system

Abstract

The system according to this embodiment aims to efficiently edit text using voice commands. [Solution] The system according to this embodiment comprises a reception unit, an editing unit, a search unit, and a reading unit. The reception unit receives voice commands. The editing unit edits text based on the voice commands received by the reception unit. The search unit searches for and replaces specified words or phrases. The reading unit reads the text aloud.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently perform text editing operations using voice commands.

[0005] The system according to the embodiment aims to efficiently edit text using voice commands.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an editing unit, a search unit, and a reading unit. The reception unit receives voice commands. The editing unit edits text based on the voice commands received by the reception unit. The search unit searches for and replaces specified words or phrases. The reading unit reads out sentences.

Effects of the Invention

[0007] The system according to this embodiment can efficiently edit text using voice commands. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI assistant system according to an embodiment of the present invention is a system that can insert, delete, and modify text using voice commands. This AI assistant system allows users to search for and replace specific words and phrases using voice commands. It also supports editing work in conjunction with a text-to-speech function. For example, by inputting a voice command such as "Add a new sentence to the end of this paragraph," the AI ​​assistant system will edit the text according to the instruction. Furthermore, by inputting a voice command such as "Replace 'specific word' with 'new word'," the user can search for and replace specific words and phrases. By using the text-to-speech function, the user can proceed with the work while confirming the edited content. As a result, the AI ​​assistant system enables text insertion, deletion, modification, search, replacement, and reading aloud using voice commands.

[0029] The AI ​​assistant system according to this embodiment comprises a reception unit, an editing unit, a search unit, and a reading unit. The reception unit receives voice commands. The reception unit can receive voice commands, for example, when the user inputs a voice command such as "Add a new sentence to the end of this paragraph." The editing unit edits text based on voice commands. The editing unit inserts, deletes, and modifies text based on voice commands, for example. The editing unit can insert text, for example, when the user inputs a voice command such as "Add a new sentence to the end of this paragraph." The editing unit can also delete text, for example, when the user inputs a voice command such as "Delete this sentence." Furthermore, the editing unit can also modify text, for example, when the user inputs a voice command such as "Replace this word with a new word." The search unit searches for and replaces specific words or phrases. The search unit can search for specific words, for example, when the user inputs a voice command such as "Search for a specific word." Furthermore, the search unit can replace specific words by having the user input a voice command such as "replace 'specific word' with 'new word'." The text-to-speech unit reads the edited text aloud. The text-to-speech unit can read the edited text aloud by having the user input a voice command such as "read this paragraph aloud." The text-to-speech unit can also read aloud specific sentences by having the user input a voice command such as "read this sentence aloud." As a result, the AI ​​assistant system according to this embodiment can insert, delete, modify, search, replace, and read aloud text using voice commands.

[0030] The reception unit accepts voice commands. For example, the reception unit can accept voice commands when a user inputs a command such as, "Add a new sentence to the end of this paragraph." Specifically, the reception unit consists of a high-sensitivity microphone and speech recognition software. The speech recognition software converts the user's voice into a digital signal and analyzes the voice command using natural language processing (NLP) technology. For example, when a user says, "Add a new sentence to the end of this paragraph," the speech recognition software converts the voice into text, and the NLP engine analyzes the text to understand the intent of the command. Furthermore, the reception unit is equipped with noise cancellation and speech filtering technologies to remove ambient noise in order to accurately recognize the user's voice commands. This allows users to accurately input voice commands even in noisy environments. The reception unit also features a multilingual speech recognition model to support multiple languages ​​and dialects, enabling accurate recognition even when users input voice commands in different languages. This allows the reception unit to meet diverse user needs and efficiently accept voice commands.

[0031] The editorial team edits text based on voice commands. For example, the editorial team inserts, deletes, and modifies text based on voice commands. Specifically, the editorial team has algorithms to analyze voice commands and perform text operations based on their content. For example, if a user enters a voice command such as "Add a new sentence to the end of this paragraph," the editorial team analyzes the command and inserts a new sentence at the specified location. Also, if a user enters a voice command such as "Delete this sentence," the editorial team identifies that sentence and deletes it from the text. Furthermore, if a user enters a voice command such as "Replace this word with a new word," the editorial team identifies that word and replaces it with a new word. To perform these operations quickly and accurately, the editorial team uses a combination of advanced text analysis and natural language processing technologies. For example, the editorial team performs grammatical and semantic analysis to understand the context and accurately grasp the user's intent. The editorial team can also record the user's editing history and make predictions and suggestions based on past operations. This allows the editorial team to streamline the user's editing work and achieve a smoother operation.

[0032] The search unit searches for and replaces specific words and phrases. For example, the user can input a voice command such as "Search for 'specific word'" to find a particular word. Specifically, the search unit creates an index for quickly searching for specific words and phrases within text and uses an efficient search algorithm. For instance, when a user inputs a voice command like "Search for 'specific word'", the search unit quickly searches for that word within the text and highlights the relevant section. Similarly, when a user inputs a voice command like "Replace 'specific word' with 'new word'", the search unit identifies the word and replaces it with the new word. To perform these operations efficiently, the search unit combines advanced search algorithms and natural language processing techniques. For example, it can use regular expressions and fuzzy search techniques to search for similar words and words containing typos or misspellings. Furthermore, the search unit can record the user's search history and provide predictions and suggestions based on past search results. This allows the search unit to streamline the user's search process and provide faster and more accurate search results.

