user's current location, and the providing unit displays related information based on this data. Location information services are services for identifying the user's location, and the providing unit uses this data to display related information. For example, if the user is in a specific area, materials related to that area can be provided preferentially. Furthermore, if the user is traveling, materials related to the user's travel destination can be provided preferentially. Furthermore, if the user is at home, materials related to the user's home can be provided preferentially. This enables efficient material provision by providing related materials based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's geographical location information into a generation AI and cause the generation AI to select the optimal delivery method.

[0089] The providing unit can analyze the user's social media activity and provide related materials when providing the materials. For example, the providing unit can analyze the user's social media activity and provide related materials when providing the materials. Social media activity includes, for example, post content, the number of likes, and the number of followers. Post content refers to content posted by the user on social media, and the providing unit analyzes the content and displays related information. The number of likes refers to the number of likes the user has given to other posts, and the providing unit displays related information based on the number of likes. The number of followers refers to the number of followers the user has, and the providing unit displays related information based on the number of likes. For example, keywords frequently used by the user on social media can be provided preferentially. Related information can also be provided based on the user's social media activity. Furthermore, material content can be provided based on topics the user follows on social media. This enables efficient material provision by providing related materials based on the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input data on the user's social media activity into a generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, generation unit, voice reception unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives user input using a touch panel 38A or a microphone 38B of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates materials using a generation AI. The voice reception unit receives user voice input using, for example, the microphone 38B of the smart device 14 and analyzes the input using voice recognition technology or natural language processing technology. The provision unit provides the generated materials to the user using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, voice reception unit, and provision 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 receives user input using the microphone 238 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates materials using a generation AI. For example, the voice reception unit receives user voice input using the microphone 238 of the smart glasses 214 and analyzes the voice using voice recognition technology or natural language processing technology. The provision unit provides the generated materials to the user using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, voice reception unit, and provision 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 receives user input using the microphone 238 of the headset-type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates materials using a generation AI. For example, the voice reception unit receives user voice input using the microphone 238 of the headset-type terminal 314, and analyzes the voice input using voice recognition technology or natural language processing technology. The provision unit provides the generated materials to the user using, for example, the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, voice reception unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates materials using a generation AI. For example, the voice reception unit receives user voice input using the microphone 238 of the robot 414 and analyzes it using voice recognition technology or natural language processing technology. The provision unit provides the generated materials to the user using, for example, the speaker 240 of the robot 414.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can automatically search for and display related reference materials based on the user's input. For example, when a user creates a "sales report," past sales reports and related statistical data can be automatically displayed. When a user creates a "technical document," related technical papers and patent information can be displayed. Furthermore, when a user creates a "presentation document," past presentation documents and related visual materials can be displayed. This allows users to quickly obtain the information they need and create documents efficiently.

[0092] The generation unit can automatically select and apply a document template based on the user's input. For example, when a user creates a "sales report," it can automatically select and apply the most suitable sales report template. Similarly, when a user creates a "technical document," it can select and apply a template appropriate for a technical document. Furthermore, when a user creates "presentation materials," it can select and apply a template appropriate for presentation materials. This eliminates the need for the user to manually select templates, allowing them to create documents efficiently.

[0093] The generation unit can automatically summarize the content of documents and extract key points based on user input. For example, when a user creates a "sales report," it can automatically summarize the lengthy content and extract the important points. Similarly, when a user creates a "technical document," it can summarize the technical details and extract the main conclusions. Furthermore, when a user creates "presentation materials," it can automatically extract the key points of the presentation and reflect them in the slides. This allows users to efficiently create documents and effectively communicate important information.

[0094] The voice input unit can automatically generate and present relevant questions based on the user's voice input. For example, when a user is creating a "sales report," it can automatically generate and present relevant questions (e.g., "What are this month's sales?"). When a user is creating a "technical document," it can generate and present questions about technical details (e.g., "What technologies were used?"). Furthermore, when a user is creating "presentation materials," it can generate and present questions about the key points of the presentation (e.g., "What are the main messages?"). This allows users to efficiently input the necessary information and create documents.

