System

Abstract

An object of the system according to the embodiment is to efficiently generate a grammatically correct sentence from a word or a short phrase.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives an input of a word or a short phrase. The analysis unit analyzes the input received by the reception unit. The generation unit generates a grammatically correct sentence based on the result analyzed by the analysis unit. The providing unit provides the sentence generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that inputting characters takes time, making it difficult to create text efficiently.

[0005] The system according to the embodiment aims to efficiently generate grammatically correct sentences from words and short phrases. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit receives input of a word or short phrase. The analyzing unit analyzes the input received by the receiving unit. The generating unit generates grammatically correct sentences based on the results of the analysis by the analyzing unit. The providing unit provides the sentences generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate grammatically correct sentences from words and short phrases. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A character input assistance system according to an embodiment of the present invention reduces the burden of character input by accepting input of words or short phrases and carefully converting them using a generation AI. In the character input assistance system, a user inputs words or short phrases, and a generation AI analyzes the input and generates grammatically correct sentences, which are then provided to the user. For example, if a user inputs "Meeting at 10 o'clock," the generation AI generates a grammatically correct sentence such as "There is a meeting at 10 o'clock." This allows users to obtain grammatically correct sentences with simple input. This significantly reduces the burden of character input and enables efficient communication. For example, users do not need to input complex sentences; instead, they can obtain grammatically correct sentences simply by inputting simple words or phrases.

[0029] A character input assistance system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives words or short phrases input by a user. For example, the user inputs "meeting at 10 o'clock." The analysis unit analyzes the input received by the reception unit based on the context. For example, the analysis unit analyzes the input "meeting at 10 o'clock" and identifies an appropriate grammatical structure. The generation unit uses a generation AI to generate grammatically correct sentences based on the results of the analysis by the analysis unit. For example, the generation unit generates grammatically correct sentences such as "I have a meeting at 10 o'clock." The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the sentences generated by the generation unit to a user. For example, the provision unit displays the generated sentences on the user's device. In this way, the character input assistance system according to an embodiment can convert input of words or short phrases into grammatically correct sentences, thereby reducing the burden of character input.

[0030] The reception unit can analyze the user's past input history and select an appropriate input reception method. For example, the reception unit preferentially selects an input method (such as voice or text) that the user has frequently used in the past. The reception unit can also predict and select an input method to be used during a specific time period based on the user's past input history. The reception unit can also analyze the user's past input history and select the most efficient input method. This allows for the selection of the optimal input reception method based on the user's past input history, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0031] The reception unit can filter inputs based on the user's current task or area of ​​interest when receiving the input. For example, the reception unit preferentially receives words and phrases related to the task the user is currently performing. The reception unit can also filter and receive related words and phrases based on the user's area of ​​interest. The reception unit can also analyze the user's current task or area of ​​interest and filter and receive the most appropriate input. In this way, by filtering the inputs based on the user's current task or area of ​​interest, it is possible to preferentially receive highly relevant inputs. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0032] When receiving input, the reception unit can select an appropriate reception means depending on the user's input method. For example, if the user uses voice input, the reception unit can use voice recognition technology to receive the input. Furthermore, if the user uses text input, the reception unit can also preferentially receive keyboard input. Furthermore, if the user uses image input, the reception unit can also use image recognition technology to receive the input. This allows the most appropriate reception means to be selected depending on the user's input method, thereby improving the accuracy and efficiency of input. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0033] When receiving input, the reception unit can prioritize receiving highly relevant inputs in consideration of the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes receiving inputs related to that location. The reception unit can also prioritize receiving related words and phrases based on the user's current location. The reception unit can also analyze the user's geographical location information and prioritize receiving optimal inputs. This can improve the accuracy of inputs by preferentially receiving highly relevant inputs based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0034] The reception unit can analyze the user's social media activity and receive related input when receiving input. For example, the reception unit can prioritize receiving words and phrases that the user frequently uses on social media. The reception unit can also analyze the content of the user's posts on social media and prioritize receiving related input. The reception unit can also prioritize receiving related input by referring to the activities of the user's friends on social media. In this way, the accuracy of the input can be improved by receiving related input based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0035] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. The reception unit customizes the optimal input reception method, for example, based on feedback provided by the user in the past. The reception unit can also preferentially accept a specific input method based on the user's past feedback. The reception unit can also analyze the user's feedback and customize the most efficient input reception method. In this way, input efficiency can be improved by customizing the reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0036] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during analysis. For example, the analysis unit performs a detailed analysis on an input with high importance. The analysis unit can also perform a simplified analysis on an input with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the input. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the input. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0037] The analysis unit can apply different analysis algorithms depending on the category of input during analysis. For example, the analysis unit applies a natural language processing algorithm to text input. The analysis unit can also apply an image recognition algorithm to image input. The analysis unit can also apply a voice recognition algorithm to voice input. This allows the analysis accuracy to be improved by applying the optimal analysis algorithm depending on the category of input. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the current analysis, for example, based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and select the optimal analysis algorithm. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. This makes it possible to provide more accurate analysis results by improving the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without using AI.