[0033] The text-to-speech unit reads aloud edited text. For example, the unit can read edited text aloud when the user inputs a voice command such as "Read this paragraph." Specifically, the text-to-speech unit uses speech synthesis technology to convert text into speech. For instance, when a user inputs a voice command like "Read this paragraph," the unit identifies the paragraph and uses its speech synthesis engine to convert the text into natural-sounding speech. Similarly, when a user inputs a voice command like "Read this sentence," the unit identifies the sentence and converts it into speech. The text-to-speech unit also has a function to adjust the parameters of the speech synthesis engine to improve the naturalness and clarity of the speech. For example, by adjusting the speed, pitch, and intonation of the speech, it can achieve speech output tailored to the user's preferences. Furthermore, the text-to-speech unit is equipped with multiple voice models, allowing users to select different voice qualities and speaker voices. This enables the text-to-speech unit to meet diverse user needs and read edited text naturally and clearly. In addition, the text-to-speech unit can continuously improve the quality of speech synthesis based on user feedback. For example, when a user provides feedback on the spoken audio, the parameters of the speech synthesis engine are adjusted based on that feedback to achieve higher quality audio output. This allows the reading unit to provide users with high-quality audio output and effectively convey the content of the edited text.

[0034] The editorial department can insert, delete, and modify text based on voice commands. For example, the editorial department can insert text when the user enters a voice command such as "Add a new sentence to the end of this paragraph." The editorial department can also delete text when the user enters a voice command such as "Delete this sentence." Furthermore, the editorial department can modify text when the user enters a voice command such as "Replace this word with a new word." This makes it possible to edit text based on voice commands. Some or all of the above processes in the editorial department may be performed using AI, for example, or not. For example, the editorial department can input voice commands into a generating AI, which can then perform text insertion, deletion, and modification.

[0035] The search unit can search for and replace specific words or phrases. For example, the search unit can search for a specific word by having the user input a voice command such as "Search for 'specific word'." The search unit can also replace a specific word by having the user input a voice command such as "Replace 'specific word' with 'new word'." This enables the searching and replacement of specific words and phrases. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the text to be searched into a generating AI, which can then perform the search and replacement of specific words or phrases.

[0036] The text-to-speech unit can read aloud edited text. For example, the text-to-speech unit can read aloud edited text when the user inputs a voice command such as "Read this paragraph." The text-to-speech unit can also read aloud specific sentences when the user inputs a voice command such as "Read this sentence." This makes it possible to read aloud edited text. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input edited text into a generating AI, and the generating AI can perform the text-to-speech function.

[0037] The reception unit can analyze the user's past voice command history and select the optimal reception method. For example, the reception unit prioritizes receiving voice commands that the user has frequently used in the past. The reception unit can also predict commands to be used during specific time periods based on the user's past voice command history and receive them accordingly. Furthermore, the reception unit can analyze the user's past voice command history and propose the most efficient reception method. This allows for the selection of the optimal reception method based on past voice command history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past voice command history into a generating AI, which can then select the optimal reception method.

[0038] The reception unit can filter out noise from the user's current ambient sounds when receiving a voice command. For example, if the user is in a noisy environment, the reception unit can filter out noise from the ambient sounds and accurately receive the voice command. If the user is in a quiet environment, the reception unit can also filter out ambient sounds to a minimum and smoothly receive the voice command. Furthermore, if the user is moving, the reception unit can filter out noise from ambient sounds in real time and receive the voice command. This improves the accuracy of voice command reception by filtering out noise from ambient sounds. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input ambient sound data into a generating AI, which can then perform noise reduction.

[0039] The reception unit can prioritize receiving voice commands that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception unit will prioritize receiving voice commands related to that location. Furthermore, if the user is on the move, the reception unit can prioritize receiving voice commands related to movement. Additionally, if the user is at home, the reception unit can prioritize receiving voice commands related to home. This allows for the priority of receiving commands that are highly relevant based on geographical location information. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI, which can then select highly relevant commands.

[0040] The reception unit can analyze the user's social media activity when receiving a voice command and accept relevant commands. For example, if the user has posted about a specific topic on social media, the reception unit will prioritize receiving voice commands related to that topic. It can also prioritize receiving voice commands related to an event if the user has participated in a specific event on social media. Furthermore, if the user belongs to a specific group on social media, the reception unit can prioritize receiving voice commands related to that group. This allows for the acceptance of relevant commands based on social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI, which can then select relevant commands.

[0041] The editorial team can adjust the level of detail in editing based on the importance of the text. For example, they can edit important sections of text in detail and less important sections simply. They can also prioritize edits based on the importance of the text. Furthermore, they can add additional annotations to important sections of text and perform detailed editing on them. This allows them to adjust the level of detail in editing based on the importance of the text. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input text importance data into a generating AI, which can then adjust the level of detail in editing.

[0042] The editorial department can apply different editing algorithms depending on the category of the text during editing. For example, the editorial department can apply a news-specific editing algorithm to news articles. They can also apply an editing algorithm specifically for academic papers to academic papers. Furthermore, they can apply a blog-specific editing algorithm to blog posts. This allows for the application of different editing algorithms depending on the category of the text. Some or all of the above processes in the editorial department may be performed using AI, for example, or not. For example, the editorial department can input text category data into a generating AI, which can then select an appropriate editing algorithm.

