system

Abstract

The system according to this embodiment aims to efficiently manage, appropriately classify, and store user questions and inquiries. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a storage unit, a search unit, and a sharing unit. The reception unit receives questions or inquiries from users. The analysis unit analyzes the questions or inquiries received by the reception unit and classifies them into appropriate categories. The storage unit stores the data analyzed by the analysis unit in a database. The search unit searches and retrieves the data stored by the storage unit. The sharing unit provides functions for multiple platforms to share data.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the doubts and questions of users have not been sufficiently managed efficiently and properly classified and stored, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently manage the doubts and questions of users and properly classify and store them. …]]END]]

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a storage unit, a search unit, and a sharing unit. The reception unit receives questions or inquiries from users. The analysis unit analyzes the questions or inquiries received by the reception unit and classifies them into appropriate categories. The storage unit stores the data analyzed by the analysis unit in a database. The search unit searches and retrieves the data stored by the storage unit. The sharing unit provides functions for multiple platforms to share data. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage, appropriately classify, and store user questions and inquiries. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] [[ID=!13]] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The question-solving system according to an embodiment of the present invention is a system that automatically categorizes questions and inquiries that arise in daily life, stores them in a database, and provides a platform that allows users to retrieve information at any time. In this question-solving system, the user inputs a question or inquiry, the system analyzes the question or inquiry, and classifies it into an appropriate category. The classified data is stored in a database, and the user can search and retrieve that information at any time. Furthermore, by linking the platform, a huge data set can be formed, making it possible to share more information. For example, the user inputs questions such as "Why is the sky blue?" or "Why do apples fall?". This information is input into the system. Next, the system analyzes the input question or inquiry. The system uses natural language processing technology to understand the content of the question or inquiry and classify it into an appropriate category. For example, the question "Why is the sky blue?" is classified into the "Natural Phenomena" category, and the question "Why do apples fall?" is classified into the "Physics" category. The classified data is stored in a database. The user can access the database and search and retrieve information at any time. For example, by searching the "Natural Phenomena" category, the answer to the question "Why is the sky blue?" can be found. Furthermore, by linking platforms, a massive dataset can be formed. When multiple users use the same platform, more questions and inquiries are accumulated in the database, facilitating information sharing. This allows users to access more information and quickly find answers to their questions. This mechanism enables users to easily resolve questions that arise in their daily lives. For example, if a child wonders, "Why is the sky blue?", a parent can use the platform to quickly find the answer. Similarly, if a student wonders, "Why do apples fall?", a teacher can use the platform to provide the answer. This allows users to quickly obtain answers to everyday questions and inquiries, leading to improved knowledge. In this way, the question-solving system can efficiently analyze, store, search, and share users' questions and inquiries.

[0029] The question-solving system according to this embodiment comprises a reception unit, an analysis unit, a storage unit, a search unit, and a sharing unit. The reception unit receives questions or inquiries from users. These questions or inquiries from users include, for example, technical questions, general questions, etc., but are not limited to such examples. The reception unit provides, for example, an interface for users to input questions or inquiries. The analysis unit analyzes the questions or inquiries received by the reception unit and classifies them into appropriate categories. The analysis unit understands the content of the questions or inquiries using, for example, natural language processing techniques and classifies them into appropriate categories. Natural language processing techniques include, for example, morphological analysis, grammatical analysis, semantic analysis, etc. The storage unit stores the data analyzed by the analysis unit in a database. The database includes, for example, a relational database, a NoSQL database, etc. The search unit searches and retrieves the data stored by the storage unit. The search unit provides, for example, an interface for users to search and retrieve information from the database. The information to be searched includes, for example, text data, image data, etc. The sharing unit provides functions for multiple platforms to share data. The shared component, for example, collects and integrates data from each platform. These platforms include, for example, a web platform and a mobile platform. This enables the question-solving system according to the embodiment to efficiently analyze, store, search, and share user questions and inquiries.

[0030] The analysis unit can understand the content of questions and inquiries using natural language processing techniques and classify them into appropriate categories. Natural language processing techniques include, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can use morphological analysis to break down the words of a question and inquiry and analyze the meaning of each word. The analysis unit can also use grammatical analysis to analyze the grammatical structure of a question and inquiry and understand the meaning of the sentence. Furthermore, the analysis unit can use semantic analysis to understand the context of a question and inquiry and classify it into an appropriate category. For example, the analysis unit uses morphological analysis to analyze the question "Why is the sky blue?" and extracts the words "sky" and "blue". Next, it uses grammatical analysis to analyze the grammatical structure of the question "Why is the sky blue?" and understand the meaning of the sentence. Finally, it uses semantic analysis to understand the context of the question "Why is the sky blue?" and classify it into the category of "natural phenomena". In this way, the accuracy of question and inquiry analysis is improved by using natural language processing techniques.

[0031] The storage unit can save the analyzed data to a database. Databases include, for example, relational databases and NoSQL databases. The storage unit can, for example, save the analyzed data in a table format using a relational database. It can also save the analyzed data in a document format using a NoSQL database. For example, the storage unit can save the question "Why is the sky blue?" in a table using a relational database. Then, the storage unit can save the same question "Why is the sky blue?" as a document using a NoSQL database. This allows for efficient storage of the analyzed data.

[0032] The search unit allows users to search and retrieve information from the database. The information retrieved may include, for example, text data and image data. The search unit provides, for example, an interface for searching text data. It can also provide an interface for searching image data. For example, the search unit provides a keyword search function for searching text data. Next, the search unit provides image recognition technology for searching image data. This allows users to quickly search and retrieve the information they need.

[0033] The shared component can collect and integrate data from various platforms. These platforms include, for example, web platforms and mobile platforms. For instance, the shared component can collect and integrate data from a web platform. It can also collect and integrate data from a mobile platform. For example, the shared component could collect the question "Why is the sky blue?" from a web platform and integrate it into a database. Then, it could collect the same question "Why is the sky blue?" from a mobile platform and integrate it into the database. This process of collecting and integrating data from multiple platforms facilitates information sharing.

[0034] The reception desk can analyze a user's past input history and suggest appropriate input methods. For example, it can automatically display questions and inquiries that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions and inquiries that the user will use at specific times of day based on their past input history. For example, the reception desk can automatically display questions and inquiries that the user has frequently entered in the past as suggestions. Next, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. Finally, the reception desk predicts and suggests questions and inquiries that the user will use at specific times of day based on their past input history. This streamlines the input process by suggesting the optimal input method based on past input history.

[0035] The input system can suggest input options based on the user's current areas of interest when they enter questions or inquiries. For example, it can suggest related questions or inquiries based on keywords the user has recently searched for. It can also suggest related questions or inquiries based on articles or news the user has recently read. Furthermore, it can suggest related questions or inquiries based on events or activities the user has recently participated in. For example, the input system can suggest related questions or inquiries based on keywords the user has recently searched for. Next, it can suggest related questions or inquiries based on articles or news the user has recently read. Finally, it can suggest related questions or inquiries based on events or activities the user has recently participated in. This streamlines the input process by suggesting input options based on the user's areas of interest.

