System

Abstract

An object of a system according to an embodiment is to efficiently search for information stored by a user and quickly acquire necessary information.SOLUTION: A system includes an information collection part, an analysis part, a search engine provision part, and a file presentation part. The information collection unit collects information stored in a personal computer, a cloud, a smartphone, or the like of the user. The analysis unit analyzes the information collected by the information collection unit. The search engine providing unit provides a search engine optimized for the user based on the information analyzed by the analysis unit. The file presenter quickly presents necessary information, files, and memos using the search engine provided by the search engine provider.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to efficiently search for saved information and quickly obtain required information.

[0005] The system according to the embodiment aims to enable a user to efficiently search for saved information and quickly obtain required information. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a search engine providing unit, and a file presentation unit. The information collection unit collects information stored on a user's computer, cloud, smartphone, etc. The analysis unit analyzes the information collected by the information collection unit. The search engine providing unit provides a search engine optimized for the user based on the information analyzed by the analysis unit. The file presentation unit quickly presents necessary information, files, and notes using the search engine provided by the search engine providing unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to efficiently search for information stored by the user and quickly obtain required information. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 Memory Finder system according to an embodiment of the present invention is a system that allows a user to quickly recall past decisions and learnings and efficiently find necessary information. As a result, the Memory Finder system allows a user to quickly recall past decisions and learnings and efficiently find necessary information.

[0029] The Memory Finder system according to the embodiment includes an information collection unit, an analysis unit, a search engine providing unit, and a file presentation unit. The information collection unit collects information stored on a user's computer, cloud storage, smartphone, etc. For example, the information collection unit scans files on the user's computer, acquires data stored in cloud storage, and collects notes and images on the smartphone. The information collection unit can also periodically scan these devices to collect new information. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit can analyze the content of documents using text analysis technology. The analysis unit can also recognize text and objects in images using image analysis technology. The analysis unit can also convert the content of voice memos into text using voice analysis technology. The search engine providing unit provides a search engine optimized for the user based on the information analyzed by the analysis unit. For example, the search engine providing unit can analyze the user's past search history and click history to identify frequently searched keywords and phrases. The search engine providing unit can also optimize search results based on the user's search behavior using a machine learning algorithm. The file presentation unit quickly presents necessary information, files, and notes using a search engine provided by the search engine providing unit. For example, the file presentation unit displays files and notes related to keywords searched by the user. The file presentation unit can also prioritize displaying related information based on the user's past actions and learning history. This allows the Memory Finder system according to the embodiment to quickly recall past decisions and learning content and efficiently find necessary information. For example, when a user issues an instruction such as "Find last year's project report," the Memory Finder system searches for and quickly presents related files. Furthermore, by preferentially displaying files and notes that the user frequently accesses, the user can quickly find necessary information.

[0030] The information collection unit can analyze voice memos and video messages, convert them into text using voice recognition technology, and make them searchable. The information collection unit, for example, analyzes voice memos recorded by a user on a smartphone or computer and converts them into text using voice recognition technology. For example, meeting minutes and idea memos can be automatically converted from audio to text and made searchable. The information collection unit can also analyze video messages filmed by a user and convert the audio in the video into text using voice recognition technology. For example, the content of the video message can be converted into text and made searchable. The information collection unit can also convert voice memos into text in real time using voice recognition technology. For example, the content spoken by the user can be converted into text in real time, making it instantly searchable. In this way, by converting voice memos and video messages into text and making them searchable, users can quickly find the information they need.

[0031] The information collection unit can collect information from social media accounts, analyze posts and messages, and present related information. For example, the information collection unit collects posts and messages from a user's social media accounts, which the generation AI analyzes. For example, it analyzes the content of posts on Facebook and Twitter and presents related information. The information collection unit can also analyze messages from social media accounts and present related information. For example, it can analyze messages that a user has sent and received in the past and display related information in search results. The information collection unit can also analyze images and videos from social media accounts and present related information. For example, it can analyze images posted on Instagram and display related information. In this way, by collecting information from social media accounts and presenting related information, a user can quickly find the information they need.

[0032] The information collection unit can collect data from the wearable device and present relevant information based on the health condition and activity history. For example, the information collection unit collects heart rate and activity logs from the user's wearable device, and the generation AI analyzes the collected data. For example, health advice is provided based on heart rate data during exercise. The information collection unit can also analyze the activity log from the wearable device and present relevant information. For example, health information is displayed based on the number of steps taken and exercise time. The information collection unit can also collect data from the wearable device in real time, instantly analyze it, and present relevant information. For example, heart rate data during exercise is analyzed in real time and health advice is instantly provided. This allows the user to quickly find information based on their health condition and activity history by collecting data from the wearable device and presenting relevant information.