[0043] The editorial department can determine editing priorities based on when the text was created. For example, the editorial department might prioritize editing the most recent text. Alternatively, it could postpone editing older text. Furthermore, the editorial department can dynamically adjust editing priorities according to when the text was created. This allows for the determination of editing priorities based on when the text was created. Some or all of the above processes in the editorial department may be performed using AI, or not. For example, the editorial department could input text creation date data into a generating AI, which could then determine the editing priorities.

[0044] The editorial department can adjust the editing order based on the relevance of the text during the editing process. For example, the editorial department may prioritize editing highly relevant text. Alternatively, the editorial department may postpone editing less relevant text. Furthermore, the editorial department can dynamically adjust the editing order based on the relevance of the text. This allows for adjusting the editing order based on the relevance of the text. Some or all of the above processes in the editorial department may be performed using AI, for example, or not. For example, the editorial department can input text relevance data into a generating AI, which can then adjust the editing order.

[0045] The search unit can improve search accuracy by considering the relationships between texts during a search. For example, the search unit analyzes the relationships between texts and prioritizes displaying highly relevant results. The search unit can also improve the accuracy of search results by considering the relationships between texts. Furthermore, the search unit can search for related phrases and words based on the relationships between texts. This allows for improved search accuracy by considering the relationships between texts. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input text relationship data into a generating AI, which can then improve search accuracy.

[0046] The search unit can perform searches while considering the attribute information of the text's author. For example, the search unit can display relevant search results by considering the text's author's field of expertise. It can also display highly reliable search results by considering the text's author's years of experience. Furthermore, the search unit can display relevant search results by considering the text's author's past writing history. This allows the search to be performed while considering the attribute information of the text's author. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the author's attribute information into a generating AI, which can then select relevant search results.

[0047] The search unit can perform searches while considering the geographical distribution of the text. For example, the search unit can analyze the geographical distribution of the text and prioritize displaying search results for relevant regions. The search unit can also improve the accuracy of search results by considering the geographical distribution of the text. Furthermore, the search unit can search for related phrases and words based on the geographical distribution of the text. This allows the search to be performed while considering the geographical distribution of the text. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input geographical distribution data into a generating AI, and the generating AI can select relevant search results.

[0048] The search unit can improve the accuracy of searches by referring to related literature in the text during the search process. For example, the search unit can analyze related literature in the text and prioritize displaying highly relevant search results. The search unit can also improve the accuracy of search results by considering related literature in the text. Furthermore, the search unit can search for related phrases and words based on related literature in the text. This allows for improved search accuracy by referring to related literature in the text. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input related literature data into a generating AI, which can then improve the accuracy of the search.

[0049] The text-to-speech unit can adjust the level of detail in its reading based on the importance of the text. For example, it can read important parts of the text in detail and less important parts briefly. The reading unit can also determine the reading priority based on the importance of the text. Furthermore, it can add additional annotations to important parts of the text and read them in detail. This allows for adjustment of the level of detail based on the importance of the text. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input text importance data into a generating AI, which can then adjust the level of detail in its reading.

[0050] The text-to-speech unit can apply different reading algorithms depending on the category of the text during reading. For example, the reading unit can apply a news-specific reading algorithm to news articles. It can also apply a reading algorithm specifically for academic papers to academic papers. Furthermore, it can apply a blog-specific reading algorithm to blog posts. This allows for the application of different reading algorithms depending on the category of the text. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input text category data into a generating AI, which can then select an appropriate reading algorithm.

[0051] The text-to-speech unit can determine the reading priority based on when the text was created. For example, the text-to-speech unit may prioritize reading the most recent text. It can also postpone reading older text. Furthermore, the text-to-speech unit can dynamically adjust the reading priority according to when the text was created. This allows the reading priority to be determined based on when the text was created. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input text creation date data into a generating AI, which can then determine the reading priority.

[0052] The text-to-speech unit can adjust the reading order based on the relevance of the text during reading. For example, the text-to-speech unit may prioritize reading highly relevant text. It can also postpone reading less relevant text. Furthermore, the text-to-speech unit can dynamically adjust the reading order according to the relevance of the text. This allows the reading order to be adjusted based on the relevance of the text. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input text relevance data into a generating AI, which can then adjust the reading order.

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

[0054] The editorial team can analyze a user's past editing history and suggest the optimal editing method. For example, it can prioritize suggesting editing commands that the user has frequently used in the past. Furthermore, the editorial team can predict and suggest editing commands that a user will use at specific times of the day based on their past editing history. In addition, the editorial team can analyze a user's past editing history and suggest the most efficient editing method. This allows for the suggestion of the optimal editing method based on past editing history.

[0055] The text-to-speech unit can analyze the user's past reading history and suggest the optimal reading method. For example, it can prioritize suggesting reading commands that the user has frequently used in the past. Furthermore, the text-to-speech unit can predict and suggest reading commands to be used during specific time periods based on the user's past reading history. In addition, the text-to-speech unit can analyze the user's past reading history and suggest the most efficient reading method. This allows the system to suggest the optimal reading method based on past reading history.

[0056] The editorial team can analyze the context of the text and make editing suggestions based on that context. For example, they can suggest appropriate words or phrases in a particular context. They can also suggest appropriate grammatical corrections based on the context. Furthermore, they can suggest appropriate stylistic changes based on the context. This allows them to make editing suggestions based on the context of the text.

[0057] The text-to-speech function can analyze the user's past reading speeds when reading text and suggest an optimal reading speed. For example, if the user has previously preferred a slow reading speed, it can suggest a similar speed. Similarly, if the user has previously preferred a fast reading speed, it can suggest a similar speed. Furthermore, if the user has previously preferred a medium reading speed, it can suggest a similar speed. This allows the system to suggest an optimal reading speed based on past reading speeds.