[0036] The input system can suggest highly relevant input options when users enter questions or inquiries, taking into account their geographical location. For example, the input system can suggest questions related to the user's current location. It can also suggest questions related to places the user has visited in the past. Furthermore, it can suggest questions related to travel destinations the user is planning. For example, the input system can suggest questions related to the user's current location. Next, it can suggest questions related to places the user has visited in the past. Finally, it can suggest questions related to travel destinations the user is planning. This streamlines the input process by suggesting input options that take geographical location into account. Geographical location information is obtained using technologies such as GPS data and IP addresses. GPS data obtains the user's current location with high accuracy. IP addresses estimate the geographical location based on the user's internet connection location. This allows the system to accurately obtain the user's geographical location information and suggest appropriate input options.

[0037] The input system can analyze a user's social media activity when they enter questions or inquiries and suggest relevant input options. For example, it can suggest questions related to a user's recent posts. It can also suggest questions related to posts from accounts the user follows. Furthermore, it can suggest questions related to the activities of groups and communities the user participates in. For example, it can suggest questions related to a user's recent posts. Next, it can suggest questions related to posts from accounts the user follows. Finally, it can suggest questions related to the activities of groups and communities the user participates in. This streamlines the input process by suggesting input options based on social media activity. Social media activity analysis is performed using techniques such as post content analysis and follower analysis. Post content analysis analyzes text and images posted by the user and extracts relevant questions. Follower analysis analyzes posts from accounts the user follows and extracts relevant questions. This allows for accurate analysis of users' social media activity and the presentation of appropriate input suggestions.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the questions and inquiries during the analysis. For example, the analysis unit will perform a detailed analysis for questions and inquiries of high importance. The analysis unit can also perform a concise analysis for questions and inquiries of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail for questions and inquiries of medium importance. For example, the analysis unit will perform a detailed analysis for questions and inquiries of high importance. Next, the analysis unit will perform a concise analysis for questions and inquiries of low importance. Finally, the analysis unit will perform an analysis with an appropriate level of detail for questions and inquiries of medium importance. This improves the accuracy of the analysis by performing the analysis based on the importance of the questions and inquiries. The evaluation of importance is performed using criteria such as user ratings and frequency. User ratings determine importance based on the ratings that users have given to questions and inquiries. Frequency determines importance based on how often the questions and inquiries have been submitted. This allows for accurate evaluation of the importance of questions and inquiries and enables appropriate analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the category of the question or inquiry during analysis. For example, the analysis unit applies a scientific analysis algorithm to questions or inquiries about natural phenomena. It can also apply a physical analysis algorithm to questions or inquiries about physics. Furthermore, it can apply a sociological analysis algorithm to questions or inquiries about social phenomena. For example, the analysis unit applies a scientific analysis algorithm to questions or inquiries about natural phenomena. Next, it applies a physical analysis algorithm to questions or inquiries about physics. Finally, it applies a sociological analysis algorithm to questions or inquiries about social phenomena. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category. Category classification is performed using criteria such as technology categories and business categories. Technology categories include questions or inquiries about technology. Business categories include questions or inquiries about business. This allows for accurate classification of questions and inquiries and the application of appropriate analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on when questions and inquiries were submitted. For example, the analysis unit can prioritize recently submitted questions and inquiries. It can also postpone older questions and inquiries. Furthermore, it can moderately analyze questions and inquiries that have been submitted for a moderate period of time. For example, the analysis unit prioritizes recently submitted questions and inquiries. Next, the analysis unit postpones older questions and inquiries. Finally, the analysis unit moderately analyzes questions and inquiries that have been submitted for a moderate period of time. This improves the efficiency of analysis by performing analysis based on submission date. The submission date can be obtained using techniques such as timestamps and submission dates. The timestamp records the exact date and time when a question or inquiry was submitted. The submission date records the date when a question or inquiry was submitted. This allows for accurate acquisition of the submission date of questions and inquiries and the determination of appropriate analysis priorities.

[0041] The analysis unit can adjust the order of analysis based on the relevance of questions and inquiries during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant questions and inquiries. It can also postpone the analysis of less relevant questions and inquiries. Furthermore, the analysis unit can appropriately analyze questions and inquiries of moderate relevance. For example, the analysis unit prioritizes the analysis of highly relevant questions and inquiries. Next, the analysis unit postpones the analysis of less relevant questions and inquiries. Finally, the analysis unit appropriately analyzes questions and inquiries of moderate relevance. This improves the efficiency of the analysis by performing analysis based on relevance. Relevance is evaluated using criteria such as common keywords and related topics. Common keywords evaluate relevance based on keywords common to the questions and inquiries. Related topics evaluate relevance based on topics related to the questions and inquiries. This allows for accurate evaluation of the relevance of questions and inquiries and adjustment of the appropriate order of analysis.

[0042] The storage unit can select an appropriate storage format by referring to past data when storing data. For example, the storage unit can select the optimal storage format based on the format of data previously stored. It can also select the optimal storage format based on the frequency of use of past data. Furthermore, it can select the optimal storage format based on the relevance of past data. For example, the storage unit selects the optimal storage format based on the format of data previously stored. Next, the storage unit selects the optimal storage format based on the frequency of use of past data. Finally, the storage unit selects the optimal storage format based on the relevance of past data. This streamlines data management by selecting the optimal storage format based on past data. Referencing past data is performed using criteria such as past storage format and data type. Past storage format records how data was stored in the past. Data type records the type of data stored in the past. This allows for accurate referencing of past data and selection of an appropriate storage format.

[0043] The storage unit can apply different storage algorithms depending on the data category when storing data. For example, the storage unit can apply a scientific storage algorithm to data related to natural phenomena. It can also apply a physical storage algorithm to data related to physics. Furthermore, it can apply a sociological storage algorithm to data related to social phenomena. For example, the storage unit applies a scientific storage algorithm to data related to natural phenomena. Next, it applies a physical storage algorithm to data related to physics. Finally, it applies a sociological storage algorithm to data related to social phenomena. This makes data management more efficient by applying storage algorithms according to the category. Category classification is done using criteria such as technical data and business data. Technical data includes data related to technology. Business data includes data related to business. This allows for accurate classification of data categories and the application of appropriate storage algorithms.

[0044] The storage unit can determine the storage priority based on the data submission date when storing data. For example, the storage unit can prioritize storing recently submitted data. It can also postpone storing older data. Furthermore, the storage unit can moderately store data with a moderate submission date. For example, the storage unit prioritizes storing recently submitted data. Next, the storage unit postpones storing older data. Finally, the storage unit moderately stores data with a moderate submission date. This streamlines data management by storing data based on submission date. The submission date can be obtained using techniques such as timestamps and submission dates. The timestamp records the exact date and time the data was submitted. The submission date records the date the data was submitted. This allows for accurate acquisition of the data submission date and enables the determination of appropriate storage priorities.

