System

Abstract

An object of a system according to an embodiment is to generate a natural sentence by utilizing personal data and conversation data of a user.SOLUTION: A system according to an embodiment includes a personal data collection unit, a conversation data collection unit, a prompt input unit, a sentence generation unit, and an output unit. The personal data collection unit collects personal data of a user. The conversation data collection unit collects daily conversation data of a user. The prompt input unit receives a prompt from a user. The sentence generation unit generates a sentence on the basis of the data collected by the personal data collection unit and the conversation data collection unit. The output unit provides the user with the sentence generated by the sentence generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately utilized personal data and conversation data of users to generate natural sentences, and there is room for improvement.

[0005] The system according to the embodiment aims to generate natural sentences by utilizing personal data and conversation data of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a personal data collection unit, a conversation data collection unit, a prompt input unit, a sentence generation unit, and an output unit. The personal data collection unit collects personal data of a user. The conversation data collection unit collects daily conversation data of the user. The prompt input unit accepts prompts from the user. The sentence generation unit generates sentences based on the data collected by the personal data collection unit and the conversation data collection unit. The output unit provides the sentences generated by the sentence generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can generate natural sentences by utilizing personal data and conversation data of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The text generation system according to the embodiment of the present invention utilizes personal data and conversation data of a user to generate text that appears as if it was written by the user himself / herself. This enables the text generation system to generate natural text that reflects the characteristics and style of the user.

[0029] A sentence generation system according to an embodiment includes a personal data collection unit, a conversation data collection unit, a prompt input unit, a sentence generation unit, and an output unit. The personal data collection unit collects personal data of a user. For example, the personal data collection unit collects the content of a user's social media posts. The personal data collection unit can also collect the user's email exchanges. The personal data collection unit can also collect the content of a user's diary entries. The conversation data collection unit collects the user's daily conversation data. For example, the conversation data collection unit collects the content of conversations the user has had via a smartphone. The conversation data collection unit can also collect the content of conversations the user has had via a voice assistant. The conversation data collection unit can also collect the content of conversations the user has had via video calls. The prompt input unit accepts prompts from a user. For example, the prompt input unit accepts a prompt input by a user such as "Please write a blog post about my travel memories." The prompt input unit can also accept a prompt input by a user such as "Please write a work report." The prompt input unit can also accept a prompt input by the user, such as "Please write a letter to a friend." The sentence generation unit generates sentences based on the data collected by the personal data collection unit and the conversation data collection unit. For example, the sentence generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate sentences that reflect the user's characteristics and style. The sentence generation unit can also generate sentences that incorporate phrases and expressions frequently used by the user. The sentence generation unit can also generate content that is in line with a theme or topic based on the prompt. The output unit provides the sentences generated by the sentence generation unit to the user. For example, the output unit can post the generated sentences to the user's blog. The output unit can also post the generated sentences to the user's social networking site. The output unit can also send the generated sentences as emails to the user.As a result, the sentence generation system according to the embodiment can generate sentences that appear as if they were written by the user themselves by utilizing the user's personal data and conversation data. For example, even when you are busy, you can use the generation AI to efficiently create sentences. Furthermore, even people who are not good at writing sentences can easily create high-quality sentences by using the generation AI.

[0030] The personal data collection unit can predict the user's future behavior and interests and generate sentences based on them. The personal data collection unit, for example, analyzes the user's past behavioral data and predicts future behavioral patterns. For example, based on places the user has visited and events they have attended in the past, it predicts the place the user is likely to visit next and generates sentences related to that place. The personal data collection unit also analyzes the user's interests and concerns and predicts future interests. For example, based on books the user has read and movies they have watched in the past, it predicts the topic the user is likely to be interested in next and generates sentences related to that topic. The personal data collection unit also analyzes the content of the user's social media posts and predicts the content of future posts. For example, based on content the user has posted in the past, it predicts the content the user is likely to post next and generates sentences related to that content. In this way, it is possible to predict the user's future behavior and interests and generate sentences based on them.