[0039] During analysis, the analysis unit can determine the analysis priority based on the time of input submission. For example, the analysis unit prioritizes analysis of input submitted earlier. The analysis unit can also postpone analysis of input submitted later. The analysis unit can also dynamically adjust the analysis priority based on the time of submission. This enables efficient analysis by determining the analysis priority based on the time of input submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The analysis unit can adjust the order of analysis based on the relevance of the inputs during analysis. For example, the analysis unit prioritizes analysis of highly relevant inputs. The analysis unit can also postpone analysis of less relevant inputs. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the inputs. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the inputs. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit uses a lot of technical terms. Also, if the user's level of expertise is low, the analysis unit can avoid technical terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This can facilitate understanding of the analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0042] The generation unit can adjust the level of detail of the sentence to be generated based on the importance of the analysis result during generation. For example, the generation unit generates detailed sentences for analysis results with high importance. The generation unit can also generate simplified sentences for analysis results with low importance. The generation unit can also dynamically adjust the level of detail of the sentence to be generated according to the importance of the analysis result. This enables efficient sentence generation by adjusting the level of detail of the sentence to be generated according to the importance of the analysis result. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

[0043] The generation unit can apply different generation algorithms depending on the category of the analysis results during generation. For example, the generation unit applies a natural language generation algorithm to text analysis results. The generation unit can also apply an image generation algorithm to image analysis results. The generation unit can also apply a voice generation algorithm to voice analysis results. This allows the application of an optimal generation algorithm depending on the category of the analysis results, thereby improving the accuracy of the generated sentences. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0044] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit can improve the accuracy of current generation, for example, based on the user's past generation results. The generation unit can also analyze the user's past generation results and select the optimal generation algorithm. The generation unit can also adjust the level of detail of generation by referring to the user's past generation results. This makes it possible to provide more accurate sentences by improving the accuracy of generation based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI or without using AI.

[0045] At the time of generation, the generation unit can determine the priority of sentences to be generated based on the submission time of the analysis results. For example, the generation unit can generate sentences preferentially for analysis results that are submitted early. The generation unit can also postpone the generation of sentences for analysis results that are submitted late. The generation unit can also dynamically adjust the priority of sentences to be generated based on the submission time. This enables efficient sentence generation by determining the priority of sentences to be generated based on the submission time of the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0046] The generation unit can adjust the order of sentences to be generated based on the relevance of the analysis results during generation. For example, the generation unit prioritizes generating sentences for analysis results with high relevance. The generation unit can also postpone generating sentences for analysis results with low relevance. The generation unit can also dynamically adjust the order of sentences to be generated based on the relevance of the analysis results. This enables efficient sentence generation by adjusting the order of sentences to be generated based on the relevance of the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0047] The generation unit can adjust the use of technical terms in the generated sentences according to the user's level of expertise during generation. For example, if the user's level of expertise is high, the generation unit uses a lot of technical terms. Also, if the user's level of expertise is low, the generation unit can avoid technical terms. The generation unit can also dynamically adjust the use of technical terms in the generated sentences according to the user's level of expertise. In this way, by adjusting the use of technical terms in the generated sentences according to the user's level of expertise, it is possible to provide sentences that are easy for the user to understand. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0048] The providing unit can select the optimal display method by referring to the user's past operation history when providing the display. For example, the providing unit preferentially provides a display method that the user has used favorably in the past. The providing unit can also select a specific display method from the user's past operation history. The providing unit can also analyze the user's past operation history and select the most efficient display method. In this way, by selecting the optimal display method based on the user's past operation history, it is possible to provide a display that is easy for the user to use. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0049] The providing unit can customize the display content according to the user's current task when providing the information. For example, the providing unit prioritizes displaying information related to the task the user is currently performing. The providing unit can also customize and display related information based on the user's current task. The providing unit can also analyze the user's current task and customize optimal display content. In this way, highly relevant information can be provided by customizing the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0050] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, by selecting the optimal display method based on the user's device information, it is possible to provide a display that is highly visible to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0051] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This makes it possible to provide a display that is easy for the user to understand by making the display content multilingual based on the user's language setting. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0052] At the time of providing the information, the providing unit can analyze the user's social media activity and provide related information. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related information. The providing unit can also provide related information by referring to the activities of the user's friends on social media. In this way, by providing related information based on the user's social media activity, it is possible to provide information that is useful to 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.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can retrieve information from related external databases based on the user's input and reflect it in the analysis results. For example, if the user inputs "Meeting at 10 o'clock," the analysis unit retrieves detailed information about the meeting from a calendar application and provides it to the generation unit. If the user inputs "weather," the analysis unit can retrieve the latest weather information from a weather forecast database and provide it to the generation unit. Furthermore, if the user inputs "news," the analysis unit can retrieve the latest related news articles from a news feed and provide it to the generation unit. In this way, by retrieving information from related external databases based on the user's input and reflecting it in the analysis results, it is possible to provide richer information.