[0058] The editorial team can analyze the readability of the text and make editing suggestions based on that readability. For example, they can suggest simplifying complex sentences. They can also suggest shortening long paragraphs. Furthermore, they can suggest replacing difficult words with simpler ones. In this way, they can make editing suggestions based on the readability of the text.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk accepts voice commands. For example, a user can input a voice command such as "Add a new sentence to the end of this paragraph." Step 2: The editorial team edits the text based on voice commands. For example, they insert, delete, and modify text based on voice commands. Users can insert text by entering a voice command such as "Add a new sentence to the end of this paragraph." They can also delete text by entering a voice command such as "Delete this sentence." Furthermore, users can modify text by entering a voice command such as "Replace this word with a new word." Step 3: The search unit searches for and replaces specific words or phrases. For example, a user can search for a specific word by entering a voice command such as "Search for 'specific word'." Similarly, a user can replace a specific word by entering a voice command such as "Replace 'specific word' with 'new word'." Step 4: The text-to-speech function reads the edited text aloud. For example, the user can input a voice command such as "Read this paragraph," and the edited text will be read aloud. The user can also input a voice command such as "Read this sentence," and a specific sentence will be read aloud.

[0061] (Example of form 2) An AI assistant system according to an embodiment of the present invention is a system that can insert, delete, and modify text using voice commands. This AI assistant system allows users to search for and replace specific words and phrases using voice commands. It also supports editing work in conjunction with a text-to-speech function. For example, by inputting a voice command such as "Add a new sentence to the end of this paragraph," the AI ​​assistant system will edit the text according to the instruction. Furthermore, by inputting a voice command such as "Replace 'specific word' with 'new word'," the user can search for and replace specific words and phrases. By using the text-to-speech function, the user can proceed with the work while confirming the edited content. As a result, the AI ​​assistant system enables text insertion, deletion, modification, search, replacement, and reading aloud using voice commands.

[0062] The AI ​​assistant system according to this embodiment comprises a reception unit, an editing unit, a search unit, and a reading unit. The reception unit receives voice commands. The reception unit can receive voice commands, for example, when the user inputs a voice command such as "Add a new sentence to the end of this paragraph." The editing unit edits text based on voice commands. The editing unit inserts, deletes, and modifies text based on voice commands, for example. The editing unit can insert text, for example, when the user inputs a voice command such as "Add a new sentence to the end of this paragraph." The editing unit can also delete text, for example, when the user inputs a voice command such as "Delete this sentence." Furthermore, the editing unit can also modify text, for example, when the user inputs a voice command such as "Replace this word with a new word." The search unit searches for and replaces specific words or phrases. The search unit can search for specific words, for example, when the user inputs a voice command such as "Search for a specific word." Furthermore, the search unit can replace specific words by having the user input a voice command such as "replace 'specific word' with 'new word'." The text-to-speech unit reads the edited text aloud. The text-to-speech unit can read the edited text aloud by having the user input a voice command such as "read this paragraph aloud." The text-to-speech unit can also read aloud specific sentences by having the user input a voice command such as "read this sentence aloud." As a result, the AI ​​assistant system according to this embodiment can insert, delete, modify, search, replace, and read aloud text using voice commands.

[0063] The reception unit accepts voice commands. For example, the reception unit can accept voice commands when a user inputs a command such as, "Add a new sentence to the end of this paragraph." Specifically, the reception unit consists of a high-sensitivity microphone and speech recognition software. The speech recognition software converts the user's voice into a digital signal and analyzes the voice command using natural language processing (NLP) technology. For example, when a user says, "Add a new sentence to the end of this paragraph," the speech recognition software converts the voice into text, and the NLP engine analyzes the text to understand the intent of the command. Furthermore, the reception unit is equipped with noise cancellation and speech filtering technologies to remove ambient noise in order to accurately recognize the user's voice commands. This allows users to accurately input voice commands even in noisy environments. The reception unit also features a multilingual speech recognition model to support multiple languages ​​and dialects, enabling accurate recognition even when users input voice commands in different languages. This allows the reception unit to meet diverse user needs and efficiently accept voice commands.

[0064] The editorial team edits text based on voice commands. For example, the editorial team inserts, deletes, and modifies text based on voice commands. Specifically, the editorial team has algorithms to analyze voice commands and perform text operations based on their content. For example, if a user enters a voice command such as "Add a new sentence to the end of this paragraph," the editorial team analyzes the command and inserts a new sentence at the specified location. Also, if a user enters a voice command such as "Delete this sentence," the editorial team identifies that sentence and deletes it from the text. Furthermore, if a user enters a voice command such as "Replace this word with a new word," the editorial team identifies that word and replaces it with a new word. To perform these operations quickly and accurately, the editorial team uses a combination of advanced text analysis and natural language processing technologies. For example, the editorial team performs grammatical and semantic analysis to understand the context and accurately grasp the user's intent. The editorial team can also record the user's editing history and make predictions and suggestions based on past operations. This allows the editorial team to streamline the user's editing work and achieve a smoother operation.