[0031] The personal data collection unit can analyze the user's visual data and generate text that reflects the user's visual features. The personal data collection unit, for example, analyzes photos taken by the user and generates text based on their content. For example, it analyzes travel photos and generates text about the places and experiences. The personal data collection unit can also analyze videos taken by the user and generate text based on their content. For example, it can analyze a video of an event and generate text about the details and impressions of the event. The personal data collection unit can also comprehensively analyze the user's visual data and generate text that reflects the user's visual features. For example, it can generate text that incorporates visual elements based on the content of photos and videos. This makes it possible to analyze the user's visual data and generate text that reflects the user's visual features.

[0032] The conversation data collection unit can analyze the tone and speed of a user's voice and generate sentences based on that. The conversation data collection unit, for example, analyzes the tone of a user's voice and generates sentences based on that tone. For example, if the tone is calm, calm sentences are generated. The conversation data collection unit also analyzes the speed of a user's voice and generates sentences based on that speed. For example, if the speed is fast, sentences that give a sense of urgency are generated. The conversation data collection unit also comprehensively analyzes the tone and speed of a user's voice and generates sentences based on the results. For example, sentences are generated according to a combination of tone and speed. This makes it possible to analyze the tone and speed of a user's voice and generate sentences based on that.

[0033] The conversation data collection unit can analyze the user's gestures and facial expressions and generate sentences based on them. The conversation data collection unit, for example, analyzes the user's gestures and generates sentences based on those gestures. For example, in the case of a waving gesture, a greeting sentence is generated. The conversation data collection unit also analyzes the user's facial expressions and generates sentences based on those expressions. For example, in the case of a smiling face, a positive sentence is generated. The conversation data collection unit also comprehensively analyzes the user's gestures and facial expressions and generates sentences based on the results. For example, a sentence is generated according to a combination of gestures and facial expressions. In this way, the user's gestures and facial expressions can be analyzed and sentences can be generated based on them.

[0034] The prompt input unit can analyze the user's past prompt history and suggest the most appropriate prompt. For example, the prompt input unit analyzes the user's past prompt history and suggests similar prompts. For example, if a prompt related to travel was input in the past, a prompt related to travel will be suggested the next time. The prompt input unit also analyzes the user's past prompt history and automatically generates the most appropriate prompt. For example, a new prompt is suggested based on the pattern of past prompts. The prompt input unit also comprehensively analyzes the user's past prompt history and suggests the most appropriate prompt. For example, the prompt is suggested based on the content and theme of past prompts. In this way, the user's past prompt history can be analyzed and the most appropriate prompt can be suggested.

[0035] The prompt input unit can analyze the user's current situation and environment and generate prompts based thereon. For example, the prompt input unit analyzes the user's current location information and generates prompts related to that location. For example, if the user is traveling, a prompt related to the travel destination is suggested. The prompt input unit can also analyze the user's current activity status and generate prompts based thereon. For example, if the user is playing a sport, a prompt related to the sport is suggested. The prompt input unit can also analyze the user's current environmental data (weather, time of day, etc.) and generate prompts based thereon. For example, on a rainy day, a prompt related to indoor activities is suggested. In this way, the user's current situation and environment can be analyzed and prompts can be generated based thereon.

[0036] The sentence generation unit can analyze the structure and patterns of the user's past sentences and generate sentences based on them. The sentence generation unit, for example, analyzes the structure of the user's past sentences and generates sentences based on that structure. For example, it generates sentences based on the arrangement of paragraphs and the length of sentences. The sentence generation unit also analyzes the patterns of the user's past sentences and generates sentences based on those patterns. For example, it generates sentences that incorporate frequently used phrases and expressions. The sentence generation unit also comprehensively analyzes the structure and patterns of the user's past sentences and generates sentences based on the results. For example, it generates sentences according to a combination of structure and pattern. This makes it possible to analyze the structure and patterns of the user's past sentences and generate sentences based on them.