[0095] The providing unit can suggest the optimal document format based on the user's document usage history. For example, if the user has frequently used PDF documents in the past, the providing unit can suggest the PDF format. Also, if the user has frequently used Word documents in the past, the providing unit can suggest the Word format. Furthermore, if the user has used HTML documents in the past, the providing unit can suggest the HTML format. This allows the user to receive documents in the optimal format and use them efficiently.

[0096] The generation unit can estimate the user's emotions and adjust the design of the materials based on those emotions. For example, if the user is relaxed, the generation unit can generate materials with a calm design. If the user is excited, the generation unit can generate materials with a visually stimulating design. Furthermore, if the user is stressed, the generation unit can generate materials with a simple and easy-to-read design. By providing materials with designs that match the user's emotions, more effective communication becomes possible.

[0097] The reception unit can estimate the user's emotions and provide feedback on the input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can display an encouraging message. If the user is relaxed, the reception unit can provide positive feedback. Furthermore, if the user is in a hurry, the reception unit can suggest an efficient input method. In this way, by providing feedback according to the user's emotions, a more comfortable input environment can be realized.

[0098] The generation unit can estimate the user's emotions and adjust the content of the materials based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate materials including detailed explanations. If the user is in a hurry, the generation unit can generate materials including a concise summary. Furthermore, if the user is excited, the generation unit can generate materials that make extensive use of visually appealing graphics. This allows for more effective information transmission by providing materials with content that matches the user's emotions.

[0099] The providing unit can estimate the user's emotions and adjust the timing of providing materials based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can slightly delay the provision of materials to give the user time to relax. Also, if the user is relaxed, the providing unit can provide materials quickly. Furthermore, if the user is in a hurry, the providing unit can provide the most important materials with priority. This allows for more effective use by providing materials at a timing that suits the user's emotions.

[0100] The service provider can estimate the user's emotions and adjust the format of the materials based on those emotions. For example, if the user is relaxed, the service provider can provide materials in a detailed format. If the user is in a hurry, the service provider can provide materials in a concise format. Furthermore, if the user is excited, the service provider can provide materials in a visually appealing format. By providing materials in a format that matches the user's emotions, more effective information transmission becomes possible.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit is a section where the user inputs text. Texts input by the user include business documents, technical documents, creative documents, etc. The reception unit can accept text by keyboard input, touch input, voice input, etc. Step 2: The generation unit uses the generation AI to generate materials based on the text entered by the reception unit. In the generation unit, the generation AI generates 200 patterns of materials. The generation AI generates optimal materials based on past data and algorithms. For example, when generating a business document, the generation AI references data from past business documents and generates materials in the optimal format. The generation unit can also process the materials by setting images such as black taste, POP, and old-fashioned. For example, the generation AI adjusts the design and layout of the materials to match the image desired by the user. Step 3: The voice reception unit is the part where the user can input their requests by voice. The voice reception unit analyzes the user's requests using voice recognition technology and natural language processing technology. For example, if the user inputs by voice, "I want you to create presentation materials," the voice reception unit analyzes the request and conveys it to the generation unit. Step 4: The providing unit provides the data generated by the generating unit to the user. The providing unit provides the data in a format desired by the user. For example, the providing unit can provide the data in PDF format, Word format, HTML format, etc.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 section for inputting text; a generation unit that generates a document based on the text input by the reception unit; a voice receiving unit that receives voice input; a providing unit that provides the material generated by the generating unit; Equipped with A system characterized by:

2. The generation unit Generate 200 patterns of materials using generative AI The system of claim 1 .

3. The generation unit Edit your materials by setting a black taste, POP, or old-fashioned image. The system of claim 1 .

4. The voice receiving unit Analyze user preferences using voice recognition or natural language processing technology The system of claim 1 .

5. The providing unit Provide materials in the format you prefer The system of claim 1 .

6. The generation unit Generate appropriate materials based on past data and algorithms The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the timing of sentence input based on the estimated user emotions. The system of claim 1 .

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

9. The reception unit Filtering based on your current projects and interests as you type The system of claim 1 .

10. The reception unit Estimate the user's emotions and prioritize the sentences to be input based on the estimated user emotions. The system of claim 1 .

Images