[0045] The storage unit can adjust the order in which data is stored based on its relevance. For example, the storage unit can prioritize storing highly relevant data. It can also postpone storing less relevant data. Furthermore, it can store moderately relevant data. For example, the storage unit prioritizes storing highly relevant data. Next, it postpones storing less relevant data. Finally, it stores moderately relevant data. This streamlines data management by performing storage based on relevance. Relevance is evaluated using criteria such as common keywords and related topics. Common keywords evaluate relevance based on keywords common to the data. Related topics evaluate relevance based on topics related to the data. This allows for accurate evaluation of data relevance and adjustment of the appropriate storage order.

[0046] The search engine can apply an appropriate search algorithm by referring to past search history during a search. For example, the search engine can apply the optimal search algorithm based on keywords the user has searched for in the past. It can also prioritize displaying highly relevant search results based on the user's past search history. Furthermore, the search engine can analyze the user's past search patterns and apply the optimal search algorithm. For example, the search engine applies the optimal search algorithm based on keywords the user has searched for in the past. Next, the search engine prioritizes displaying highly relevant search results based on the user's past search history. Finally, the search engine analyzes the user's past search patterns and applies the optimal search algorithm. This improves search accuracy by applying the optimal search algorithm based on past search history. Past search history is referenced using criteria such as past search keywords and search results. Past search keywords record keywords the user has searched for in the past. Search results record search results the user has obtained in the past. This allows for accurate reference to past search history and the application of an appropriate search algorithm.

[0047] The search unit can apply different search algorithms depending on the data category during a search. For example, the search unit can apply a scientific search algorithm to data related to natural phenomena. It can also apply a physical search algorithm to data related to physics. Furthermore, it can apply a sociological search algorithm to data related to social phenomena. For example, the search unit applies a scientific search algorithm to data related to natural phenomena. Next, it applies a physical search algorithm to data related to physics. Finally, it applies a sociological search algorithm to data related to social phenomena. This improves search accuracy by applying a search algorithm appropriate to the category. Category classification is performed using criteria such as technical data and business data. Technical data includes data related to technology. Business data includes data related to business. This allows for accurate classification of data categories and the application of appropriate search algorithms.

[0048] The search function can prioritize search results based on when the data was submitted. For example, it might prioritize recently submitted data. It can also prioritize older data. Furthermore, it can appropriately display data with a moderate submission time. For example, it might prioritize recently submitted data, then prioritize older data, and finally display data with a moderate submission time. This makes it easier to understand search results when searching based on submission time. The submission time is obtained using techniques such as timestamps and submission dates. A timestamp records the exact date and time the data was submitted. The submission date records the date the data was submitted. This allows for accurate retrieval of data submission time and appropriate prioritization of search results.

[0049] The search engine can adjust the order of search results based on the relevance of the data during a search. For example, the search engine can prioritize displaying highly relevant data. It can also postpone displaying less relevant data. Furthermore, it can display moderately relevant data appropriately. For example, the search engine prioritizes displaying highly relevant data. Next, it postpones displaying less relevant data. Finally, it displays moderately relevant data appropriately. This makes it easier to understand the search results by performing a relevance-based search. Relevance is evaluated using criteria such as common keywords and related topics. Common keywords evaluate relevance based on keywords common to the data. Related topics evaluate relevance based on topics related to the data. This allows for accurate evaluation of data relevance and adjustment of the order of search results appropriately.

[0050] The sharing system can select an appropriate sharing method by referring to past sharing history when sharing data. For example, the sharing system can select the optimal sharing method based on the format of data shared in the past. It can also select frequently used sharing methods from past sharing history. Furthermore, the sharing system can analyze past sharing history to select the most efficient sharing method. For example, the sharing system selects the optimal sharing method based on the format of data shared in the past. Next, the sharing system selects frequently used sharing methods from past sharing history. Finally, the sharing system analyzes past sharing history to select the most efficient sharing method. This streamlines data sharing by selecting the optimal sharing method based on past sharing history. Referencing past sharing history is done using criteria such as past shared data and sharing methods. Past shared data records what kind of data was shared in the past. Sharing methods record how data was shared in the past. This allows for accurate referencing of past sharing history and selection of an appropriate sharing method.

[0051] The sharing function can apply different sharing algorithms depending on the data category when sharing data. For example, it can apply a scientific sharing algorithm to data on natural phenomena, a physical sharing algorithm to data on physics, and a sociological sharing algorithm to data on social phenomena. This makes data sharing more efficient by applying a sharing algorithm appropriate to the category. Category classification is done using criteria such as technical data and business data. Technical data includes data on technology, and business data includes data on business. This allows for accurate classification of data categories and the application of appropriate sharing algorithms.

[0052] The sharing system can prioritize data sharing based on when the data was submitted. For example, it might prioritize sharing recently submitted data. It can also postpone sharing older data. Furthermore, it can share data of moderate age to a reasonable extent. For example, it might prioritize sharing recently submitted data, then postpone sharing older data, and finally share data of moderate age to a reasonable extent. This makes data sharing more efficient by sharing based on submission date. The submission date can be obtained using techniques such as timestamps and submission dates. The timestamp records the exact date and time the data was submitted. The submission date records the date the data was submitted. This allows for accurate data submission and the determination of appropriate sharing priorities.

[0053] The sharing system can adjust the order of data sharing based on its relevance. For example, it can prioritize sharing highly relevant data. It can also postpone sharing less relevant data. Furthermore, it can share moderately relevant data as well. For example, it can prioritize sharing highly relevant data, then postpone sharing less relevant data, and finally share moderately relevant data as well. This makes data sharing more efficient by sharing based on relevance. Relevance is evaluated using criteria such as common keywords and related topics. Common keywords evaluate relevance based on keywords common to the data. Related topics evaluate relevance based on topics related to the data. This allows for accurate evaluation of data relevance and adjustment of the appropriate sharing order.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can automatically suggest relevant past questions and answers based on the user's input. For example, if a user enters "Why is the sky blue?", the reception desk will search for similar questions in the past and present relevant answers. The reception desk can also display suggested related questions based on parts of the question entered by the user. Furthermore, the reception desk can suggest relevant topics and articles based on the category of the question entered by the user. This allows users to quickly obtain information related to their input, improving the efficiency of question resolution.

[0056] The analysis unit can use a combination of multiple analysis algorithms for user input. For example, morphological analysis and grammatical analysis can be combined to perform more accurate analysis. Furthermore, semantic analysis and contextual analysis can be combined to gain a deeper understanding of the intent behind the question. In addition, machine learning algorithms can be used to improve analysis accuracy based on past analysis results. This allows the analysis unit to analyze user input more accurately and classify it into appropriate categories.