[0055] The generation unit can generate related images and videos based on the user's input. For example, if the user inputs "Meeting at 10 o'clock," the generation unit generates an image showing the meeting schedule. If the user inputs "Weather," the generation unit can also generate a video showing a weather forecast. Furthermore, if the user inputs "News," the generation unit can also generate a video summarizing a news article. In this way, by generating related images and videos based on the user's input, it is possible to provide information visually.

[0056] The analysis unit can generate relevant statistical data based on the user's input and reflect it in the analysis results. For example, if the user inputs "sales," the analysis unit can obtain relevant statistical data from a sales database and provide it to the generation unit. Also, if the user inputs "population," the analysis unit can obtain the latest population data from a demographic database and provide it to the generation unit. Furthermore, if the user inputs "market research," the analysis unit can obtain relevant statistical data from a market research database and provide it to the generation unit. In this way, by generating relevant statistical data based on the user's input and reflecting it in the analysis results, more accurate information can be provided.

[0057] The providing unit can provide related links and resources based on the user's input. For example, if the user inputs "meeting at 10 o'clock," the providing unit can provide a link to detailed information about the meeting. Also, if the user inputs "weather," the providing unit can provide a link to a weather forecast site. Furthermore, if the user inputs "news," the providing unit can provide a link to a news article. In this way, by providing related links and resources based on the user's input, it is possible to enable the user to quickly access the information they need.

[0058] The analysis unit can retrieve related academic papers and research materials based on the user's input and reflect them in the analysis results. For example, if the user inputs "AI technology," the analysis unit can retrieve related papers from an academic database and provide them to the generation unit. Alternatively, if the user inputs "medical research," the analysis unit can retrieve the latest research materials from a medical database and provide them to the generation unit. Furthermore, if the user inputs "environmental issues," the analysis unit can retrieve related research materials from an environmental database and provide them to the generation unit. This allows for deeper knowledge to be provided by retrieving related academic papers and research materials based on the user's input and reflecting them in the analysis results.

[0059] The generation unit can generate sentences using related templates based on the user's input. For example, if the user inputs "Meeting at 10 o'clock," the generation unit can generate sentences using a meeting notification template. If the user inputs "weather," the generation unit can also generate sentences using a weather forecast template. If the user inputs "news," the generation unit can also generate sentences using a news article template. This allows for efficient sentence generation by generating sentences using related templates based on the user's input.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives a word or short phrase entered by the user. For example, the user enters "Meeting at 10 o'clock." Step 2: The analyzer analyzes the input received by the receiver based on the context. For example, the analyzer analyzes the input "Meeting at 10 o'clock" and identifies the appropriate grammatical structure. Step 3: The generator uses a generation AI to generate grammatically correct sentences based on the results of the analysis by the analyzer. For example, the generator generates a grammatically correct sentence such as "I have a meeting at 10 o'clock." The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The providing unit provides the text generated by the generating unit to the user. For example, the providing unit displays the generated text on the user's device.