[0033] The information collection unit can collect information from a calendar app or a task management app and analyze information related to schedules and tasks. The information collection unit, for example, collects schedule information from a user's calendar app, which the generation AI analyzes. For example, the information collection unit presents related materials and notes based on meeting and event schedules. The information collection unit can also collect task information from a task management app and present related information. For example, related information can be displayed based on task deadlines and progress. The information collection unit can also collect information from a calendar app or a task management app in real time, instantly analyze it, and present related information. For example, it can analyze meeting schedules in real time and instantly display related materials. This allows the user to quickly find information related to their schedules and tasks by collecting information from a calendar app or a task management app and analyzing related information.

[0034] The search engine providing unit can analyze search history and click history to provide a search algorithm optimized for each individual user. For example, the search engine providing unit can analyze a user's search history to identify frequently searched keywords and phrases. For example, the search engine providing unit can prioritize displaying information related to a specific topic. The search engine providing unit can also analyze a user's click history to display related information. For example, related information can be displayed based on links clicked in the past. The search engine providing unit can also use a machine learning algorithm to optimize search results based on the user's search behavior. For example, search results can be customized based on the user's past search history. In this way, by analyzing the search history and click history and providing a search algorithm optimized for each individual user, the user can quickly find the information they need.

[0035] The search engine providing unit can learn from users' feedback on past search results and improve the accuracy of search results. For example, the search engine providing unit collects users' feedback on past search results, and the generation AI learns from this. For example, the search algorithm is improved based on search results rated by users. The search engine providing unit can also analyze user feedback and improve the accuracy of search results. For example, the search results are optimized based on links clicked by users and information rated by users. The search engine providing unit can also collect user feedback in real time and immediately reflect this in search results. For example, information rated by users is immediately reflected in search results. In this way, the search engine providing unit can learn from feedback on past search results and improve the accuracy of search results, allowing users to quickly find the information they need.

[0036] The search engine providing unit can analyze geographical location information and provide search results based on the location information. For example, the search engine providing unit collects the user's geographical location information, which is then analyzed by the generation AI. For example, nearby restaurants and stores are displayed in the search results based on the user's current location. The search engine providing unit can also display related information based on the user's location information. For example, news and event information related to a specific area is displayed. The search engine providing unit can also collect location information in real time and instantly reflect this in the search results. For example, information based on the user's current location is instantly displayed while the user is on the move. This allows the user to quickly find the information they need by analyzing geographical location information and providing search results based on the location information.

[0037] The search engine providing unit synchronizes information between devices and can provide consistent search results from any device. For example, the search engine providing unit synchronizes information between multiple devices of a user, and the generation AI provides consistent search results. For example, information searched on a smartphone is displayed in the same way on a computer. The search engine providing unit can also synchronize information between devices using cloud storage. For example, data stored on the cloud can be synchronized in real time, making it accessible from any device. The search engine providing unit can also share data between devices and unify search results. For example, a user's search history and click history can be shared between devices to provide consistent search results. This allows information to be synchronized between devices and consistent search results can be provided, enabling users to quickly find the information they need from any device.

[0038] The analysis unit can analyze the user's past behavioral data in chronological order, identify behavioral patterns, and provide related information. For example, the analysis unit can analyze the user's past behavioral data in chronological order and identify behavioral patterns. For example, related information is provided based on activities performed on specific days of the week. The analysis unit can also analyze the user's behavioral data and identify frequently performed activities. For example, related information is displayed based on activities performed during specific time periods. The analysis unit can also analyze the user's behavioral data in real time, instantly identify behavioral patterns, and provide related information. For example, if the user is in a specific location, information related to that location is instantly displayed. In this way, by analyzing the user's past behavioral data in chronological order, identifying behavioral patterns, and providing related information, the user can quickly find the information they need.

[0039] The analysis unit can analyze the user's learning history and suggest the next content to learn based on the user's learning progress and level of understanding. The analysis unit, for example, analyzes the user's learning history to grasp the user's learning progress and level of understanding. For example, it suggests the next content to learn based on the content previously learned and test results. The analysis unit can also analyze the user's learning history and evaluate the user's learning progress. For example, it suggests the next content to learn based on the study time and the level of understanding of the learning content. The analysis unit can also analyze the user's learning history in real time, instantly evaluate the user's learning progress and level of understanding, and suggest the next content to learn. For example, if the user's level of understanding is determined to be low during study, it can instantly suggest supplementary learning content. In this way, by analyzing the learning history and suggesting the next content to learn based on the user's learning progress and level of understanding, the user can efficiently progress through their studies.

[0040] The analysis unit can analyze the user's past purchasing history and provide related information based on the purchasing pattern. The analysis unit, for example, analyzes the user's past purchasing history and identifies purchasing patterns. For example, it provides information related to products and services that are purchased regularly. The analysis unit can also analyze the user's purchasing history and identify products that are frequently purchased. For example, it displays information related to products of a specific brand or category. The analysis unit can also analyze the user's purchasing history in real time and instantly identify purchasing patterns to provide related information. For example, if the user purchases a specific product, information related to that product is instantly displayed. In this way, by analyzing the purchasing history and providing related information based on the purchasing pattern, the user can quickly find the information they need.