[0057] The storage unit can select a storage format based on the importance of the data when saving it. For example, highly important data can be stored in a highly redundant format to prevent data loss. Less important data can be stored in a compressed format to improve storage efficiency. Furthermore, data of moderate importance can be stored in a balanced format to achieve both efficiency and security. In this way, selecting a storage format according to the importance of the data improves the efficiency of data management.

[0058] The search function can prioritize displaying relevant search results based on the user's search history. For example, it can prioritize displaying information related to keywords the user has searched for in the past. It can also suggest relevant information based on the content of pages the user has previously viewed. Furthermore, it can analyze the user's search patterns to provide optimal search results. This allows the system to leverage the user's search history to provide more relevant search results.

[0059] The shared section allows for adjusting the scope of data sharing based on user access permissions. For example, only publicly available information can be shared with general users, while data containing confidential information can be shared with administrators. Data security can also be ensured by granting access only to specific groups. Furthermore, the level of detail of shared data can be adjusted according to the user's role. This allows for both information security and efficient data sharing by aligning data sharing with user access permissions.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives questions or inquiries from users. These questions or inquiries may include, but are not limited to, technical questions or general inquiries. The reception desk may, for example, provide an interface for users to input their questions or inquiries. Step 2: The analysis unit analyzes the questions or inquiries received by the reception unit and classifies them into appropriate categories. The analysis unit understands the content of the questions or inquiries using, for example, natural language processing techniques and classifies them into appropriate categories. Natural language processing techniques include, for example, morphological analysis, grammatical analysis, and semantic analysis. Step 3: The storage unit stores the data analyzed by the analysis unit in a database. The database may include, for example, a relational database or a NoSQL database. Step 4: The search unit searches for and retrieves data stored by the storage unit. The search unit provides, for example, an interface for users to search for and retrieve information from the database. The information to be searched may include, for example, text data, image data, etc. Step 5: The sharing section provides functionality for multiple platforms to share data. For example, the sharing section collects and integrates data from each platform. Platforms include, for example, web platforms and mobile platforms.

[0062] (Example of form 2) The question-solving system according to an embodiment of the present invention is a system that automatically categorizes questions and inquiries that arise in daily life, stores them in a database, and provides a platform that allows users to retrieve information at any time. In this question-solving system, the user inputs a question or inquiry, the system analyzes the question or inquiry, and classifies it into an appropriate category. The classified data is stored in a database, and the user can search and retrieve that information at any time. Furthermore, by linking the platform, a huge data set can be formed, making it possible to share more information. For example, the user inputs questions such as "Why is the sky blue?" or "Why do apples fall?". This information is input into the system. Next, the system analyzes the input question or inquiry. The system uses natural language processing technology to understand the content of the question or inquiry and classify it into an appropriate category. For example, the question "Why is the sky blue?" is classified into the "Natural Phenomena" category, and the question "Why do apples fall?" is classified into the "Physics" category. The classified data is stored in a database. The user can access the database and search and retrieve information at any time. For example, by searching the "Natural Phenomena" category, the answer to the question "Why is the sky blue?" can be found. Furthermore, by linking platforms, a massive dataset can be formed. When multiple users use the same platform, more questions and inquiries are accumulated in the database, facilitating information sharing. This allows users to access more information and quickly find answers to their questions. This mechanism enables users to easily resolve questions that arise in their daily lives. For example, if a child wonders, "Why is the sky blue?", a parent can use the platform to quickly find the answer. Similarly, if a student wonders, "Why do apples fall?", a teacher can use the platform to provide the answer. This allows users to quickly obtain answers to everyday questions and inquiries, leading to improved knowledge. In this way, the question-solving system can efficiently analyze, store, search, and share users' questions and inquiries.

[0063] The question-solving system according to this embodiment comprises a reception unit, an analysis unit, a storage unit, a search unit, and a sharing unit. The reception unit receives questions or inquiries from users. These questions or inquiries from users include, for example, technical questions, general questions, etc., but are not limited to such examples. The reception unit provides, for example, an interface for users to input questions or inquiries. The analysis unit analyzes the questions or inquiries received by the reception unit and classifies them into appropriate categories. The analysis unit understands the content of the questions or inquiries using, for example, natural language processing techniques and classifies them into appropriate categories. Natural language processing techniques include, for example, morphological analysis, grammatical analysis, semantic analysis, etc. The storage unit stores the data analyzed by the analysis unit in a database. The database includes, for example, a relational database, a NoSQL database, etc. The search unit searches and retrieves the data stored by the storage unit. The search unit provides, for example, an interface for users to search and retrieve information from the database. The information to be searched includes, for example, text data, image data, etc. The sharing unit provides functions for multiple platforms to share data. The shared component, for example, collects and integrates data from each platform. These platforms include, for example, a web platform and a mobile platform. This enables the question-solving system according to the embodiment to efficiently analyze, store, search, and share user questions and inquiries.

[0064] The analysis unit can understand the content of questions and inquiries using natural language processing techniques and classify them into appropriate categories. Natural language processing techniques include, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can use morphological analysis to break down the words of a question and inquiry and analyze the meaning of each word. The analysis unit can also use grammatical analysis to analyze the grammatical structure of a question and inquiry and understand the meaning of the sentence. Furthermore, the analysis unit can use semantic analysis to understand the context of a question and inquiry and classify it into an appropriate category. For example, the analysis unit uses morphological analysis to analyze the question "Why is the sky blue?" and extracts the words "sky" and "blue". Next, it uses grammatical analysis to analyze the grammatical structure of the question "Why is the sky blue?" and understand the meaning of the sentence. Finally, it uses semantic analysis to understand the context of the question "Why is the sky blue?" and classify it into the category of "natural phenomena". In this way, the accuracy of question and inquiry analysis is improved by using natural language processing techniques.

[0065] The storage unit can save the analyzed data to a database. Databases include, for example, relational databases and NoSQL databases. The storage unit can, for example, save the analyzed data in a table format using a relational database. It can also save the analyzed data in a document format using a NoSQL database. For example, the storage unit can save the question "Why is the sky blue?" in a table using a relational database. Then, the storage unit can save the same question "Why is the sky blue?" as a document using a NoSQL database. This allows for efficient storage of the analyzed data.

[0066] The search unit allows users to search and retrieve information from the database. The information retrieved may include, for example, text data and image data. The search unit provides, for example, an interface for searching text data. It can also provide an interface for searching image data. For example, the search unit provides a keyword search function for searching text data. Next, the search unit provides image recognition technology for searching image data. This allows users to quickly search and retrieve the information they need.

[0067] The shared component can collect and integrate data from various platforms. These platforms include, for example, web platforms and mobile platforms. For instance, the shared component can collect and integrate data from a web platform. It can also collect and integrate data from a mobile platform. For example, the shared component could collect the question "Why is the sky blue?" from a web platform and integrate it into a database. Then, it could collect the same question "Why is the sky blue?" from a mobile platform and integrate it into the database. This process of collecting and integrating data from multiple platforms facilitates information sharing.