[0062] (Example 2) A character input assistance system according to an embodiment of the present invention reduces the burden of character input by accepting input of words or short phrases and carefully converting them using a generation AI. In the character input assistance system, a user inputs words or short phrases, and a generation AI analyzes the input and generates grammatically correct sentences, which are then provided to the user. For example, if a user inputs "Meeting at 10 o'clock," the generation AI generates a grammatically correct sentence such as "There is a meeting at 10 o'clock." This allows users to obtain grammatically correct sentences with simple input. This significantly reduces the burden of character input and enables efficient communication. For example, users do not need to input complex sentences; instead, they can obtain grammatically correct sentences simply by inputting simple words or phrases.

[0063] A character input assistance system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives words or short phrases input by a user. For example, the user inputs "meeting at 10 o'clock." The analysis unit analyzes the input received by the reception unit based on the context. For example, the analysis unit analyzes the input "meeting at 10 o'clock" and identifies an appropriate grammatical structure. The generation unit uses a generation AI to generate grammatically correct sentences based on the results of the analysis by the analysis unit. For example, the generation unit generates grammatically correct sentences such as "I have a meeting at 10 o'clock." The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the sentences generated by the generation unit to a user. For example, the provision unit displays the generated sentences on the user's device. In this way, the character input assistance system according to an embodiment can convert input of words or short phrases into grammatically correct sentences, thereby reducing the burden of character input.

[0064] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can delay the timing of input reception and wait until the user is relaxed. Furthermore, if the user is relaxed, the reception unit can also accelerate the timing of input reception to promote smooth input. Furthermore, if the user is in a hurry, the reception unit can immediately accept input and perform quick processing. Thus, by adjusting the timing of input reception according to the user's emotion, the user's stress can be reduced. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is 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, for example, AI, or without AI.

[0065] The reception unit can analyze the user's past input history and select an appropriate input reception method. For example, the reception unit preferentially selects an input method (such as voice or text) that the user has frequently used in the past. The reception unit can also predict and select an input method to be used during a specific time period based on the user's past input history. The reception unit can also analyze the user's past input history and select the most efficient input method. This allows for the selection of the optimal input reception method based on the user's past input history, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0066] The reception unit can filter inputs based on the user's current task or area of ​​interest when receiving the input. For example, the reception unit preferentially receives words and phrases related to the task the user is currently performing. The reception unit can also filter and receive related words and phrases based on the user's area of ​​interest. The reception unit can also analyze the user's current task or area of ​​interest and filter and receive the most appropriate input. In this way, by filtering the inputs based on the user's current task or area of ​​interest, it is possible to preferentially receive highly relevant inputs. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0067] When receiving input, the reception unit can select an appropriate reception means depending on the user's input method. For example, if the user uses voice input, the reception unit can use voice recognition technology to receive the input. Furthermore, if the user uses text input, the reception unit can also preferentially receive keyboard input. Furthermore, if the user uses image input, the reception unit can also use image recognition technology to receive the input. This allows the most appropriate reception means to be selected depending on the user's input method, thereby improving the accuracy and efficiency of input. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0068] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit postpones inputs of lower importance and prioritizes inputs of higher importance. Furthermore, when the user is relaxed, the reception unit can equally accept all inputs. Furthermore, when the user is in a hurry, the reception unit can prioritize inputs that require quick processing. Thus, by determining the priority of inputs according to the user's emotions, important inputs can be processed preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is 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, for example, AI, or without AI.

[0069] When receiving input, the reception unit can prioritize receiving highly relevant inputs in consideration of the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes receiving inputs related to that location. The reception unit can also prioritize receiving related words and phrases based on the user's current location. The reception unit can also analyze the user's geographical location information and prioritize receiving optimal inputs. This can improve the accuracy of inputs by preferentially receiving highly relevant inputs based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0070] The reception unit can analyze the user's social media activity and receive related input when receiving input. For example, the reception unit can prioritize receiving words and phrases that the user frequently uses on social media. The reception unit can also analyze the content of the user's posts on social media and prioritize receiving related input. The reception unit can also prioritize receiving related input by referring to the activities of the user's friends on social media. In this way, the accuracy of the input can be improved by receiving related input based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0071] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. The reception unit customizes the optimal input reception method, for example, based on feedback provided by the user in the past. The reception unit can also preferentially accept a specific input method based on the user's past feedback. The reception unit can also analyze the user's feedback and customize the most efficient input reception method. In this way, input efficiency can be improved by customizing the reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit uses a simple and easy-to-understand presentation method. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. By adjusting the way the analysis is presented according to the user's emotions, it is possible to facilitate understanding of the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during analysis. For example, the analysis unit performs a detailed analysis on an input with high importance. The analysis unit can also perform a simplified analysis on an input with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the input. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the input. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0074] The analysis unit can apply different analysis algorithms depending on the category of input during analysis. For example, the analysis unit applies a natural language processing algorithm to text input. The analysis unit can also apply an image recognition algorithm to image input. The analysis unit can also apply a voice recognition algorithm to voice input. This allows the analysis accuracy to be improved by applying the optimal analysis algorithm depending on the category of input. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the current analysis, for example, based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and select the optimal analysis algorithm. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. This makes it possible to provide more accurate analysis results by improving the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without using AI.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can perform a short, to-the-point analysis. The analysis unit can also perform a detailed analysis if the user is relaxed. The analysis unit can also perform a quick analysis if the user is in a hurry. This allows for adjusting the length of the analysis according to the user's emotions, thereby facilitating user understanding. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI.