[0065] The search unit searches for and replaces specific words and phrases. For example, the user can input a voice command such as "Search for 'specific word'" to find a particular word. Specifically, the search unit creates an index for quickly searching for specific words and phrases within text and uses an efficient search algorithm. For instance, when a user inputs a voice command like "Search for 'specific word'", the search unit quickly searches for that word within the text and highlights the relevant section. Similarly, when a user inputs a voice command like "Replace 'specific word' with 'new word'", the search unit identifies the word and replaces it with the new word. To perform these operations efficiently, the search unit combines advanced search algorithms and natural language processing techniques. For example, it can use regular expressions and fuzzy search techniques to search for similar words and words containing typos or misspellings. Furthermore, the search unit can record the user's search history and provide predictions and suggestions based on past search results. This allows the search unit to streamline the user's search process and provide faster and more accurate search results.

[0066] The text-to-speech unit reads aloud edited text. For example, the unit can read edited text aloud when the user inputs a voice command such as "Read this paragraph." Specifically, the text-to-speech unit uses speech synthesis technology to convert text into speech. For instance, when a user inputs a voice command like "Read this paragraph," the unit identifies the paragraph and uses its speech synthesis engine to convert the text into natural-sounding speech. Similarly, when a user inputs a voice command like "Read this sentence," the unit identifies the sentence and converts it into speech. The text-to-speech unit also has a function to adjust the parameters of the speech synthesis engine to improve the naturalness and clarity of the speech. For example, by adjusting the speed, pitch, and intonation of the speech, it can achieve speech output tailored to the user's preferences. Furthermore, the text-to-speech unit is equipped with multiple voice models, allowing users to select different voice qualities and speaker voices. This enables the text-to-speech unit to meet diverse user needs and read edited text naturally and clearly. In addition, the text-to-speech unit can continuously improve the quality of speech synthesis based on user feedback. For example, when a user provides feedback on the spoken audio, the parameters of the speech synthesis engine are adjusted based on that feedback to achieve higher quality audio output. This allows the reading unit to provide users with high-quality audio output and effectively convey the content of the edited text.

[0067] The editorial department can insert, delete, and modify text based on voice commands. For example, the editorial department can insert text when the user enters a voice command such as "Add a new sentence to the end of this paragraph." The editorial department can also delete text when the user enters a voice command such as "Delete this sentence." Furthermore, the editorial department can modify text when the user enters a voice command such as "Replace this word with a new word." This makes it possible to edit text based on voice commands. Some or all of the above processes in the editorial department may be performed using AI, for example, or not. For example, the editorial department can input voice commands into a generating AI, which can then perform text insertion, deletion, and modification.

[0068] The search unit can search for and replace specific words or phrases. For example, the search unit can search for a specific word by having the user input a voice command such as "Search for 'specific word'." The search unit can also replace a specific word by having the user input a voice command such as "Replace 'specific word' with 'new word'." This enables the searching and replacement of specific words and phrases. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the text to be searched into a generating AI, which can then perform the search and replacement of specific words or phrases.

[0069] The text-to-speech unit can read aloud edited text. For example, the text-to-speech unit can read aloud edited text when the user inputs a voice command such as "Read this paragraph." The text-to-speech unit can also read aloud specific sentences when the user inputs a voice command such as "Read this sentence." This makes it possible to read aloud edited text. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input edited text into a generating AI, and the generating AI can perform the text-to-speech function.

[0070] The reception unit can estimate the user's emotions and adjust the timing of voice command reception based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the timing of voice command reception and wait until the user is relaxed. Conversely, if the user is relaxed, the reception unit can speed up the timing of voice command reception to enable smooth operation. Furthermore, if the user is in a hurry, the reception unit can make the timing of voice command reception immediate to support quick operation. This allows the timing of voice command reception to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0071] The reception unit can analyze the user's past voice command history and select the optimal reception method. For example, the reception unit prioritizes receiving voice commands that the user has frequently used in the past. The reception unit can also predict commands to be used during specific time periods based on the user's past voice command history and receive them accordingly. Furthermore, the reception unit can analyze the user's past voice command history and propose the most efficient reception method. This allows for the selection of the optimal reception method based on past voice command history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past voice command history into a generating AI, which can then select the optimal reception method.

[0072] The reception unit can filter out noise from the user's current ambient sounds when receiving a voice command. For example, if the user is in a noisy environment, the reception unit can filter out noise from the ambient sounds and accurately receive the voice command. If the user is in a quiet environment, the reception unit can also filter out ambient sounds to a minimum and smoothly receive the voice command. Furthermore, if the user is moving, the reception unit can filter out noise from ambient sounds in real time and receive the voice command. This improves the accuracy of voice command reception by filtering out noise from ambient sounds. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input ambient sound data into a generating AI, which can then perform noise reduction.

[0073] The reception unit can estimate the user's emotions and determine the priority of voice commands to receive based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize important voice commands. If the user is relaxed, the reception unit can also prioritize all voice commands equally. Furthermore, if the user is in a hurry, the reception unit can prioritize urgent voice commands. This allows for the prioritization of voice commands according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0074] The reception unit can prioritize receiving voice commands that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception unit will prioritize receiving voice commands related to that location. Furthermore, if the user is on the move, the reception unit can prioritize receiving voice commands related to movement. Additionally, if the user is at home, the reception unit can prioritize receiving voice commands related to home. This allows for the priority of receiving commands that are highly relevant based on geographical location information. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI, which can then select highly relevant commands.