[0037] The output unit can compare the generated text with the user's past text and revise it to maintain consistency. For example, the output unit compares the generated text with the user's past text and revise it to maintain consistency in style and tone. For example, the text is revised to use the same style as the past text. The output unit also compares the generated text with the user's past text and revise it to maintain consistency in content. For example, the text is revised to use the same theme or topic as the past text. The output unit also compares the generated text with the user's past text comprehensively and revise it to maintain consistency. For example, the text is revise to match all of the style, tone, and content. In this way, the generated text can be compared with the user's past text and revise it to maintain consistency.

[0038] The output unit can correct the generated sentence in real time based on user feedback. For example, the output unit collects user feedback on the generated sentence in real time and corrects the sentence based on the feedback. For example, it immediately corrects parts pointed out by the user. The output unit also analyzes the user feedback and corrects the sentence based on the results. For example, it makes corrections that reflect the user's opinions. The output unit also comprehensively analyzes the user's real-time feedback on the generated sentence and corrects the sentence based on the results. For example, it integrates multiple pieces of feedback and corrects the sentence. This allows the generated sentence to be corrected in real time based on user feedback.

[0039] The output unit can convert the generated text into different formats. For example, the output unit converts the generated text into a blog post format. For example, it adds headings and paragraphs to make it a suitable format for a blog post. The output unit also converts the generated text into a social media post format. For example, it summarizes the text into short sentences and adds hashtags and emojis to make it a suitable format for a social media post. The output unit also converts the generated text into an email format. For example, it adds a greeting and a signature to make it a suitable format for an email. In this way, the generated text can be converted into different formats.

[0040] The output unit can share the generated text with other users and improve it based on that feedback. For example, the output unit shares the generated text with other users and improves it based on that feedback. For example, it collects comments and ratings on the shared text and makes revisions based on that. The output unit also analyzes the feedback from other users and improves the text based on the results. For example, it integrates feedback from multiple users and makes revisions. The output unit also shares the generated text with other users in real time and immediately reflects that feedback to improve it. For example, it makes revisions in real time using an online collaboration tool. This allows the generated text to be shared with other users and improved based on that feedback.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The text generation system can also analyze a user's past purchasing history and generate text based on that. For example, it can generate review articles about products the user has previously purchased. It can also generate text that introduces how to use and examples of use of products purchased by the user. It can also generate text that suggests related products to products purchased by the user. In this way, it is possible to generate more personalized text by utilizing the user's purchasing history.

[0043] The sentence generation system can also analyze the user's health data and generate sentences based on it. For example, sentences containing exercise results and advice can be generated based on the user's exercise data. Also, sentences containing advice on nutritional balance can be generated based on the user's dietary data. Furthermore, sentences about sleep quality and how to improve it can be generated based on the user's sleep data. In this way, sentences useful for health management can be generated by utilizing the user's health data.

[0044] The text generation system can also generate text that provides related information based on the user's hobbies and interests. For example, if the user is interested in music, it can generate text about the latest music news and recommended artists. If the user is interested in cooking, it can generate text about new recipes and cooking tips. Furthermore, if the user is interested in traveling, it can generate text about recommended travel spots and travel plans. This makes it possible to provide information that matches the user's hobbies and interests.

[0045] The text generation system can also analyze the user's learning history and generate educational texts based on it. For example, it can generate texts to review what the user has learned in the past. It can also generate explanatory texts on new topics that interest the user. It can also generate texts that include advice and study plans according to the user's learning progress. This makes it possible to provide texts to support the user's learning.

[0046] The text generation system can also analyze the user's occupational data and generate business documents based on that data. For example, if the user works in sales, it can generate sales reports and proposals. If the user works in a managerial position, it can generate meeting minutes and business reports. Furthermore, if the user works in a creative position, it can generate proposals and presentation materials. This allows for the efficient creation of business documents tailored to the user's occupation.