[0041] The analysis unit can analyze the user's past travel history and provide information related to travel destinations. The analysis unit, for example, analyzes the user's past travel history and provides information related to travel destinations. For example, information related to tourist spots and accommodations visited in the past is displayed. The analysis unit can also analyze the user's travel history and identify frequently visited travel destinations. For example, information related to a specific area is displayed. The analysis unit can also analyze the user's travel history in real time and instantly provide information related to travel destinations. For example, if the user is currently traveling, information related to that travel destination is instantly displayed. In this way, by analyzing the travel history and providing information related to the travel destination, the user can quickly find the information they need.

[0042] The file presentation unit can analyze the update history of the user's files and notes and prioritize presenting the most recent information. The file presentation unit, for example, analyzes the update history of the user's files and notes and prioritize presenting the most recent information. For example, recently updated files and notes are displayed at the top of search results. The file presentation unit can also analyze the update history of the user's files and notes and prioritize displaying important updates. For example, files and notes to which important changes have been made are displayed at the top. The file presentation unit can also analyze the update history of the user's files and notes in real time and instantly present the most recent information. For example, immediately after a user updates a file, the file is displayed at the top of search results. In this way, by analyzing the update history of files and notes and prioritize presenting the most recent information, the user can quickly find the information they need.

[0043] The file presentation unit can automatically tag a user's files and notes and present related information based on the tags. For example, the file presentation unit uses a generation AI to automatically tag a user's files and notes and present related information based on the tags. For example, it can display files and notes related to specific keywords. The file presentation unit can also analyze a user's files and notes and automatically generate appropriate tags. For example, it can generate tags based on the content of a document and display related information. The file presentation unit can also tag a user's files and notes in real time and instantly present related information. For example, immediately after a new note is created, information related to that note can be instantly displayed. This allows users to quickly find the information they need by automatically tagging files and notes and presenting related information based on tags.

[0044] The file presentation unit can automatically classify a user's files and notes and present them organized by category. For example, the file presentation unit uses a generation AI to automatically classify a user's files and notes and present them organized by category. For example, it can classify files into categories such as work-related and personal-related. The file presentation unit can also analyze a user's files and notes and automatically classify them into appropriate categories. For example, it can generate categories based on the content of a document and display related information. The file presentation unit can also classify a user's files and notes in real time and instantly organize and present them organized by category. For example, immediately after a new note is created, it can be classified into the appropriate category and displayed immediately. This allows users to quickly find the information they need by automatically classifying files and notes and presenting them organized by category.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The Memory Finder system can also be equipped with a health management unit that collects the user's biometric information and provides information based on their health condition. For example, it can monitor the user's heart rate and blood pressure and provide relaxation advice if it determines that the user is under high stress. It can also analyze sleep data and provide advice on how to improve sleep if the quality is poor. It can also suggest appropriate exercise plans based on exercise data. This makes it possible to provide information based on the user's health condition and provide more personalized support.

[0047] The Memory Finder system can also include a hobby analysis unit that provides information based on the user's hobbies and interests. For example, it can collect information related to hobbies the user has previously searched for and suggest new hobbies. It can also suggest related events and products based on events the user has attended and items they have purchased. It can also provide related news and articles based on the user's interests. This makes it possible to provide information based on the user's hobbies and interests, supporting a more fulfilling lifestyle.

[0048] The Memory Finder system can also be equipped with a learning support unit that provides information based on the user's learning style. For example, if the user prefers visual learning, visual content can be provided first. If the user prefers auditory learning, audio content can be provided. Furthermore, it can also suggest what the user should learn next based on their learning progress. This makes it possible to provide information tailored to the user's learning style, supporting more effective learning.

[0049] The Memory Finder system can also be equipped with a travel support unit that analyzes the user's past travel history and suggests the next travel destination. For example, it can suggest the next travel destination based on the places and accommodations the user has visited in the past. It can also provide related tourist spots and activities based on the user's travel history. It can also suggest optimal travel plans based on the user's travel style. This makes it possible to provide information based on the user's travel history, supporting a more fulfilling travel experience.

[0050] The Memory Finder system can also be equipped with a purchasing support unit that analyzes a user's purchasing history and suggests the next product to purchase. For example, it can suggest related products based on products the user has previously purchased. It can also predict the next product to be purchased based on the user's purchasing patterns. It can also prioritize the display of products from specific brands or categories based on the user's purchasing history. This makes it possible to provide information based on the user's purchasing history and support more efficient purchasing.