[0068] The reception desk can estimate the user's emotions and customize the input interface for questions and inquiries according to the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of questions and inquiries. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. Next, if the user is relaxed, the reception desk provides detailed input options and suggests customizable input methods. Finally, if the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of questions and inquiries. This makes the input process easier by providing an input interface that responds to the user's emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition technology analyzes the user's facial expressions and estimates their emotions. Voice analysis technology analyzes the tone and speed of the user's voice and estimates their emotions. This allows for accurate estimation of user emotions and the provision of an appropriate input interface.

[0069] The reception desk can analyze a user's past input history and suggest appropriate input methods. For example, it can automatically display questions and inquiries that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions and inquiries that the user will use at specific times of day based on their past input history. For example, the reception desk can automatically display questions and inquiries that the user has frequently entered in the past as suggestions. Next, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. Finally, the reception desk predicts and suggests questions and inquiries that the user will use at specific times of day based on their past input history. This streamlines the input process by suggesting the optimal input method based on past input history.

[0070] The input system can suggest input options based on the user's current areas of interest when they enter questions or inquiries. For example, it can suggest related questions or inquiries based on keywords the user has recently searched for. It can also suggest related questions or inquiries based on articles or news the user has recently read. Furthermore, it can suggest related questions or inquiries based on events or activities the user has recently participated in. For example, the input system can suggest related questions or inquiries based on keywords the user has recently searched for. Next, it can suggest related questions or inquiries based on articles or news the user has recently read. Finally, it can suggest related questions or inquiries based on events or activities the user has recently participated in. This streamlines the input process by suggesting input options based on the user's areas of interest.

[0071] The reception desk can estimate the user's emotions and change the design of the input interface according to the estimated emotions. For example, if the user is tense, the reception desk can provide an interface with calming colors to reduce visual stress. Also, if the user is enjoying themselves, the reception desk can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception desk can provide a simple and highly visible interface to make the input process easier. For example, if the user is tense, the reception desk can provide an interface with calming colors to reduce visual stress. Next, if the user is enjoying themselves, the reception desk can provide an interface with bright colors to make the input process more enjoyable. Finally, if the user is tired, the reception desk can provide a simple and highly visible interface to make the input process easier. In this way, by providing an interface design that responds to the user's emotions, the input process becomes more comfortable. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition technology analyzes the user's facial expressions and estimates their emotions. Voice analysis technology analyzes the tone and speed of the user's voice and estimates their emotions. This allows for accurate estimation of user emotions and the provision of appropriate interface designs.

[0072] The input system can suggest highly relevant input options when users enter questions or inquiries, taking into account their geographical location. For example, the input system can suggest questions related to the user's current location. It can also suggest questions related to places the user has visited in the past. Furthermore, it can suggest questions related to travel destinations the user is planning. For example, the input system can suggest questions related to the user's current location. Next, it can suggest questions related to places the user has visited in the past. Finally, it can suggest questions related to travel destinations the user is planning. This streamlines the input process by suggesting input options that take geographical location into account. Geographical location information is obtained using technologies such as GPS data and IP addresses. GPS data obtains the user's current location with high accuracy. IP addresses estimate the geographical location based on the user's internet connection location. This allows the system to accurately obtain the user's geographical location information and suggest appropriate input options.

[0073] The input system can analyze a user's social media activity when they enter questions or inquiries and suggest relevant input options. For example, it can suggest questions related to a user's recent posts. It can also suggest questions related to posts from accounts the user follows. Furthermore, it can suggest questions related to the activities of groups and communities the user participates in. For example, it can suggest questions related to a user's recent posts. Next, it can suggest questions related to posts from accounts the user follows. Finally, it can suggest questions related to the activities of groups and communities the user participates in. This streamlines the input process by suggesting input options based on social media activity. Social media activity analysis is performed using techniques such as post content analysis and follower analysis. Post content analysis analyzes text and images posted by the user and extracts relevant questions. Follower analysis analyzes posts from accounts the user follows and extracts relevant questions. This allows for accurate analysis of users' social media activity and the presentation of appropriate input suggestions.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide more information. If the user is in a hurry, the analysis unit can perform a concise analysis and provide results quickly. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide more information. Next, if the user is in a hurry, the analysis unit can perform a concise analysis and provide results quickly. Finally, if the user is excited, the analysis unit can provide visually stimulating analysis results. This improves analysis accuracy by providing an analysis algorithm that is tailored to the user's emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition technology analyzes the user's facial expressions and estimates emotions. Voice analysis technology analyzes the tone and speed of the user's voice and estimates emotions. This allows for accurate estimation of the user's emotions and the provision of an appropriate analysis algorithm.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the questions and inquiries during the analysis. For example, the analysis unit will perform a detailed analysis for questions and inquiries of high importance. The analysis unit can also perform a concise analysis for questions and inquiries of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail for questions and inquiries of medium importance. For example, the analysis unit will perform a detailed analysis for questions and inquiries of high importance. Next, the analysis unit will perform a concise analysis for questions and inquiries of low importance. Finally, the analysis unit will perform an analysis with an appropriate level of detail for questions and inquiries of medium importance. This improves the accuracy of the analysis by performing the analysis based on the importance of the questions and inquiries. The evaluation of importance is performed using criteria such as user ratings and frequency. User ratings determine importance based on the ratings that users have given to questions and inquiries. Frequency determines importance based on how often the questions and inquiries have been submitted. This allows for accurate evaluation of the importance of questions and inquiries and enables appropriate analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the category of the question or inquiry during analysis. For example, the analysis unit applies a scientific analysis algorithm to questions or inquiries about natural phenomena. It can also apply a physical analysis algorithm to questions or inquiries about physics. Furthermore, it can apply a sociological analysis algorithm to questions or inquiries about social phenomena. For example, the analysis unit applies a scientific analysis algorithm to questions or inquiries about natural phenomena. Next, it applies a physical analysis algorithm to questions or inquiries about physics. Finally, it applies a sociological analysis algorithm to questions or inquiries about social phenomena. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category. Category classification is performed using criteria such as technology categories and business categories. Technology categories include questions or inquiries about technology. Business categories include questions or inquiries about business. This allows for accurate classification of questions and inquiries and the application of appropriate analysis algorithms.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and easy-to-read display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, if the user is nervous, the analysis unit provides a simple and easy-to-read display method. Next, if the user is relaxed, the analysis unit provides a display method that includes detailed information. Finally, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By providing a display method that matches the user's emotions, the analysis results become easier to understand. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition technology analyzes the user's facial expressions and estimates emotions. Voice analysis technology analyzes the tone and speed of the user's voice and estimates emotions. This allows for accurate estimation of the user's emotions and the provision of an appropriate display method.