[0047] The text generation system can also analyze the user's local information and generate region-specific text based on that information. For example, it can generate text containing event information and news about the area where the user lives. It can also generate text about tourist information and history of an area in which the user is interested. Furthermore, if the user is considering moving, it can generate text containing lifestyle and real estate information about that area. This makes it possible to provide useful text based on the user's local information.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The personal data collection unit collects personal data of the user. For example, the personal data collection unit collects the content of the user's social media posts, email exchanges, and diary entries. Step 2: The conversation data collection unit collects daily conversation data of the user. For example, the conversation data collection unit collects the contents of conversations the user has had via a smartphone, a voice assistant, or a video call. Step 3: The prompt input unit receives prompts from the user. For example, the prompt input unit receives prompts such as "Please write a blog post about my travel memories," "Please write a work report," or "Please write a letter to a friend." Step 4: The sentence generation unit generates sentences based on the data collected by the personal data collection unit and the conversation data collection unit. For example, the sentence generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate sentences that reflect the user's characteristics and style. It can also generate sentences that incorporate the user's frequently used phrases and expressions, or content that is in line with a theme or topic based on prompts. Step 5: The output unit provides the text generated by the text generation unit to the user. For example, the output unit can post the generated text to the user's blog or social networking site, or send it as an email to the user.

[0050] (Example 2) The text generation system according to the embodiment of the present invention utilizes personal data and conversation data of a user to generate text that appears as if it was written by the user himself / herself. This enables the text generation system to generate natural text that reflects the characteristics and style of the user.

[0051] A sentence generation system according to an embodiment includes a personal data collection unit, a conversation data collection unit, a prompt input unit, a sentence generation unit, and an output unit. The personal data collection unit collects personal data of a user. For example, the personal data collection unit collects the content of a user's social media posts. The personal data collection unit can also collect the user's email exchanges. The personal data collection unit can also collect the content of a user's diary entries. The conversation data collection unit collects the user's daily conversation data. For example, the conversation data collection unit collects the content of conversations the user has had via a smartphone. The conversation data collection unit can also collect the content of conversations the user has had via a voice assistant. The conversation data collection unit can also collect the content of conversations the user has had via video calls. The prompt input unit accepts prompts from a user. For example, the prompt input unit accepts a prompt input by a user such as "Please write a blog post about my travel memories." The prompt input unit can also accept a prompt input by a user such as "Please write a work report." The prompt input unit can also accept a prompt input by the user, such as "Please write a letter to a friend." The sentence generation unit generates sentences based on the data collected by the personal data collection unit and the conversation data collection unit. For example, the sentence generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate sentences that reflect the user's characteristics and style. The sentence generation unit can also generate sentences that incorporate phrases and expressions frequently used by the user. The sentence generation unit can also generate content that is in line with a theme or topic based on the prompt. The output unit provides the sentences generated by the sentence generation unit to the user. For example, the output unit can post the generated sentences to the user's blog. The output unit can also post the generated sentences to the user's social networking site. The output unit can also send the generated sentences as emails to the user.As a result, the sentence generation system according to the embodiment can generate sentences that appear as if they were written by the user themselves by utilizing the user's personal data and conversation data. For example, even when you are busy, you can use the generation AI to efficiently create sentences. Furthermore, even people who are not good at writing sentences can easily create high-quality sentences by using the generation AI.

[0052] The personal data collection unit can predict the user's future behavior and interests and generate sentences based on them. The personal data collection unit, for example, analyzes the user's past behavioral data and predicts future behavioral patterns. For example, based on places the user has visited and events they have attended in the past, it predicts the place the user is likely to visit next and generates sentences related to that place. The personal data collection unit also analyzes the user's interests and concerns and predicts future interests. For example, based on books the user has read and movies they have watched in the past, it predicts the topic the user is likely to be interested in next and generates sentences related to that topic. The personal data collection unit also analyzes the content of the user's social media posts and predicts the content of future posts. For example, based on content the user has posted in the past, it predicts the content the user is likely to post next and generates sentences related to that content. In this way, it is possible to predict the user's future behavior and interests and generate sentences based on them.