[0051] The Memory Finder system can also include a prediction unit that analyzes a user's past behavioral data and predicts future behavior based on their behavioral patterns. For example, it can suggest the next action a user should take based on past actions taken on a specific day or time of day. It can also predict future behavior based on the user's behavioral patterns and provide related information. Furthermore, it can analyze the user's behavioral data in real time and instantly predict future behavior. This makes it possible to provide information based on the user's behavioral patterns, supporting more efficient behavior.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The information collection unit collects information stored on the user's computer, cloud, smartphone, etc. For example, the information collection unit scans files on the user's computer, obtains data stored in cloud storage, and collects notes and images on the smartphone. The information collection unit can also periodically scan these devices to collect new information. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit can use text analysis technology to analyze the content of documents, image analysis technology to recognize text and objects in images, and voice analysis technology to convert the content of voice memos into text. Step 3: The search engine provider provides the user with an optimized search engine based on the information analyzed by the analysis unit. For example, the search engine provider may analyze the user's past search history and click history to identify frequently searched keywords and phrases. The search engine provider may also use machine learning algorithms to optimize search results based on the user's search behavior. Step 4: The file presentation unit quickly presents necessary information, files, and notes using the search engine provided by the search engine provision unit. For example, the file presentation unit may display files and notes related to keywords searched by the user, and may also preferentially display related information based on the user's past behavior and learning history.

[0054] (Example 2) The Memory Finder system according to an embodiment of the present invention is a system that allows a user to quickly recall past decisions and learnings and efficiently find necessary information. As a result, the Memory Finder system allows a user to quickly recall past decisions and learnings and efficiently find necessary information.

[0055] The Memory Finder system according to the embodiment includes an information collection unit, an analysis unit, a search engine providing unit, and a file presentation unit. The information collection unit collects information stored on a user's computer, cloud storage, smartphone, etc. For example, the information collection unit scans files on the user's computer, acquires data stored in cloud storage, and collects notes and images on the smartphone. The information collection unit can also periodically scan these devices to collect new information. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit can analyze the content of documents using text analysis technology. The analysis unit can also recognize text and objects in images using image analysis technology. The analysis unit can also convert the content of voice memos into text using voice analysis technology. The search engine providing unit provides a search engine optimized for the user based on the information analyzed by the analysis unit. For example, the search engine providing unit can analyze the user's past search history and click history to identify frequently searched keywords and phrases. The search engine providing unit can also optimize search results based on the user's search behavior using a machine learning algorithm. The file presentation unit quickly presents necessary information, files, and notes using a search engine provided by the search engine providing unit. For example, the file presentation unit displays files and notes related to keywords searched by the user. The file presentation unit can also prioritize displaying related information based on the user's past actions and learning history. This allows the Memory Finder system according to the embodiment to quickly recall past decisions and learning content and efficiently find necessary information. For example, when a user issues an instruction such as "Find last year's project report," the Memory Finder system searches for and quickly presents related files. Furthermore, by preferentially displaying files and notes that the user frequently accesses, the user can quickly find necessary information.

[0056] The information collection unit can analyze voice memos and video messages, convert them into text using voice recognition technology, and make them searchable. The information collection unit, for example, analyzes voice memos recorded by a user on a smartphone or computer and converts them into text using voice recognition technology. For example, meeting minutes and idea memos can be automatically converted from audio to text and made searchable. The information collection unit can also analyze video messages filmed by a user and convert the audio in the video into text using voice recognition technology. For example, the content of the video message can be converted into text and made searchable. The information collection unit can also convert voice memos into text in real time using voice recognition technology. For example, the content spoken by the user can be converted into text in real time, making it instantly searchable. In this way, by converting voice memos and video messages into text and making them searchable, users can quickly find the information they need.

[0057] The information collection unit can collect information from social media accounts, analyze posts and messages, and present related information. For example, the information collection unit collects posts and messages from a user's social media accounts, which the generation AI analyzes. For example, it analyzes the content of posts on Facebook and Twitter and presents related information. The information collection unit can also analyze messages from social media accounts and present related information. For example, it can analyze messages that a user has sent and received in the past and display related information in search results. The information collection unit can also analyze images and videos from social media accounts and present related information. For example, it can analyze images posted on Instagram and display related information. In this way, by collecting information from social media accounts and presenting related information, a user can quickly find the information they need.

[0058] The information collecting unit can use the emotion estimation function to preferentially collect information recorded by a user with a specific emotion and provide search results based on the emotion. The information collecting unit, for example, performs emotion estimation on information recorded by a user and preferentially collects information with a strong specific emotion. For example, notes with a strong emotion of joy or surprise are preferentially displayed in the search results. The information collecting unit can also use the emotion estimation function to analyze information recorded by a user with a specific emotion and provide search results based on the emotion. For example, information with a strong positive emotion is preferentially displayed. The information collecting unit can also use the emotion estimation function to analyze information recorded by a user with a specific emotion in real time and immediately reflect the analysis in the search results. For example, information recorded by a user with emotion is immediately displayed in the search results. This allows the user to quickly find the information they need by providing search results based on emotions.