[0078] The analysis unit can determine the priority of analysis based on when questions and inquiries were submitted. For example, the analysis unit can prioritize recently submitted questions and inquiries. It can also postpone older questions and inquiries. Furthermore, it can moderately analyze questions and inquiries that have been submitted for a moderate period of time. For example, the analysis unit prioritizes recently submitted questions and inquiries. Next, the analysis unit postpones older questions and inquiries. Finally, the analysis unit moderately analyzes questions and inquiries that have been submitted for a moderate period of time. This improves the efficiency of analysis by performing analysis based on submission date. The submission date can be obtained using techniques such as timestamps and submission dates. The timestamp records the exact date and time when a question or inquiry was submitted. The submission date records the date when a question or inquiry was submitted. This allows for accurate acquisition of the submission date of questions and inquiries and the determination of appropriate analysis priorities.

[0079] The analysis unit can adjust the order of analysis based on the relevance of questions and inquiries during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant questions and inquiries. It can also postpone the analysis of less relevant questions and inquiries. Furthermore, the analysis unit can appropriately analyze questions and inquiries of moderate relevance. For example, the analysis unit prioritizes the analysis of highly relevant questions and inquiries. Next, the analysis unit postpones the analysis of less relevant questions and inquiries. Finally, the analysis unit appropriately analyzes questions and inquiries of moderate relevance. This improves the efficiency of the analysis by performing analysis based on relevance. Relevance is evaluated using criteria such as common keywords and related topics. Common keywords evaluate relevance based on keywords common to the questions and inquiries. Related topics evaluate relevance based on topics related to the questions and inquiries. This allows for accurate evaluation of the relevance of questions and inquiries and adjustment of the appropriate order of analysis.

[0080] The data storage unit can estimate the user's emotions and adjust the data storage method based on the estimated emotions. For example, if the user is relaxed, the storage unit can provide a storage method that includes detailed metadata. If the user is in a hurry, the storage unit can also provide a storage method that includes concise metadata. Furthermore, if the user is excited, the storage unit can provide a visually stimulating storage method. For example, if the user is relaxed, the storage unit can provide a storage method that includes detailed metadata. Next, if the user is in a hurry, the storage unit can provide a storage method that includes concise metadata. Finally, if the user is excited, the storage unit can provide a visually stimulating storage method. This makes data management easier by providing storage methods that correspond to the user's emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition technology analyzes the user's facial expressions and estimates emotions. Voice analysis technology analyzes the tone and speed of the user's voice and estimates emotions. This makes it possible to accurately estimate the user's emotions and provide an appropriate storage method.

[0081] The storage unit can select an appropriate storage format by referring to past data when storing data. For example, the storage unit can select the optimal storage format based on the format of data previously stored. It can also select the optimal storage format based on the frequency of use of past data. Furthermore, it can select the optimal storage format based on the relevance of past data. For example, the storage unit selects the optimal storage format based on the format of data previously stored. Next, the storage unit selects the optimal storage format based on the frequency of use of past data. Finally, the storage unit selects the optimal storage format based on the relevance of past data. This streamlines data management by selecting the optimal storage format based on past data. Referencing past data is performed using criteria such as past storage format and data type. Past storage format records how data was stored in the past. Data type records the type of data stored in the past. This allows for accurate referencing of past data and selection of an appropriate storage format.

[0082] The storage unit can apply different storage algorithms depending on the data category when storing data. For example, the storage unit can apply a scientific storage algorithm to data related to natural phenomena. It can also apply a physical storage algorithm to data related to physics. Furthermore, it can apply a sociological storage algorithm to data related to social phenomena. For example, the storage unit applies a scientific storage algorithm to data related to natural phenomena. Next, it applies a physical storage algorithm to data related to physics. Finally, it applies a sociological storage algorithm to data related to social phenomena. This makes data management more efficient by applying storage algorithms according to the category. Category classification is done using criteria such as technical data and business data. Technical data includes data related to technology. Business data includes data related to business. This allows for accurate classification of data categories and the application of appropriate storage algorithms.

[0083] The data storage unit can estimate the user's emotions and determine the data storage priority based on the estimated emotions. For example, if the user is relaxed, the storage unit will prioritize saving detailed data. If the user is in a hurry, the storage unit can also prioritize saving concise data. Furthermore, if the user is excited, the storage unit can also prioritize saving visually stimulating data. For example, if the user is relaxed, the storage unit will prioritize saving detailed data. Next, if the user is in a hurry, the storage unit will prioritize saving concise data. Finally, if the user is excited, the storage unit will prioritize saving visually stimulating data. This makes data management more efficient by providing storage priorities according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition technology analyzes the user's facial expressions and estimates emotions. Voice analysis technology analyzes the tone and speed of the user's voice and estimates emotions. This allows for accurate estimation of the user's emotions and provides appropriate storage priorities.

[0084] The storage unit can determine the storage priority based on the data submission date when storing data. For example, the storage unit can prioritize storing recently submitted data. It can also postpone storing older data. Furthermore, the storage unit can moderately store data with a moderate submission date. For example, the storage unit prioritizes storing recently submitted data. Next, the storage unit postpones storing older data. Finally, the storage unit moderately stores data with a moderate submission date. This streamlines data management by storing data based on submission date. The submission date can be obtained using techniques such as timestamps and submission dates. The timestamp records the exact date and time the data was submitted. The submission date records the date the data was submitted. This allows for accurate acquisition of the data submission date and enables the determination of appropriate storage priorities.

[0085] The storage unit can adjust the order in which data is stored based on its relevance. For example, the storage unit can prioritize storing highly relevant data. It can also postpone storing less relevant data. Furthermore, it can store moderately relevant data. For example, the storage unit prioritizes storing highly relevant data. Next, it postpones storing less relevant data. Finally, it stores moderately relevant data. This streamlines data management by performing storage based on relevance. Relevance is evaluated using criteria such as common keywords and related topics. Common keywords evaluate relevance based on keywords common to the data. Related topics evaluate relevance based on topics related to the data. This allows for accurate evaluation of data relevance and adjustment of the appropriate storage order.

[0086] The search unit can estimate the user's emotions and adjust the display method of search results according to the estimated emotions. For example, if the user is nervous, the search unit provides a simple and highly visible display method. If the user is relaxed, the search unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the search unit can provide a display method that gets straight to the point. For example, if the user is nervous, the search unit provides a simple and highly visible display method. Next, if the user is relaxed, the search unit provides a display method that includes detailed information. Finally, if the user is in a hurry, the search unit provides a display method that gets straight to the point. By providing a display method that matches the user's emotions, it becomes easier to understand the search results. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition technology analyzes the user's facial expressions and estimates emotions. Voice analysis technology analyzes the tone and speed of the user's voice and estimates emotions. This makes it possible to accurately estimate the user's emotions and provide an appropriate display method.