[0075] The reception unit can analyze the user's social media activity when receiving a voice command and accept relevant commands. For example, if the user has posted about a specific topic on social media, the reception unit will prioritize receiving voice commands related to that topic. It can also prioritize receiving voice commands related to an event if the user has participated in a specific event on social media. Furthermore, if the user belongs to a specific group on social media, the reception unit can prioritize receiving voice commands related to that group. This allows for the acceptance of relevant commands based on social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI, which can then select relevant commands.

[0076] The editorial team can estimate the user's emotions and adjust the editorial style based on those emotions. For example, if the user is relaxed, the editorial team can use softer language. If the user is stressed, the editorial team can use concise and clear language. Furthermore, if the user is excited, the editorial team can use visually stimulating language. This allows the editorial style to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI or not. For example, the editorial team can input user emotion data into a generative AI, which can then adjust the editorial style.

[0077] The editorial team can adjust the level of detail in editing based on the importance of the text. For example, they can edit important sections of text in detail and less important sections simply. They can also prioritize edits based on the importance of the text. Furthermore, they can add additional annotations to important sections of text and perform detailed editing on them. This allows them to adjust the level of detail in editing based on the importance of the text. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input text importance data into a generating AI, which can then adjust the level of detail in editing.

[0078] The editorial department can apply different editing algorithms depending on the category of the text during editing. For example, the editorial department can apply a news-specific editing algorithm to news articles. They can also apply an editing algorithm specifically for academic papers to academic papers. Furthermore, they can apply a blog-specific editing algorithm to blog posts. This allows for the application of different editing algorithms depending on the category of the text. Some or all of the above processes in the editorial department may be performed using AI, for example, or not. For example, the editorial department can input text category data into a generating AI, which can then select an appropriate editing algorithm.

[0079] The editorial team can estimate the user's emotions and adjust the length of the edit based on the estimated emotions. For example, if the user is relaxed, the editorial team can perform a detailed edit and provide longer text. If the user is stressed, the editorial team can perform a concise edit and provide shorter text. Furthermore, if the user is in a hurry, the editorial team can perform a short, to-the-point edit. This allows the length of the edit to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI or not. For example, the editorial team can input user emotion data into a generative AI, which can then adjust the length of the edit.

[0080] The editorial department can determine editing priorities based on when the text was created. For example, the editorial department might prioritize editing the most recent text. Alternatively, it could postpone editing older text. Furthermore, the editorial department can dynamically adjust editing priorities according to when the text was created. This allows for the determination of editing priorities based on when the text was created. Some or all of the above processes in the editorial department may be performed using AI, or not. For example, the editorial department could input text creation date data into a generating AI, which could then determine the editing priorities.

[0081] The editorial department can adjust the editing order based on the relevance of the text during the editing process. For example, the editorial department may prioritize editing highly relevant text. Alternatively, the editorial department may postpone editing less relevant text. Furthermore, the editorial department can dynamically adjust the editing order based on the relevance of the text. This allows for adjusting the editing order based on the relevance of the text. Some or all of the above processes in the editorial department may be performed using AI, for example, or not. For example, the editorial department can input text relevance data into a generating AI, which can then adjust the editing order.

[0082] The search unit can estimate the user's emotions and adjust the search criteria based on the estimated emotions. For example, if the user is relaxed, the search unit can apply broad search criteria. If the user is stressed, the search unit can also apply narrow search criteria. Furthermore, if the user is in a hurry, the search unit can apply fast search criteria. This allows the search criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI, which can then adjust the search criteria.

[0083] The search unit can improve search accuracy by considering the relationships between texts during a search. For example, the search unit analyzes the relationships between texts and prioritizes displaying highly relevant results. The search unit can also improve the accuracy of search results by considering the relationships between texts. Furthermore, the search unit can search for related phrases and words based on the relationships between texts. This allows for improved search accuracy by considering the relationships between texts. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input text relationship data into a generating AI, which can then improve search accuracy.

[0084] The search unit can perform searches while considering the attribute information of the text's author. For example, the search unit can display relevant search results by considering the text's author's field of expertise. It can also display highly reliable search results by considering the text's author's years of experience. Furthermore, the search unit can display relevant search results by considering the text's author's past writing history. This allows the search to be performed while considering the attribute information of the text's author. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the author's attribute information into a generating AI, which can then select relevant search results.

[0085] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated emotions. For example, if the user is relaxed, the search unit may display detailed search results at the top. If the user is stressed, the search unit may also display concise search results at the top. Furthermore, if the user is in a hurry, the search unit may also display search results that can be accessed quickly at the top. This allows the display order of search results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not. For example, the search unit can input user emotion data into a generative AI, which can then adjust the display order of search results.

[0086] The search unit can perform searches while considering the geographical distribution of the text. For example, the search unit can analyze the geographical distribution of the text and prioritize displaying search results for relevant regions. The search unit can also improve the accuracy of search results by considering the geographical distribution of the text. Furthermore, the search unit can search for related phrases and words based on the geographical distribution of the text. This allows the search to be performed while considering the geographical distribution of the text. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input geographical distribution data into a generating AI, and the generating AI can select relevant search results.

[0087] The search unit can improve the accuracy of searches by referring to related literature in the text during the search process. For example, the search unit can analyze related literature in the text and prioritize displaying highly relevant search results. The search unit can also improve the accuracy of search results by considering related literature in the text. Furthermore, the search unit can search for related phrases and words based on related literature in the text. This allows for improved search accuracy by referring to related literature in the text. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input related literature data into a generating AI, which can then improve the accuracy of the search.