[0077] During analysis, the analysis unit can determine the analysis priority based on the time of input submission. For example, the analysis unit prioritizes analysis of input submitted earlier. The analysis unit can also postpone analysis of input submitted later. The analysis unit can also dynamically adjust the analysis priority based on the time of submission. This enables efficient analysis by determining the analysis priority based on the time of input submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the inputs during analysis. For example, the analysis unit prioritizes analysis of highly relevant inputs. The analysis unit can also postpone analysis of less relevant inputs. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the inputs. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the inputs. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0079] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit uses a lot of technical terms. Also, if the user's level of expertise is low, the analysis unit can avoid technical terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This can facilitate understanding of the analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0080] The generation unit can estimate the user's emotions and adjust the expression method of the generated sentence based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit uses a simple and easy-to-understand expression method. Furthermore, if the user is relaxed, the generation unit can use a detailed expression method. Furthermore, if the user is in a hurry, the generation unit can use a method that focuses on the main points. In this way, by adjusting the expression method of the generated sentence according to the user's emotions, it is possible to provide sentences that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0081] The generation unit can adjust the level of detail of the sentence to be generated based on the importance of the analysis result during generation. For example, the generation unit generates detailed sentences for analysis results with high importance. The generation unit can also generate simplified sentences for analysis results with low importance. The generation unit can also dynamically adjust the level of detail of the sentence to be generated according to the importance of the analysis result. This enables efficient sentence generation by adjusting the level of detail of the sentence to be generated according to the importance of the analysis result. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

[0082] The generation unit can apply different generation algorithms depending on the category of the analysis results during generation. For example, the generation unit applies a natural language generation algorithm to text analysis results. The generation unit can also apply an image generation algorithm to image analysis results. The generation unit can also apply a voice generation algorithm to voice analysis results. This allows the application of an optimal generation algorithm depending on the category of the analysis results, thereby improving the accuracy of the generated sentences. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0083] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit can improve the accuracy of current generation, for example, based on the user's past generation results. The generation unit can also analyze the user's past generation results and select the optimal generation algorithm. The generation unit can also adjust the level of detail of generation by referring to the user's past generation results. This makes it possible to provide more accurate sentences by improving the accuracy of generation based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI or without using AI.

[0084] The generation unit can estimate the user's emotions and adjust the length of the generated sentences based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate short, to-the-point sentences. Furthermore, if the user is relaxed, the generation unit can generate longer sentences with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate short sentences that can be read quickly. By adjusting the length of the generated sentences according to the user's emotions, it is possible to provide sentences of an appropriate length for the user. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI.