[0053] The personal data collection unit can analyze the user's biometric information and generate sentences that reflect the user's emotional state. The personal data collection unit, for example, analyzes the user's heart rate data and estimates the user's emotional state. For example, if the heart rate is elevated, it is determined that the user is excited, and sentences corresponding to that emotion are generated. The personal data collection unit also analyzes the user's electrodermal activity data and estimates the user's emotional state. For example, if the electrodermal activity is high, it is determined that the user is stressed, and sentences corresponding to that emotion are generated. The personal data collection unit also comprehensively analyzes the user's biometric information and estimates the user's emotional state. For example, it analyzes both the heart rate and electrodermal activity and generates sentences that reflect emotions such as a relaxed state or a tense state. In this way, the user's biometric information can be analyzed and sentences that reflect the user's emotional state can be generated.

[0054] The personal data collection unit can analyze the user's visual data and generate text that reflects the user's visual features. The personal data collection unit, for example, analyzes photos taken by the user and generates text based on their content. For example, it analyzes travel photos and generates text about the places and experiences. The personal data collection unit can also analyze videos taken by the user and generate text based on their content. For example, it can analyze a video of an event and generate text about the details and impressions of the event. The personal data collection unit can also comprehensively analyze the user's visual data and generate text that reflects the user's visual features. For example, it can generate text that incorporates visual elements based on the content of photos and videos. This makes it possible to analyze the user's visual data and generate text that reflects the user's visual features.

[0055] The conversation data collection unit can analyze the tone and speed of a user's voice and generate sentences based on that. The conversation data collection unit, for example, analyzes the tone of a user's voice and generates sentences based on that tone. For example, if the tone is calm, calm sentences are generated. The conversation data collection unit also analyzes the speed of a user's voice and generates sentences based on that speed. For example, if the speed is fast, sentences that give a sense of urgency are generated. The conversation data collection unit also comprehensively analyzes the tone and speed of a user's voice and generates sentences based on the results. For example, sentences are generated according to a combination of tone and speed. This makes it possible to analyze the tone and speed of a user's voice and generate sentences based on that.

[0056] The conversation data collection unit can analyze the user's gestures and facial expressions and generate sentences based on them. The conversation data collection unit, for example, analyzes the user's gestures and generates sentences based on those gestures. For example, in the case of a waving gesture, a greeting sentence is generated. The conversation data collection unit also analyzes the user's facial expressions and generates sentences based on those expressions. For example, in the case of a smiling face, a positive sentence is generated. The conversation data collection unit also comprehensively analyzes the user's gestures and facial expressions and generates sentences based on the results. For example, a sentence is generated according to a combination of gestures and facial expressions. In this way, the user's gestures and facial expressions can be analyzed and sentences can be generated based on them.

[0057] The prompt input unit can analyze the user's past prompt history and suggest the most appropriate prompt. For example, the prompt input unit analyzes the user's past prompt history and suggests similar prompts. For example, if a prompt related to travel was input in the past, a prompt related to travel will be suggested the next time. The prompt input unit also analyzes the user's past prompt history and automatically generates the most appropriate prompt. For example, a new prompt is suggested based on the pattern of past prompts. The prompt input unit also comprehensively analyzes the user's past prompt history and suggests the most appropriate prompt. For example, the prompt is suggested based on the content and theme of past prompts. In this way, the user's past prompt history can be analyzed and the most appropriate prompt can be suggested.