[0059] The information collection unit can collect data from the wearable device and present relevant information based on the health condition and activity history. For example, the information collection unit collects heart rate and activity logs from the user's wearable device, and the generation AI analyzes the collected data. For example, health advice is provided based on heart rate data during exercise. The information collection unit can also analyze the activity log from the wearable device and present relevant information. For example, health information is displayed based on the number of steps taken and exercise time. The information collection unit can also collect data from the wearable device in real time, instantly analyze it, and present relevant information. For example, heart rate data during exercise is analyzed in real time and health advice is instantly provided. This allows the user to quickly find information based on their health condition and activity history by collecting data from the wearable device and presenting relevant information.

[0060] The information collection unit can collect information from a calendar app or a task management app and analyze information related to schedules and tasks. The information collection unit, for example, collects schedule information from a user's calendar app, which the generation AI analyzes. For example, the information collection unit presents related materials and notes based on meeting and event schedules. The information collection unit can also collect task information from a task management app and present related information. For example, related information can be displayed based on task deadlines and progress. The information collection unit can also collect information from a calendar app or a task management app in real time, instantly analyze it, and present related information. For example, it can analyze meeting schedules in real time and instantly display related materials. This allows the user to quickly find information related to their schedules and tasks by collecting information from a calendar app or a task management app and analyzing related information.

[0061] The information collecting unit can use the emotion estimation function to preferentially collect information recorded when the user has a specific emotion and provide search results based on the emotion. For example, the information collecting unit can use the emotion estimation function to preferentially collect information recorded when the user has a specific emotion. For example, information with a strong positive emotion is preferentially displayed in the search results. The information collecting unit can also use the emotion estimation function to analyze information recorded when the user has a specific emotion and provide search results based on the emotion. For example, information with a strong negative emotion is preferentially displayed. The information collecting unit can also use the emotion estimation function to analyze information recorded when the user has a specific emotion in real time and immediately reflect the analysis in the search results. For example, information recorded by the user with emotion is immediately displayed in the search results. This allows the user to quickly find the information they need by providing search results based on their emotions.

[0062] The search engine providing unit can analyze search history and click history to provide a search algorithm optimized for each individual user. For example, the search engine providing unit can analyze a user's search history to identify frequently searched keywords and phrases. For example, the search engine providing unit can prioritize displaying information related to a specific topic. The search engine providing unit can also analyze a user's click history to display related information. For example, related information can be displayed based on links clicked in the past. The search engine providing unit can also use a machine learning algorithm to optimize search results based on the user's search behavior. For example, search results can be customized based on the user's past search history. In this way, by analyzing the search history and click history and providing a search algorithm optimized for each individual user, the user can quickly find the information they need.

[0063] The search engine providing unit can learn from users' feedback on past search results and improve the accuracy of search results. For example, the search engine providing unit collects users' feedback on past search results, and the generation AI learns from this. For example, the search algorithm is improved based on search results rated by users. The search engine providing unit can also analyze user feedback and improve the accuracy of search results. For example, the search results are optimized based on links clicked by users and information rated by users. The search engine providing unit can also collect user feedback in real time and immediately reflect this in search results. For example, information rated by users is immediately reflected in search results. In this way, the search engine providing unit can learn from feedback on past search results and improve the accuracy of search results, allowing users to quickly find the information they need.

[0064] The search engine providing unit can use the emotion estimation function to provide search results according to the user's emotional state and prioritize displaying information for reducing stress. The search engine providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time. For example, if it detects a high stress state, it prioritizes displaying information that will help relax. The search engine providing unit can also customize search results based on the user's emotional state. For example, it prioritizes displaying information that will elicit positive emotions. The search engine providing unit can also use the emotion estimation function to monitor the user's emotional state in real time and instantly reflect this in the search results. For example, if the user is feeling stressed, it instantly displays information that will help relax. In this way, by providing search results according to the emotional state and prioritize displaying information that will help reduce stress, the user can quickly find the information they need.

[0065] The search engine providing unit can analyze geographical location information and provide search results based on the location information. For example, the search engine providing unit collects the user's geographical location information, which is then analyzed by the generation AI. For example, nearby restaurants and stores are displayed in the search results based on the user's current location. The search engine providing unit can also display related information based on the user's location information. For example, news and event information related to a specific area is displayed. The search engine providing unit can also collect location information in real time and instantly reflect this in the search results. For example, information based on the user's current location is instantly displayed while the user is on the move. This allows the user to quickly find the information they need by analyzing geographical location information and providing search results based on the location information.

[0066] The search engine providing unit synchronizes information between devices and can provide consistent search results from any device. For example, the search engine providing unit synchronizes information between multiple devices of a user, and the generation AI provides consistent search results. For example, information searched on a smartphone is displayed in the same way on a computer. The search engine providing unit can also synchronize information between devices using cloud storage. For example, data stored on the cloud can be synchronized in real time, making it accessible from any device. The search engine providing unit can also share data between devices and unify search results. For example, a user's search history and click history can be shared between devices to provide consistent search results. This allows information to be synchronized between devices and consistent search results can be provided, enabling users to quickly find the information they need from any device.