[0085] At the time of generation, the generation unit can determine the priority of sentences to be generated based on the submission time of the analysis results. For example, the generation unit can generate sentences preferentially for analysis results that are submitted early. The generation unit can also postpone the generation of sentences for analysis results that are submitted late. The generation unit can also dynamically adjust the priority of sentences to be generated based on the submission time. This enables efficient sentence generation by determining the priority of sentences to be generated based on the submission time of the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0086] The generation unit can adjust the order of sentences to be generated based on the relevance of the analysis results during generation. For example, the generation unit prioritizes generating sentences for analysis results with high relevance. The generation unit can also postpone generating sentences for analysis results with low relevance. The generation unit can also dynamically adjust the order of sentences to be generated based on the relevance of the analysis results. This enables efficient sentence generation by adjusting the order of sentences to be generated based on the relevance of the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0087] The generation unit can adjust the use of technical terms in the generated sentences according to the user's level of expertise during generation. For example, if the user's level of expertise is high, the generation unit uses a lot of technical terms. Also, if the user's level of expertise is low, the generation unit can avoid technical terms. The generation unit can also dynamically adjust the use of technical terms in the generated sentences according to the user's level of expertise. In this way, by adjusting the use of technical terms in the generated sentences according to the user's level of expertise, it is possible to provide sentences that are easy for the user to understand. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0088] The providing unit can estimate the user's emotions and adjust the display method of the text to be provided based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit can provide a simple, highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the text to be provided according to the user's emotions, it is possible to provide a display that is highly visible to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0089] The providing unit can select the optimal display method by referring to the user's past operation history when providing the display. For example, the providing unit preferentially provides a display method that the user has used favorably in the past. The providing unit can also select a specific display method from the user's past operation history. The providing unit can also analyze the user's past operation history and select the most efficient display method. In this way, by selecting the optimal display method based on the user's past operation history, it is possible to provide a display that is easy for the user to use. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0090] The providing unit can customize the display content according to the user's current task when providing the information. For example, the providing unit prioritizes displaying information related to the task the user is currently performing. The providing unit can also customize and display related information based on the user's current task. The providing unit can also analyze the user's current task and customize optimal display content. In this way, highly relevant information can be provided by customizing the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0091] The providing unit can estimate the user's emotions and adjust the operation procedures of the text to be provided based on the estimated user's emotions. For example, the providing unit simplifies the operation procedures when the user is feeling stressed. The providing unit can also provide detailed operation procedures when the user is relaxed. The providing unit can also provide procedures that allow the user to operate quickly when the user is in a hurry. In this way, by adjusting the operation procedures of the text to be provided according to the user's emotions, it is possible to provide operation procedures that are easy for the user to use. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0092] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, by selecting the optimal display method based on the user's device information, it is possible to provide a display that is highly visible to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0093] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This makes it possible to provide a display that is easy for the user to understand by making the display content multilingual based on the user's language setting. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0094] At the time of providing the information, the providing unit can analyze the user's social media activity and provide related information. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related information. The providing unit can also provide related information by referring to the activities of the user's friends on social media. In this way, by providing related information based on the user's social media activity, it is possible to provide information that is useful to 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. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation 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 is realized by the reception device 38 of the smart device 14 and receives words or short phrases entered by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input received by the reception unit based on the context. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates grammatically correct sentences based on the results of analysis using the generation AI. The provision unit is realized, for example, by the output device 40 of the smart device 14 and displays the generated sentences on the user's device. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation 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 is realized by the microphone 238 of the smart glasses 214 and receives words or short phrases entered by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input received by the reception unit based on the context. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates grammatically correct sentences based on the results of analysis using the generation AI. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and displays the generated sentences on the user's device. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation 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 is realized by the microphone 238 of the headset-type terminal 314 and receives words or short phrases entered by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input received by the reception unit based on the context. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates grammatically correct sentences based on the results of analysis using the generation AI. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and displays the generated sentences on the user's device. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation 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 is realized by the microphone 238 of the robot 414 and receives words or short phrases entered by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input received by the reception unit based on the context. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates grammatically correct sentences based on the results of analysis using the generation AI. The provision unit is realized, for example, by the speaker 240 of the robot 414 and displays the generated sentences on the user's device.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The analysis unit can retrieve information from related external databases based on the user's input and reflect it in the analysis results. For example, if the user inputs "Meeting at 10 o'clock," the analysis unit retrieves detailed information about the meeting from a calendar application and provides it to the generation unit. If the user inputs "weather," the analysis unit can retrieve the latest weather information from a weather forecast database and provide it to the generation unit. Furthermore, if the user inputs "news," the analysis unit can retrieve the latest related news articles from a news feed and provide it to the generation unit. In this way, by retrieving information from related external databases based on the user's input and reflecting it in the analysis results, it is possible to provide richer information.

[0097] The reception unit can estimate the user's emotions and provide input feedback 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 also provide positive feedback. Furthermore, if the user is in a hurry, the reception unit can provide quick feedback to support the user's input. In this way, by providing input feedback according to the user's emotions, it is possible to improve user satisfaction.