[0058] The prompt input unit can analyze the user's current situation and environment and generate prompts based thereon. For example, the prompt input unit analyzes the user's current location information and generates prompts related to that location. For example, if the user is traveling, a prompt related to the travel destination is suggested. The prompt input unit can also analyze the user's current activity status and generate prompts based thereon. For example, if the user is playing a sport, a prompt related to the sport is suggested. The prompt input unit can also analyze the user's current environmental data (weather, time of day, etc.) and generate prompts based thereon. For example, on a rainy day, a prompt related to indoor activities is suggested. In this way, the user's current situation and environment can be analyzed and prompts can be generated based thereon.

[0059] The prompt input unit can suggest prompts according to the emotional state of the user. For example, the prompt input unit analyzes the emotional state of the user in real time and suggests prompts according to that emotion. For example, if the user is feeling positive, it suggests prompts with a fun theme. The prompt input unit also analyzes the emotional state of the user and suggests prompts according to changes in emotion. For example, if the user is feeling stressed, it suggests prompts with a relaxing theme. The prompt input unit also comprehensively analyzes the user's emotional data and suggests prompts that are optimal for that emotion. For example, if the user is excited, it suggests prompts with a theme that will make use of that excitement. In this way, it is possible to suggest prompts according to the emotional state of the user.

[0060] The sentence generation unit can analyze the structure and patterns of the user's past sentences and generate sentences based on them. The sentence generation unit, for example, analyzes the structure of the user's past sentences and generates sentences based on that structure. For example, it generates sentences based on the arrangement of paragraphs and the length of sentences. The sentence generation unit also analyzes the patterns of the user's past sentences and generates sentences based on those patterns. For example, it generates sentences that incorporate frequently used phrases and expressions. The sentence generation unit also comprehensively analyzes the structure and patterns of the user's past sentences and generates sentences based on the results. For example, it generates sentences according to a combination of structure and pattern. This makes it possible to analyze the structure and patterns of the user's past sentences and generate sentences based on them.

[0061] The sentence generation unit can generate sentences in a style and tone that correspond to the user's emotional state. For example, the sentence generation unit analyzes the user's emotional state in real time and generates sentences in a style and tone that correspond to the emotion. For example, if the user has positive emotions, the sentence generation unit generates sentences in a cheerful style and tone. The sentence generation unit also analyzes the user's emotional state and generates sentences in a style and tone that correspond to changes in emotion. For example, if the user is feeling stressed, the sentence generation unit generates sentences in a calm style and tone. The sentence generation unit also comprehensively analyzes the user's emotional data and generates sentences in a style and tone that are optimal for the emotion. For example, if the user is excited, the sentences are generated in a style and tone that reflects the excitement. In this way, sentences can be generated in a style and tone that correspond to the user's emotional state.

[0062] The output unit can compare the generated text with the user's past text and revise it to maintain consistency. For example, the output unit compares the generated text with the user's past text and revise it to maintain consistency in style and tone. For example, the text is revised to use the same style as the past text. The output unit also compares the generated text with the user's past text and revise it to maintain consistency in content. For example, the text is revised to use the same theme or topic as the past text. The output unit also compares the generated text with the user's past text comprehensively and revise it to maintain consistency. For example, the text is revise to match all of the style, tone, and content. In this way, the generated text can be compared with the user's past text and revise it to maintain consistency.

[0063] The output unit can correct the generated sentence in real time based on user feedback. For example, the output unit collects user feedback on the generated sentence in real time and corrects the sentence based on the feedback. For example, it immediately corrects parts pointed out by the user. The output unit also analyzes the user feedback and corrects the sentence based on the results. For example, it makes corrections that reflect the user's opinions. The output unit also comprehensively analyzes the user's real-time feedback on the generated sentence and corrects the sentence based on the results. For example, it integrates multiple pieces of feedback and corrects the sentence. This allows the generated sentence to be corrected in real time based on user feedback.