[0087] The search engine can apply an appropriate search algorithm by referring to past search history during a search. For example, the search engine can apply the optimal search algorithm based on keywords the user has searched for in the past. It can also prioritize displaying highly relevant search results based on the user's past search history. Furthermore, the search engine can analyze the user's past search patterns and apply the optimal search algorithm. For example, the search engine applies the optimal search algorithm based on keywords the user has searched for in the past. Next, the search engine prioritizes displaying highly relevant search results based on the user's past search history. Finally, the search engine analyzes the user's past search patterns and applies the optimal search algorithm. This improves search accuracy by applying the optimal search algorithm based on past search history. Past search history is referenced using criteria such as past search keywords and search results. Past search keywords record keywords the user has searched for in the past. Search results record search results the user has obtained in the past. This allows for accurate reference to past search history and the application of an appropriate search algorithm.

[0088] The search unit can apply different search algorithms depending on the data category during a search. For example, the search unit can apply a scientific search algorithm to data related to natural phenomena. It can also apply a physical search algorithm to data related to physics. Furthermore, it can apply a sociological search algorithm to data related to social phenomena. For example, the search unit applies a scientific search algorithm to data related to natural phenomena. Next, it applies a physical search algorithm to data related to physics. Finally, it applies a sociological search algorithm to data related to social phenomena. This improves search accuracy by applying a search algorithm appropriate to the category. Category classification is performed using criteria such as technical data and business data. Technical data includes data related to technology. Business data includes data related to business. This allows for accurate classification of data categories and the application of appropriate search algorithms.

[0089] The search engine can estimate the user's emotions and prioritize search results based on those emotions. For example, if the user is relaxed, the search engine will prioritize displaying detailed information. If the user is in a hurry, it can prioritize displaying concise information. Furthermore, if the user is excited, it can prioritize displaying visually stimulating information. This makes it easier to understand search results by providing prioritization according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition technology analyzes the user's facial expressions and estimates their emotions. Voice analysis technology analyzes the tone and speed of the user's voice and estimates their emotions. This allows for accurate estimation of the user's emotions and provides appropriate search result prioritization.

[0090] The search function can prioritize search results based on when the data was submitted. For example, it might prioritize recently submitted data. It can also prioritize older data. Furthermore, it can appropriately display data with a moderate submission time. For example, it might prioritize recently submitted data, then prioritize older data, and finally display data with a moderate submission time. This makes it easier to understand search results when searching based on submission time. The submission time is obtained using techniques such as timestamps and submission dates. A timestamp records the exact date and time the data was submitted. The submission date records the date the data was submitted. This allows for accurate retrieval of data submission time and appropriate prioritization of search results.

[0091] The search engine can adjust the order of search results based on the relevance of the data during a search. For example, the search engine can prioritize displaying highly relevant data. It can also postpone displaying less relevant data. Furthermore, it can display moderately relevant data appropriately. For example, the search engine prioritizes displaying highly relevant data. Next, it postpones displaying less relevant data. Finally, it displays moderately relevant data appropriately. This makes it easier to understand the search results by performing a relevance-based search. Relevance is evaluated using criteria such as common keywords and related topics. Common keywords evaluate relevance based on keywords common to the data. Related topics evaluate relevance based on topics related to the data. This allows for accurate evaluation of data relevance and adjustment of the order of search results appropriately.

[0092] The sharing function can estimate the user's emotions and adjust the data sharing method according to the estimated emotions. For example, if the user is relaxed, the sharing function can provide a way to share detailed data. If the user is in a hurry, the sharing function can also provide a way to share concise data. Furthermore, if the user is excited, the sharing function can also provide a way to share visually stimulating data. For example, if the user is relaxed, the sharing function can provide a way to share detailed data. Next, if the user is in a hurry, the sharing function can provide a way to share concise data. Finally, if the user is excited, the sharing function can provide a way to share visually stimulating data. This makes data sharing more efficient by providing a sharing method that is tailored to the user's emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition technology analyzes the user's facial expressions and estimates emotions. Voice analysis technology analyzes the tone and speed of the user's voice and estimates emotions. This allows for accurate estimation of the user's emotions and the provision of appropriate sharing methods.

[0093] The sharing system can select an appropriate sharing method by referring to past sharing history when sharing data. For example, the sharing system can select the optimal sharing method based on the format of data shared in the past. It can also select frequently used sharing methods from past sharing history. Furthermore, the sharing system can analyze past sharing history to select the most efficient sharing method. For example, the sharing system selects the optimal sharing method based on the format of data shared in the past. Next, the sharing system selects frequently used sharing methods from past sharing history. Finally, the sharing system analyzes past sharing history to select the most efficient sharing method. This streamlines data sharing by selecting the optimal sharing method based on past sharing history. Referencing past sharing history is done using criteria such as past shared data and sharing methods. Past shared data records what kind of data was shared in the past. Sharing methods record how data was shared in the past. This allows for accurate referencing of past sharing history and selection of an appropriate sharing method.

[0094] The sharing function can apply different sharing algorithms depending on the data category when sharing data. For example, it can apply a scientific sharing algorithm to data on natural phenomena, a physical sharing algorithm to data on physics, and a sociological sharing algorithm to data on social phenomena. This makes data sharing more efficient by applying a sharing algorithm appropriate to the category. Category classification is done using criteria such as technical data and business data. Technical data includes data on technology, and business data includes data on business. This allows for accurate classification of data categories and the application of appropriate sharing algorithms.

[0095] The sharing function can estimate the user's emotions and prioritize shared data based on those emotions. For example, if the user is relaxed, the sharing function will prioritize sharing detailed data. If the user is in a hurry, the sharing function can also prioritize sharing concise data. Furthermore, if the user is excited, the sharing function can also prioritize sharing visually stimulating data. For example, if the user is relaxed, the sharing function will prioritize sharing detailed data. Next, if the user is in a hurry, the sharing function will prioritize sharing concise data. Finally, if the user is excited, the sharing function will prioritize sharing visually stimulating data. This makes data sharing more efficient by providing priorities according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition technology analyzes the user's facial expressions and estimates emotions. Voice analysis technology analyzes the tone and speed of the user's voice and estimates emotions. This allows for accurate estimation of the user's emotions and provides appropriate prioritization of shared data.

[0096] The sharing system can prioritize data sharing based on when the data was submitted. For example, it might prioritize sharing recently submitted data. It can also postpone sharing older data. Furthermore, it can share data of moderate age to a reasonable extent. For example, it might prioritize sharing recently submitted data, then postpone sharing older data, and finally share data of moderate age to a reasonable extent. This makes data sharing more efficient by sharing based on submission date. The submission date can be obtained using techniques such as timestamps and submission dates. The timestamp records the exact date and time the data was submitted. The submission date records the date the data was submitted. This allows for accurate data submission and the determination of appropriate sharing priorities.