[0088] The text-to-speech unit can estimate the user's emotions and adjust the reading style based on the estimated emotions. For example, if the user is relaxed, the text-to-speech unit will read in a soft voice. If the user is stressed, the text-to-speech unit can read in a calm voice. Furthermore, if the user is excited, the text-to-speech unit can read in a cheerful voice. This allows the reading style to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the text-to-speech unit may be performed using AI, or not using AI. For example, the text-to-speech unit can input user emotion data into the generative AI, which can then adjust the reading style.

[0089] The text-to-speech unit can adjust the level of detail in its reading based on the importance of the text. For example, it can read important parts of the text in detail and less important parts briefly. The reading unit can also determine the reading priority based on the importance of the text. Furthermore, it can add additional annotations to important parts of the text and read them in detail. This allows for adjustment of the level of detail based on the importance of the text. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input text importance data into a generating AI, which can then adjust the level of detail in its reading.

[0090] The text-to-speech unit can apply different reading algorithms depending on the category of the text during reading. For example, the reading unit can apply a news-specific reading algorithm to news articles. It can also apply a reading algorithm specifically for academic papers to academic papers. Furthermore, it can apply a blog-specific reading algorithm to blog posts. This allows for the application of different reading algorithms depending on the category of the text. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input text category data into a generating AI, which can then select an appropriate reading algorithm.

[0091] The text-to-speech unit can estimate the user's emotions and adjust the reading speed based on the estimated emotions. For example, if the user is relaxed, the text-to-speech unit will read slowly. If the user is stressed, the text-to-speech unit can read at a normal speed. Furthermore, if the user is in a hurry, the text-to-speech unit can read at a fast speed. This allows the reading speed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the text-to-speech unit may be performed using AI, or not using AI. For example, the text-to-speech unit can input user emotion data into the generative AI, which can then adjust the reading speed.

[0092] The text-to-speech unit can determine the reading priority based on when the text was created. For example, the text-to-speech unit may prioritize reading the most recent text. It can also postpone reading older text. Furthermore, the text-to-speech unit can dynamically adjust the reading priority according to when the text was created. This allows the reading priority to be determined based on when the text was created. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input text creation date data into a generating AI, which can then determine the reading priority.

[0093] The text-to-speech unit can adjust the reading order based on the relevance of the text during reading. For example, the text-to-speech unit may prioritize reading highly relevant text. It can also postpone reading less relevant text. Furthermore, the text-to-speech unit can dynamically adjust the reading order according to the relevance of the text. This allows the reading order to be adjusted based on the relevance of the text. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input text relevance data into a generating AI, which can then adjust the reading order.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception system can analyze the tone and speed of the user's voice to estimate their emotions. For example, if the user speaks quickly in a high-pitched voice, the reception system can estimate that the user is excited and adjust its voice command reception based on that emotion. Similarly, if the user speaks slowly in a low-pitched voice, the reception system can estimate that the user is relaxed and adjust its voice command reception based on that emotion. Furthermore, if the user speaks with an unstable voice, the reception system can estimate that the user is stressed and adjust its voice command reception based on that emotion. In this way, the system can estimate the user's emotions based on the tone and speed of their voice and adjust its voice command reception accordingly.

[0096] The editorial team can analyze a user's past editing history and suggest the optimal editing method. For example, it can prioritize suggesting editing commands that the user has frequently used in the past. Furthermore, the editorial team can predict and suggest editing commands that a user will use at specific times of the day based on their past editing history. In addition, the editorial team can analyze a user's past editing history and suggest the most efficient editing method. This allows for the suggestion of the optimal editing method based on past editing history.

[0097] The search engine can estimate the user's emotions and adjust how search results are displayed based on that estimation. For example, if the user is relaxed, it can display detailed search results. If the user is stressed, it can display concise search results. Furthermore, if the user is in a hurry, it can display search results that can be accessed quickly. This allows the search results to be displayed in accordance with the user's emotions.

[0098] The text-to-speech unit can analyze the user's past reading history and suggest the optimal reading method. For example, it can prioritize suggesting reading commands that the user has frequently used in the past. Furthermore, the text-to-speech unit can predict and suggest reading commands to be used during specific time periods based on the user's past reading history. In addition, the text-to-speech unit can analyze the user's past reading history and suggest the most efficient reading method. This allows the system to suggest the optimal reading method based on past reading history.

[0099] The reception system can estimate the user's emotions and adjust the feedback method for voice commands based on those emotions. For example, if the user is relaxed, it can provide gentle feedback. If the user is stressed, it can provide quick and clear feedback. Furthermore, if the user is in a hurry, it can provide concise and immediate feedback. This allows the feedback method for voice commands to be adjusted according to the user's emotions.

[0100] The editorial team can analyze the context of the text and make editing suggestions based on that context. For example, they can suggest appropriate words or phrases in a particular context. They can also suggest appropriate grammatical corrections based on the context. Furthermore, they can suggest appropriate stylistic changes based on the context. This allows them to make editing suggestions based on the context of the text.

[0101] The search engine can estimate the user's emotions and adjust how search results are filtered based on that estimation. For example, if the user is relaxed, it can display broad search results. If the user is stressed, it can display more refined results. Furthermore, if the user is in a hurry, it can display search results that can be accessed quickly. This allows the search results to be filtered according to the user's emotions.

[0102] The text-to-speech function can analyze the user's past reading speeds when reading text and suggest an optimal reading speed. For example, if the user has previously preferred a slow reading speed, it can suggest a similar speed. Similarly, if the user has previously preferred a fast reading speed, it can suggest a similar speed. Furthermore, if the user has previously preferred a medium reading speed, it can suggest a similar speed. This allows the system to suggest an optimal reading speed based on past reading speeds.