[0067] The search engine providing unit can use the emotion estimation function to provide search results according to the user's emotional state and prioritize displaying information that elicits positive emotions. The search engine providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time. For example, it prioritizes displaying information that elicits positive emotions. The search engine providing unit can also customize search results based on the user's emotional state. For example, if the user is feeling stressed, it prioritizes displaying information that helps the user relax. The search engine providing unit can also use the emotion estimation function to monitor the user's emotional state in real time and instantly reflect this in the search results. For example, if the user is feeling positive emotions, it instantly displays information that further elicits those emotions. In this way, by providing search results according to the emotional state and prioritize displaying information that elicits positive emotions, the user can quickly find the information they need.

[0068] The analysis unit can analyze the user's past behavioral data in chronological order, identify behavioral patterns, and provide related information. For example, the analysis unit can analyze the user's past behavioral data in chronological order and identify behavioral patterns. For example, related information is provided based on activities performed on specific days of the week. The analysis unit can also analyze the user's behavioral data and identify frequently performed activities. For example, related information is displayed based on activities performed during specific time periods. The analysis unit can also analyze the user's behavioral data in real time, instantly identify behavioral patterns, and provide related information. For example, if the user is in a specific location, information related to that location is instantly displayed. In this way, by analyzing the user's past behavioral data in chronological order, identifying behavioral patterns, and providing related information, the user can quickly find the information they need.

[0069] The analysis unit can analyze the user's learning history and suggest the next content to learn based on the user's learning progress and level of understanding. The analysis unit, for example, analyzes the user's learning history to grasp the user's learning progress and level of understanding. For example, it suggests the next content to learn based on the content previously learned and test results. The analysis unit can also analyze the user's learning history and evaluate the user's learning progress. For example, it suggests the next content to learn based on the study time and the level of understanding of the learning content. The analysis unit can also analyze the user's learning history in real time, instantly evaluate the user's learning progress and level of understanding, and suggest the next content to learn. For example, if the user's level of understanding is determined to be low during study, it can instantly suggest supplementary learning content. In this way, by analyzing the learning history and suggesting the next content to learn based on the user's learning progress and level of understanding, the user can efficiently progress through their studies.

[0070] The analysis unit can use the emotion estimation function to analyze the user's behavior and learning history during periods when the user had a specific emotion, and provide information based on the emotion. The analysis unit, for example, uses the emotion estimation function to analyze the user's behavior and learning history during periods when the user had a specific emotion. For example, learning content from periods when positive emotions were strong is preferentially displayed. The analysis unit can also analyze the user's emotional state and provide information based on the emotion. For example, information for reducing stress is displayed based on the user's behavior during periods when negative emotions were strong. The analysis unit can also use the emotion estimation function to analyze the user's behavior and learning history during periods when the user had a specific emotion in real time, and provide information based on the emotion instantly. For example, behavior from periods when the user had positive emotions is instantly displayed. This allows the user to quickly find the information they need by providing information based on their emotions.

[0071] The analysis unit can analyze the user's past purchasing history and provide related information based on the purchasing pattern. The analysis unit, for example, analyzes the user's past purchasing history and identifies purchasing patterns. For example, it provides information related to products and services that are purchased regularly. The analysis unit can also analyze the user's purchasing history and identify products that are frequently purchased. For example, it displays information related to products of a specific brand or category. The analysis unit can also analyze the user's purchasing history in real time and instantly identify purchasing patterns to provide related information. For example, if the user purchases a specific product, information related to that product is instantly displayed. In this way, by analyzing the purchasing history and providing related information based on the purchasing pattern, the user can quickly find the information they need.

[0072] The analysis unit can analyze the user's past travel history and provide information related to travel destinations. The analysis unit, for example, analyzes the user's past travel history and provides information related to travel destinations. For example, information related to tourist spots and accommodations visited in the past is displayed. The analysis unit can also analyze the user's travel history and identify frequently visited travel destinations. For example, information related to a specific area is displayed. The analysis unit can also analyze the user's travel history in real time and instantly provide information related to travel destinations. For example, if the user is currently traveling, information related to that travel destination is instantly displayed. In this way, by analyzing the travel history and providing information related to the travel destination, the user can quickly find the information they need.

[0073] The analysis unit can use the emotion estimation function to analyze the user's behavior and learning history during periods when the user had a specific emotion, and provide information based on the emotion. For example, the analysis unit can use the emotion estimation function to analyze the user's behavior and learning history during periods when the user had a specific emotion. For example, the analysis unit can prioritize displaying behavior during periods when positive emotions were strong. The analysis unit can also analyze the user's emotional state and provide information based on the emotion. For example, the analysis unit can display information for reducing stress based on behavior during periods when negative emotions were strong. The analysis unit can also use the emotion estimation function to analyze the user's behavior and learning history during periods when the user had a specific emotion in real time, and provide information based on the emotion instantly. For example, the analysis unit can instantly display behavior during periods when the user had positive emotions. This allows the user to quickly find the information they need by providing information based on their emotions.