[0098] The generation unit can generate related images and videos based on the user's input. For example, if the user inputs "Meeting at 10 o'clock," the generation unit generates an image showing the meeting schedule. If the user inputs "Weather," the generation unit can also generate a video showing a weather forecast. Furthermore, if the user inputs "News," the generation unit can also generate a video summarizing a news article. In this way, by generating related images and videos based on the user's input, it is possible to provide information visually.

[0099] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide information in a simple, highly visible format. If the user is relaxed, the providing unit can also provide information in a format that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide information in a format that focuses on the main points. In this way, by adjusting the format of the information to be provided according to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0100] The analysis unit can generate relevant statistical data based on the user's input and reflect it in the analysis results. For example, if the user inputs "sales," the analysis unit can obtain relevant statistical data from a sales database and provide it to the generation unit. Also, if the user inputs "population," the analysis unit can obtain the latest population data from a demographic database and provide it to the generation unit. Furthermore, if the user inputs "market research," the analysis unit can obtain relevant statistical data from a market research database and provide it to the generation unit. In this way, by generating relevant statistical data based on the user's input and reflecting it in the analysis results, more accurate information can be provided.

[0101] The reception unit can estimate the user's emotion and provide an input auto-completion function based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can auto-completion with simple input. If the user is relaxed, the reception unit can also auto-completion with detailed input. Furthermore, if the user is in a hurry, the reception unit can also auto-completion with quick input. In this way, by providing an input auto-completion function according to the user's emotion, input efficiency can be improved.

[0102] The providing unit can provide related links and resources based on the user's input. For example, if the user inputs "meeting at 10 o'clock," the providing unit can provide a link to detailed information about the meeting. Also, if the user inputs "weather," the providing unit can provide a link to a weather forecast site. Furthermore, if the user inputs "news," the providing unit can provide a link to a news article. In this way, by providing related links and resources based on the user's input, it is possible to enable the user to quickly access the information they need.

[0103] The analysis unit can retrieve related academic papers and research materials based on the user's input and reflect them in the analysis results. For example, if the user inputs "AI technology," the analysis unit can retrieve related papers from an academic database and provide them to the generation unit. Alternatively, if the user inputs "medical research," the analysis unit can retrieve the latest research materials from a medical database and provide them to the generation unit. Furthermore, if the user inputs "environmental issues," the analysis unit can retrieve related research materials from an environmental database and provide them to the generation unit. This allows for deeper knowledge to be provided by retrieving related academic papers and research materials based on the user's input and reflecting them in the analysis results.

[0104] The providing unit can estimate the user's emotions and adjust the priority of information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide information of high importance with priority. Also, if the user is relaxed, the providing unit can provide all information equally. Furthermore, if the user is in a hurry, the providing unit can provide information that requires quick processing with priority. In this way, by adjusting the priority of information to be provided according to the user's emotions, it is possible to quickly provide information that is important to the user.

[0105] The generation unit can generate sentences using related templates based on the user's input. For example, if the user inputs "Meeting at 10 o'clock," the generation unit can generate sentences using a meeting notification template. If the user inputs "weather," the generation unit can also generate sentences using a weather forecast template. If the user inputs "news," the generation unit can also generate sentences using a news article template. This allows for efficient sentence generation by generating sentences using related templates based on the user's input.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The reception unit receives a word or short phrase entered by the user. For example, the user enters "Meeting at 10 o'clock." Step 2: The analyzer analyzes the input received by the receiver based on the context. For example, the analyzer analyzes the input "Meeting at 10 o'clock" and identifies the appropriate grammatical structure. Step 3: The generator uses a generation AI to generate grammatically correct sentences based on the results of the analysis by the analyzer. For example, the generator generates a grammatically correct sentence such as "I have a meeting at 10 o'clock." The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The providing unit provides the text generated by the generating unit to the user. For example, the providing unit displays the generated text on the user's device.

[0108] 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.

[0109] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 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 receiving unit for receiving input of a word or short phrase; an analysis unit that analyzes the input received by the reception unit; a generation unit that generates grammatically correct sentences based on the results of the analysis by the analysis unit; a providing unit that provides the sentence generated by the generating unit. A system characterized by:

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

2. The system of claim 1.

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

4. The reception unit As input is received, it filters it based on the user's current task or area of ​​interest.

2. The system of claim 1.

5. The reception unit When accepting input, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize inputs based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When accepting input, the system takes into account the user's geographical location information and prioritizes relevant input.

2. The system of claim 1.

8. The reception unit When receiving input, analyze the user's social media activity and receive relevant input 2. The system of claim 1.

Images