[0103] The reception system can estimate the user's emotions and adjust the way voice commands are received based on those estimates. For example, if the user is relaxed, a gentle reception method will be adopted. If the user is stressed, a quick and clear reception method may be adopted. Furthermore, if the user is in a hurry, a concise and immediate reception method may be adopted. This allows the reception method of voice commands to be adjusted according to the user's emotions.

[0104] The editorial team can analyze the readability of the text and make editing suggestions based on that readability. For example, they can suggest simplifying complex sentences. They can also suggest shortening long paragraphs. Furthermore, they can suggest replacing difficult words with simpler ones. In this way, they can make editing suggestions based on the readability of the text.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk accepts voice commands. For example, a user can input a voice command such as "Add a new sentence to the end of this paragraph." Step 2: The editorial team edits the text based on voice commands. For example, they insert, delete, and modify text based on voice commands. Users can insert text by entering a voice command such as "Add a new sentence to the end of this paragraph." They can also delete text by entering a voice command such as "Delete this sentence." Furthermore, users can modify text by entering a voice command such as "Replace this word with a new word." Step 3: The search unit searches for and replaces specific words or phrases. For example, a user can search for a specific word by entering a voice command such as "Search for 'specific word'." Similarly, a user can replace a specific word by entering a voice command such as "Replace 'specific word' with 'new word'." Step 4: The text-to-speech function reads the edited text aloud. For example, the user can input a voice command such as "Read this paragraph," and the edited text will be read aloud. The user can also input a voice command such as "Read this sentence," and a specific sentence will be read aloud.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] For example, the reception unit is implemented by the microphone 38B and control unit 46A of the smart device 14. For example, the editing unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the search unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the reading unit is implemented by the speaker 40B and control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] For example, the reception unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214. For example, the editing unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the search unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the reading unit is implemented by the speaker 240 and control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] For example, the reception unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314. For example, the editing unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the search unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the reading unit is implemented by the speaker 240 and control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] For example, the reception unit is implemented by the microphone 238 and control unit 46A of the robot 414. For example, the editing unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the search unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the reading unit is implemented by the speaker 240 and control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0160] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) A reception area that accepts voice commands, An editing department edits text based on voice commands received by the aforementioned reception department, A search unit that searches for and replaces a specified word or phrase, It includes a text-to-speech unit that reads the text aloud. A system characterized by the following features. (Note 2) The aforementioned editorial department, Insert, delete, and modify text based on voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned search unit, Search for and replace specific words or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reading unit, Read the edited text aloud The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is An algorithm is used to estimate the user's emotions, and the timing of voice command acceptance is adjusted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past voice command history and select the appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving a voice command, the system filters out the user's current ambient noise to remove it. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is An algorithm is used to estimate the user's emotions, and the priority of incoming voice commands is determined based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving voice commands, the system prioritizes accepting commands that are more relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When a voice command is received, the system analyzes the user's social media activity and accepts relevant commands. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned editorial department, An algorithm is used to estimate user emotions, and the way the editing is presented is adjusted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned editorial department, When editing, adjust the level of detail based on the importance of the text. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned editorial department, When editing, different editing algorithms are applied depending on the text category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned editorial department, An algorithm is used to estimate user emotions, and the length of the edit is adjusted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned editorial department, When editing, prioritize edits based on when the text was created. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned editorial department, When editing, adjust the editing order based on the relevance of the text. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, We use an algorithm to estimate user sentiment and adjust search criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, improve search accuracy based on the relationships between texts. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, When searching, perform the search based on the attribute information of the text's author. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, An algorithm is used to estimate user sentiment, and the display order of search results is adjusted based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, When searching, perform the search based on the geographical distribution of the text. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, When searching, improve search accuracy based on related literature in the text. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reading unit, An algorithm is used to estimate the user's emotions, and the way the text is read aloud is adjusted based on the estimated emotions of the user. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reading unit, When reading aloud, adjust the level of detail in the text based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reading unit, When reading aloud, different reading algorithms are applied depending on the category of the text. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reading unit, An algorithm is used to estimate the user's emotions, and the reading speed is adjusted based on the estimated emotions of the user. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reading unit, When reading aloud, the system prioritizes reading based on when the text was created. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reading unit, When reading aloud, the reading order is adjusted based on the relevance of the text. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts voice commands, An editing department edits text based on voice commands received by the aforementioned reception department, A search unit that searches for and replaces a specified word or phrase, It includes a text-to-speech unit that reads the text aloud. A system characterized by the following features.

2. The aforementioned editorial department, Insert, delete, and modify text based on voice commands. The system according to feature 1.

3. The aforementioned search unit, Search for and replace specific words or phrases. The system according to feature 1.

4. The aforementioned reading unit, Read the edited text aloud The system according to feature 1.

5. The aforementioned reception unit is An algorithm is used to estimate the user's emotions, and the timing of voice command acceptance is adjusted based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past voice command history and select the appropriate response method. The system according to feature 1.

7. The aforementioned reception unit is When receiving a voice command, the system filters out the user's current ambient noise to remove it. The system according to feature 1.

8. The aforementioned reception unit is An algorithm is used to estimate the user's emotions, and the priority of incoming voice commands is determined based on the estimated user emotions. The system according to feature 1.

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