[0074] The file presentation unit can analyze the update history of the user's files and notes and prioritize presenting the most recent information. The file presentation unit, for example, analyzes the update history of the user's files and notes and prioritize presenting the most recent information. For example, recently updated files and notes are displayed at the top of search results. The file presentation unit can also analyze the update history of the user's files and notes and prioritize displaying important updates. For example, files and notes to which important changes have been made are displayed at the top. The file presentation unit can also analyze the update history of the user's files and notes in real time and instantly present the most recent information. For example, immediately after a user updates a file, the file is displayed at the top of search results. In this way, by analyzing the update history of files and notes and prioritize presenting the most recent information, the user can quickly find the information they need.

[0075] The file presentation unit can use the emotion estimation function to prioritize presenting files and notes recorded by the user with a particular emotion. For example, the file presentation unit can use the emotion estimation function to prioritize presenting files and notes recorded by the user with a particular emotion. For example, it can prioritize displaying notes with a strong positive emotion. The file presentation unit can also use the emotion estimation function to analyze files and notes recorded by the user with a particular emotion and prioritize presenting information based on the emotion. For example, it can prioritize displaying notes with a strong negative emotion. The file presentation unit can also use the emotion estimation function to analyze files and notes recorded by the user with a particular emotion in real time and prioritize presenting them immediately. For example, it can instantly display notes recorded by the user with emotion. In this way, by prioritizing the presentation of files and notes based on emotion, the user can quickly find the information they need.

[0076] The file presentation unit can automatically tag a user's files and notes and present related information based on the tags. For example, the file presentation unit uses a generation AI to automatically tag a user's files and notes and present related information based on the tags. For example, it can display files and notes related to specific keywords. The file presentation unit can also analyze a user's files and notes and automatically generate appropriate tags. For example, it can generate tags based on the content of a document and display related information. The file presentation unit can also tag a user's files and notes in real time and instantly present related information. For example, immediately after a new note is created, information related to that note can be instantly displayed. This allows users to quickly find the information they need by automatically tagging files and notes and presenting related information based on tags.

[0077] The file presentation unit can automatically classify a user's files and notes and present them organized by category. For example, the file presentation unit uses a generation AI to automatically classify a user's files and notes and present them organized by category. For example, it can classify files into categories such as work-related and personal-related. The file presentation unit can also analyze a user's files and notes and automatically classify them into appropriate categories. For example, it can generate categories based on the content of a document and display related information. The file presentation unit can also classify a user's files and notes in real time and instantly organize and present them organized by category. For example, immediately after a new note is created, it can be classified into the appropriate category and displayed immediately. This allows users to quickly find the information they need by automatically classifying files and notes and presenting them organized by category.

[0078] The file presentation unit can use the emotion estimation function to prioritize presenting files and notes recorded by the user with a particular emotion. For example, the file presentation unit can use the emotion estimation function to prioritize presenting files and notes recorded by the user with a particular emotion. For example, it can prioritize displaying notes with a strong positive emotion. The file presentation unit can also use the emotion estimation function to analyze files and notes recorded by the user with a particular emotion and prioritize presenting information based on the emotion. For example, it can prioritize displaying notes with a strong negative emotion. The file presentation unit can also use the emotion estimation function to analyze files and notes recorded by the user with a particular emotion in real time and prioritize presenting them immediately. For example, it can instantly display notes recorded by the user with emotion. In this way, by prioritizing the presentation of files and notes based on emotion, the user can quickly find the information they need.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The Memory Finder system can also be equipped with a health management unit that collects the user's biometric information and provides information based on their health condition. For example, it can monitor the user's heart rate and blood pressure and provide relaxation advice if it determines that the user is under high stress. It can also analyze sleep data and provide advice on how to improve sleep if the quality is poor. It can also suggest appropriate exercise plans based on exercise data. This makes it possible to provide information based on the user's health condition and provide more personalized support.

[0081] The Memory Finder system can also include a hobby analysis unit that provides information based on the user's hobbies and interests. For example, it can collect information related to hobbies the user has previously searched for and suggest new hobbies. It can also suggest related events and products based on events the user has attended and items they have purchased. It can also provide related news and articles based on the user's interests. This makes it possible to provide information based on the user's hobbies and interests, supporting a more fulfilling lifestyle.

[0082] The Memory Finder system can also include an entertainment provider that recommends music and videos based on the user's emotional state. For example, if the user is feeling stressed, relaxing music can be recommended. If the user is feeling positive, videos that will enhance those emotions can be provided. Furthermore, it is possible to recommend appropriate entertainment content in real time based on the user's emotional state. This allows for a richer experience by providing entertainment content that matches the user's emotional state.