[0064] The output unit can reflect feedback according to the user's emotional state and revise the text. The output unit, for example, analyzes the user's emotional state in real time and revise the text by reflecting feedback according to the emotion. For example, if the user has positive emotions, the output unit makes revisions to reflect the emotion. The output unit also analyzes the user's emotional state and revises the text by reflecting feedback according to changes in emotion. For example, if the user is feeling stressed, the output unit makes revisions to alleviate the emotion. The output unit also comprehensively analyzes the user's emotional data and revises the text by reflecting feedback that is optimal for the emotion. For example, if the user is excited, the output unit makes revisions that make use of the excitement. In this way, the output unit can reflect feedback according to the user's emotional state and revise the text.

[0065] The output unit can convert the generated text into different formats. For example, the output unit converts the generated text into a blog post format. For example, it adds headings and paragraphs to make it a suitable format for a blog post. The output unit also converts the generated text into a social media post format. For example, it summarizes the text into short sentences and adds hashtags and emojis to make it a suitable format for a social media post. The output unit also converts the generated text into an email format. For example, it adds a greeting and a signature to make it a suitable format for an email. In this way, the generated text can be converted into different formats.

[0066] The output unit can share the generated text with other users and improve it based on that feedback. For example, the output unit shares the generated text with other users and improves it based on that feedback. For example, it collects comments and ratings on the shared text and makes revisions based on that. The output unit also analyzes the feedback from other users and improves the text based on the results. For example, it integrates feedback from multiple users and makes revisions. The output unit also shares the generated text with other users in real time and immediately reflects that feedback to improve it. For example, it makes revisions in real time using an online collaboration tool. This allows the generated text to be shared with other users and improved based on that feedback.

[0067] The output unit can output text in an optimal format according to the user's emotional state. The output unit, for example, analyzes the user's emotional state in real time and outputs text in a format optimal for that emotion. For example, if the user has positive emotions, the output unit outputs text in a format with a bright design. The output unit also analyzes the user's emotional state and outputs text in a format according to changes in emotion. For example, if the user is feeling stressed, the output unit outputs text in a format with a calm design. The output unit also comprehensively analyzes the user's emotional data and outputs text in a format optimal for that emotion. For example, if the user is excited, the output unit outputs text in a format designed to reflect that excitement. This makes it possible to output text in an optimal format according to the user's emotional state.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The text generation system can also analyze a user's past purchasing history and generate text based on that. For example, it can generate review articles about products the user has previously purchased. It can also generate text that introduces how to use and examples of use of products purchased by the user. It can also generate text that suggests related products to products purchased by the user. In this way, it is possible to generate more personalized text by utilizing the user's purchasing history.

[0070] The sentence generation system can also analyze the user's health data and generate sentences based on it. For example, sentences containing exercise results and advice can be generated based on the user's exercise data. Also, sentences containing advice on nutritional balance can be generated based on the user's dietary data. Furthermore, sentences about sleep quality and how to improve it can be generated based on the user's sleep data. In this way, sentences useful for health management can be generated by utilizing the user's health data.

[0071] The text generation system can also estimate the user's emotional state and generate text to improve the user's mood based on the estimated emotion. For example, if the user is feeling stressed, the system can generate text containing relaxing advice and encouraging words. If the user is sad, the system can generate text containing messages of comfort and encouragement. Furthermore, if the user is happy, the system can generate positive text to share that joy. This makes it possible to provide support according to the user's emotional state.

[0072] The text generation system can also generate text that provides related information based on the user's hobbies and interests. For example, if the user is interested in music, it can generate text about the latest music news and recommended artists. If the user is interested in cooking, it can generate text about new recipes and cooking tips. Furthermore, if the user is interested in traveling, it can generate text about recommended travel spots and travel plans. This makes it possible to provide information that matches the user's hobbies and interests.