[0097] The sharing system can adjust the order of data sharing based on its relevance. For example, it can prioritize sharing highly relevant data. It can also postpone sharing less relevant data. Furthermore, it can share moderately relevant data as well. For example, it can prioritize sharing highly relevant data, then postpone sharing less relevant data, and finally share moderately relevant data as well. This makes data sharing more efficient by sharing based on relevance. Relevance is evaluated using criteria such as common keywords and related topics. Common keywords evaluate relevance based on keywords common to the data. Related topics evaluate relevance based on topics related to the data. This allows for accurate evaluation of data relevance and adjustment of the appropriate sharing order. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, storage unit, search unit, and sharing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input questions or inquiries. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses natural language processing technology to understand the content of questions or inquiries and classify them into appropriate categories. The storage unit stores data in the database 24 of the data processing unit 12. The search unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to search for and retrieve information from the database. The sharing unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides a function for multiple platforms to share data. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, storage unit, search unit, and sharing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input questions or inquiries. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses natural language processing technology to understand the content of questions or inquiries and classify them into appropriate categories. The storage unit stores data in, for example, the database 24 of the data processing unit 12. The search unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides an interface for the user to search for and retrieve information from the database. The sharing unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides a function for multiple platforms to share data. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, storage unit, search unit, and sharing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input questions or inquiries. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses natural language processing technology to understand the content of questions or inquiries and classify them into appropriate categories. The storage unit stores data in the database 24 of the data processing unit 12. The search unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to search for and retrieve information from the database. The sharing unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides a function for multiple platforms to share data. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, storage unit, search unit, and sharing unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input questions or inquiries. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses natural language processing technology to understand the content of questions or inquiries and classify them into appropriate categories. The storage unit stores data in the database 24 of the data processing unit 12. The search unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to search for and retrieve information from the database. The sharing unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides a function for multiple platforms to share data.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can automatically suggest relevant past questions and answers based on the user's input. For example, if a user enters "Why is the sky blue?", the reception desk will search for similar questions in the past and present relevant answers. The reception desk can also display suggested related questions based on parts of the question entered by the user. Furthermore, the reception desk can suggest relevant topics and articles based on the category of the question entered by the user. This allows users to quickly obtain information related to their input, improving the efficiency of question resolution.

[0100] The analysis unit can use a combination of multiple analysis algorithms for user input. For example, morphological analysis and grammatical analysis can be combined to perform more accurate analysis. Furthermore, semantic analysis and contextual analysis can be combined to gain a deeper understanding of the intent behind the question. In addition, machine learning algorithms can be used to improve analysis accuracy based on past analysis results. This allows the analysis unit to analyze user input more accurately and classify it into appropriate categories.

[0101] The storage unit can select a storage format based on the importance of the data when saving it. For example, highly important data can be stored in a highly redundant format to prevent data loss. Less important data can be stored in a compressed format to improve storage efficiency. Furthermore, data of moderate importance can be stored in a balanced format to achieve both efficiency and security. In this way, selecting a storage format according to the importance of the data improves the efficiency of data management.

[0102] The search function can prioritize displaying relevant search results based on the user's search history. For example, it can prioritize displaying information related to keywords the user has searched for in the past. It can also suggest relevant information based on the content of pages the user has previously viewed. Furthermore, it can analyze the user's search patterns to provide optimal search results. This allows the system to leverage the user's search history to provide more relevant search results.

[0103] The shared section allows for adjusting the scope of data sharing based on user access permissions. For example, only publicly available information can be shared with general users, while data containing confidential information can be shared with administrators. Data security can also be ensured by granting access only to specific groups. Furthermore, the level of detail of shared data can be adjusted according to the user's role. This allows for both information security and efficient data sharing by aligning data sharing with user access permissions.

[0104] The reception desk can estimate the user's emotions and change the design of the input interface based on those emotions. For example, if the user is stressed, it can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, it can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, it can provide a simple and highly visible interface to make the input process easier. In this way, providing an interface design that responds to the user's emotions makes the input process more comfortable.

[0105] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on those emotions. For example, if the user is relaxed, it can perform a detailed analysis and provide more information. If the user is in a hurry, it can perform a concise analysis and provide results quickly. Furthermore, if the user is excited, it can provide visually stimulating analysis results. By providing an analysis algorithm that responds to the user's emotions, the accuracy of the analysis is improved.

[0106] The data storage unit can estimate the user's emotions and adjust the data storage method based on the estimated emotions. For example, if the user is relaxed, it can provide a storage method that includes detailed metadata. If the user is in a hurry, it can provide a storage method that includes concise metadata. Furthermore, if the user is excited, it can provide a visually stimulating storage method. This makes data management easier by providing storage methods that are tailored to the user's emotions.

[0107] The search engine can estimate the user's emotions and adjust how search results are displayed accordingly. For example, if the user is stressed, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By providing a display method that suits the user's emotions, it makes it easier to understand the search results.

[0108] The sharing function can estimate the user's emotions and adjust the data sharing method accordingly. For example, if the user is relaxed, it can provide a method for sharing detailed data. If the user is in a hurry, it can provide a method for sharing concise data. Furthermore, if the user is excited, it can provide a method for sharing visually stimulating data. This streamlines data sharing by providing sharing methods tailored to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives questions or inquiries from users. These questions or inquiries may include, but are not limited to, technical questions or general inquiries. The reception desk may, for example, provide an interface for users to input their questions or inquiries. Step 2: The analysis unit analyzes the questions or inquiries received by the reception unit and classifies them into appropriate categories. The analysis unit understands the content of the questions or inquiries using, for example, natural language processing techniques and classifies them into appropriate categories. Natural language processing techniques include, for example, morphological analysis, grammatical analysis, and semantic analysis. Step 3: The storage unit stores the data analyzed by the analysis unit in a database. The database may include, for example, a relational database or a NoSQL database. Step 4: The search unit searches for and retrieves data stored by the storage unit. The search unit provides, for example, an interface for users to search for and retrieve information from the database. The information to be searched may include, for example, text data, image data, etc. Step 5: The sharing section provides functionality for multiple platforms to share data. For example, the sharing section collects and integrates data from each platform. Platforms include, for example, web platforms and mobile platforms.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk to receive questions or inquiries from users, An analysis unit analyzes the questions or inquiries received by the reception unit and classifies them into appropriate categories, A storage unit that stores the data analyzed by the aforementioned analysis unit in a database, A search unit that searches for and retrieves data stored by the storage unit, It includes a sharing section for multiple platforms to share data. A system characterized by the following features.

2. The aforementioned analysis unit, Using natural language processing techniques, we understand the content of questions and classify them into appropriate categories. The system according to feature 1.

3. The storage unit is Save the analyzed data to the database. The system according to feature 1.

4. The aforementioned search unit, Users search for and retrieve information from the database. The system according to feature 1.

5. The aforementioned shared portion is, Collect and integrate data from each platform. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for questions and inquiries based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests appropriate input methods. The system according to feature 1.

8. The aforementioned reception unit is When a user enters a question or inquiry, the system will suggest possible answers based on the user's current areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and changes the design of the input interface according to the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When users enter questions or inquiries, the system will consider their geographical location to suggest highly relevant input options. The system according to feature 1.

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