[0083] The Memory Finder system can also be equipped with a learning support unit that provides information based on the user's learning style. For example, if the user prefers visual learning, visual content can be provided first. If the user prefers auditory learning, audio content can be provided. Furthermore, it can also suggest what the user should learn next based on their learning progress. This makes it possible to provide information tailored to the user's learning style, supporting more effective learning.

[0084] The Memory Finder system can also be equipped with a function to support communication based on the user's emotional state. For example, if the user is feeling negative, it can provide an encouraging message. If the user is feeling positive, it can suggest a social media post to share those feelings. It can also suggest appropriate communication methods based on the user's emotional state. This enables communication support that is tailored to the user's emotional state, and can provide support for building better relationships.

[0085] The Memory Finder system can also be equipped with a travel support unit that analyzes the user's past travel history and suggests the next travel destination. For example, it can suggest the next travel destination based on the places and accommodations the user has visited in the past. It can also provide related tourist spots and activities based on the user's travel history. It can also suggest optimal travel plans based on the user's travel style. This makes it possible to provide information based on the user's travel history, supporting a more fulfilling travel experience.

[0086] The Memory Finder system can also be equipped with a function to support health management based on the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing exercise or meditation. If the user is feeling positive, it can also suggest healthy eating and lifestyle changes to maintain those feelings. It can also suggest appropriate health management methods in real time based on the user's emotional state. This enables health management support that corresponds to the user's emotional state, supporting a healthier lifestyle.

[0087] The Memory Finder system can also be equipped with a purchasing support unit that analyzes a user's purchasing history and suggests the next product to purchase. For example, it can suggest related products based on products the user has previously purchased. It can also predict the next product to be purchased based on the user's purchasing patterns. It can also prioritize the display of products from specific brands or categories based on the user's purchasing history. This makes it possible to provide information based on the user's purchasing history and support more efficient purchasing.

[0088] The Memory Finder system can also be equipped with a learning support function that customizes learning content based on the user's emotional state. For example, if the user is feeling stressed, it can provide learning content that helps them relax. Or, if the user is feeling positive, it can provide learning content that further enhances those emotions. It can also suggest appropriate learning methods in real time based on the user's emotional state. This enables learning support that is tailored to the user's emotional state, supporting more effective learning.

[0089] The Memory Finder system can also include a prediction unit that analyzes a user's past behavioral data and predicts future behavior based on their behavioral patterns. For example, it can suggest the next action a user should take based on past actions taken on a specific day or time of day. It can also predict future behavior based on the user's behavioral patterns and provide related information. Furthermore, it can analyze the user's behavioral data in real time and instantly predict future behavior. This makes it possible to provide information based on the user's behavioral patterns, supporting more efficient behavior.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The information collection unit collects information stored on the user's computer, cloud, smartphone, etc. For example, the information collection unit scans files on the user's computer, obtains data stored in cloud storage, and collects notes and images on the smartphone. The information collection unit can also periodically scan these devices to collect new information. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit can use text analysis technology to analyze the content of documents, image analysis technology to recognize text and objects in images, and voice analysis technology to convert the content of voice memos into text. Step 3: The search engine provider provides the user with an optimized search engine based on the information analyzed by the analysis unit. For example, the search engine provider may analyze the user's past search history and click history to identify frequently searched keywords and phrases. The search engine provider may also use machine learning algorithms to optimize search results based on the user's search behavior. Step 4: The file presentation unit quickly presents necessary information, files, and notes using the search engine provided by the search engine provision unit. For example, the file presentation unit may display files and notes related to keywords searched by the user, and may also preferentially display related information based on the user's past behavior and learning history.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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. An information collection unit that collects information stored on the user's computer, cloud, smartphone, etc.; an analysis unit that analyzes the information collected by the information collection unit; a search engine providing unit that provides a search engine optimized for the user based on the information analyzed by the analysis unit; a file presentation unit that promptly presents necessary information, files, and memos using the search engine provided by the search engine provision unit. A system characterized by:

2. The information collecting unit Voice memos and video messages will be included in the analysis, converted into text using voice recognition technology, and made searchable.

2. The system of claim 1.

3. The information collecting unit Collect data from wearable devices and present relevant information based on health status and activity history 2. The system of claim 1.

4. The search engine providing unit Analyzes search and click histories to provide search algorithms optimized for individual users 2. The system of claim 1.

5. The analysis unit Analyzing the user's past behavioral data in chronological order, identifying behavioral patterns, and providing related information 2. The system of claim 1.

6. The file presenting unit The contents of the files and notes of the user are analyzed, and other related files and notes are presented as links.

2. The system of claim 1.

7. The information collecting unit The information recorded by the user with a particular emotion is preferentially collected, and search results based on the emotion are provided.

2. The system of claim 1.

8. The search engine providing unit Providing search results according to the emotional state of the user, and preferentially displaying information for reducing stress 2. The system of claim 1.

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