[0073] The text generation system can also estimate the user's emotional state and generate poems or short stories to express the user's emotions based on the estimated emotions. For example, if the user has romantic feelings, a romantic poem expressing those feelings can be generated. If the user has an adventurous spirit, an adventure novel expressing those feelings can be generated. Furthermore, if the user is in an introspective mood, a philosophical essay expressing those feelings can be generated. This makes it possible to provide texts that express the user's emotions in a rich way.

[0074] The text generation system can also analyze the user's learning history and generate educational texts based on it. For example, it can generate texts to review what the user has learned in the past. It can also generate explanatory texts on new topics that interest the user. It can also generate texts that include advice and study plans according to the user's learning progress. This makes it possible to provide texts to support the user's learning.

[0075] The text generation system can also estimate the user's emotional state and generate diary-style text to record the user's emotions based on the estimated emotions. For example, if the user is feeling happy, a positive diary can be generated to record the events and emotions of the day. If the user is feeling anxious, an introspective diary can be generated to help the user sort out those emotions. Furthermore, if the user is excited, an energetic diary can be generated to record that excitement. This makes it possible to provide a tool for recording and reflecting on the user's emotions.

[0076] The text generation system can also analyze the user's occupational data and generate business documents based on that data. For example, if the user works in sales, it can generate sales reports and proposals. If the user works in a managerial position, it can generate meeting minutes and business reports. Furthermore, if the user works in a creative position, it can generate proposals and presentation materials. This allows for the efficient creation of business documents tailored to the user's occupation.

[0077] The text generation system can also estimate the user's emotional state and generate SNS posts to share the user's emotions based on the estimated emotions. For example, if the user is feeling happy, a positive SNS post to share that joy can be generated. If the user is feeling sad, a sympathetic SNS post to share that emotion can be generated. Furthermore, if the user is feeling surprised, an interesting SNS post to share that surprise can be generated. This provides a tool for sharing the user's emotions with others.

[0078] The text generation system can also analyze the user's local information and generate region-specific text based on that information. For example, it can generate text containing event information and news about the area where the user lives. It can also generate text about tourist information and history of an area in which the user is interested. Furthermore, if the user is considering moving, it can generate text containing lifestyle and real estate information about that area. This makes it possible to provide useful text based on the user's local information.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The personal data collection unit collects personal data of the user. For example, the personal data collection unit collects the content of the user's social media posts, email exchanges, and diary entries. Step 2: The conversation data collection unit collects daily conversation data of the user. For example, the conversation data collection unit collects the contents of conversations the user has had via a smartphone, a voice assistant, or a video call. Step 3: The prompt input unit receives prompts from the user. For example, the prompt input unit receives prompts such as "Please write a blog post about my travel memories," "Please write a work report," or "Please write a letter to a friend." Step 4: The sentence generation unit generates sentences based on the data collected by the personal data collection unit and the conversation data collection unit. For example, the sentence generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate sentences that reflect the user's characteristics and style. It can also generate sentences that incorporate the user's frequently used phrases and expressions, or content that is in line with a theme or topic based on prompts. Step 5: The output unit provides the text generated by the text generation unit to the user. For example, the output unit can post the generated text to the user's blog or social networking site, or send it as an email to the user.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0109] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0125] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] 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.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a personal data collection unit that collects personal data of users; a conversation data collection unit that collects daily conversation data of a user; a prompt input unit that accepts a prompt from a user; a sentence generation unit that generates sentences based on the data collected by the personal data collection unit and the conversation data collection unit; an output unit that provides the sentence generated by the sentence generation unit to a user; A system characterized by:

2. The personal data collection unit Predict the user's future behavior and interests and generate sentences based on them 2. The system of claim 1.

3. The personal data collection unit Analyzing the user's biometric information and generating sentences that reflect the user's emotional state 2. The system of claim 1.

4. The personal data collection unit Analyzing the visual data of the user and generating sentences that reflect the visual characteristics 2. The system of claim 1.

5. The conversation data collection unit Analyze the tone and speed of the user's voice and generate sentences based on that.

2. The system of claim 1.

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