System

Abstract

An object of a system according to an embodiment is to efficiently search for information on a user's past behavior or event and provide an appropriate answer.SOLUTION: A system according to an embodiment includes a collection unit, a vectorization unit, a neighborhood search unit, and a provision unit. The collection unit collects information related to a behavior or an event of a user. The vectorization unit vectorizes the information collected by the collection unit. The neighborhood search unit performs neighborhood search for the user's question based on the information vectorized by the vectorization unit. The providing unit provides the information obtained by the neighborhood search unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently search for information about a user's past actions and events and obtain appropriate answers.

[0005] The system according to the embodiment aims to efficiently search for information about a user's past actions and events and provide appropriate answers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a vectorization unit, a neighborhood search unit, and a providing unit. The collection unit collects information related to user behavior and events. The vectorization unit vectorizes the information collected by the collection unit. The neighborhood search unit performs a neighborhood search for a user's question based on the information vectorized by the vectorization unit. The providing unit provides the information obtained by the neighborhood search unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently search for information about a user's past actions and events and provide appropriate answers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A search system according to an embodiment of the present invention allows a user to search for events that the user has experienced in the past. When the user grants tracking permission, the search system collects and vectorizes information about the user's behavior and events, performs a proximity search in response to the user's questions, and provides the obtained information. For example, the search system collects the user's email sending and receiving history, calendar appointments, location information, photos, and messaging app history. The collected information is then vectorized and converted into numerical data. When the user inputs a question, the search system performs a proximity search based on the vectorized information to search for the most relevant information. For example, in response to the question, "Where was I and what was I doing on Christmas five years ago?", the search system searches for information related to Christmas five years ago and provides it to the user. This allows the user to easily search for and recall past events and experiences. This allows the search system to efficiently collect, vectorize, perform a proximity search, and provide information about the user's behavior and events. For example, the search system can support the user's memory, such as where important meeting materials were shared or where and what they were doing on a particular day.

[0029] A search system according to an embodiment includes a collection unit, a vectorization unit, a neighborhood search unit, and a providing unit. The collection unit collects information related to user behavior and events. For example, the collection unit can collect a user's email sending and receiving history. The collection unit can also collect calendar schedules. The collection unit can also collect location information. The collection unit can also collect photos. The collection unit can also collect messaging app history. The vectorization unit vectorizes the information collected by the collection unit. For example, the vectorization unit converts the collected information into numerical data. The vectorization unit can also vectorize the information to make it easier to search and analyze. The vectorization unit can also vectorize information related to a specific event. The neighborhood search unit performs a neighborhood search for a user's question based on the information vectorized by the vectorization unit. For example, the neighborhood search unit searches for information closest to the user's question. The neighborhood search unit can also search for information related to the user's question. The neighborhood search unit can also search for information most relevant to the user's question. The providing unit provides the information obtained by the neighborhood search unit to the user. For example, the providing unit provides a response to a user's question. The providing unit can also display search results to the user. The providing unit can also provide information to the user. This enables the search system according to the embodiment to efficiently collect, vectorize, search nearby, and provide information about user behavior and events.

[0030] The collection unit can collect information from emails, calendars, location information, photos, and messaging apps. The collection unit, for example, collects a user's email sending and receiving history. For example, the collection unit can collect email subjects and sending dates and times. The collection unit can also collect calendar appointments. For example, the collection unit can collect calendar event names and dates and times. The collection unit can also collect location information. For example, the collection unit can collect GPS data and the accuracy of location information. The collection unit can also collect photos. For example, the collection unit can collect the date, time, and location at which a photo was taken. The collection unit can also collect messaging app history. For example, the collection unit can collect the date, time, and content of messages sent and received. In this way, by collecting information from a variety of information sources, it is possible to comprehensively obtain information about the user's behavior and events.

[0031] The collection unit encrypts the collected information and can provide it to a third party only with the user's permission. The collection unit, for example, encrypts the collected information. For example, the collection unit can encrypt the information using an encryption algorithm such as AES (Advanced Encryption Standard). The collection unit can also securely manage keys for the encrypted information. The collection unit also provides the information to a third party only with the user's permission. For example, the collection unit can provide the information to an external service provider only with the user's permission. The collection unit can also provide the information to an advertising company only with the user's permission. This protects the user's privacy and ensures the safety of the information.

[0032] The vectorization unit can convert the collected information into numerical data. For example, the vectorization unit can vectorize text data using a natural language processing algorithm. The vectorization unit can also convert image data into numerical data. For example, the vectorization unit can vectorize image data using a computer vision algorithm. The vectorization unit can also convert location information into numerical data. For example, the vectorization unit can vectorize location information using a geographic information system (GIS) algorithm. By converting the information into numerical data, it becomes easier to search and analyze.

[0033] The neighborhood search unit can search for highly relevant information based on the user's question. The neighborhood search unit, for example, searches for highly relevant information based on the user's question. For example, the neighborhood search unit can search for information that is most relevant to the user's question. The neighborhood search unit can also search for information related to the user's question. The neighborhood search unit can also search for information that is closest to the user's question. This makes it possible to provide the most relevant information to the user's question.

[0034] The providing unit can provide a response to a user's question. The providing unit, for example, provides a response to a user's question. For example, the providing unit can display search results for the user's question. The providing unit can also provide information to the user. The providing unit can also provide a response to the user. This makes it possible to provide an appropriate response to the user's question.

[0035] The collection unit can analyze the user's past behavioral patterns and select the optimal information collection method. The collection unit, for example, analyzes the user's past behavioral patterns. For example, the collection unit can select the optimal information collection method based on the user's past behavioral patterns. The collection unit can also prioritize collecting information from apps that the user has frequently used in the past. The collection unit can also concentrate information collection during a specific time period based on the user's past behavioral patterns. The collection unit can also prioritize collecting information related to a specific event based on the user's past behavioral patterns. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavioral patterns.

[0036] The collection unit can filter information based on the user's current activities and areas of interest when collecting information. For example, the collection unit can preferentially collect information related to events in which the user is currently participating. The collection unit can also filter and collect information related to topics in which the user has recently become interested. The collection unit can also exclude unnecessary information and collect only highly relevant information based on the user's current activities. In this way, highly relevant information can be collected by filtering information based on the user's current activities and areas of interest.

[0037] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting information. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Also, if the user is uploading images, the collection unit can prioritize collecting image data. This enables efficient information collection by selecting the optimal collection means depending on the user's input method.

[0038] The collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information when collecting information. For example, the collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information when collecting information. For example, the collection unit can prioritize collecting event information related to the user's current location. The collection unit can also prioritize collecting information about nearby stores and facilities based on the user's location information. The collection unit can also prioritize collecting local news and weather information based on the user's location information. In this way, highly relevant information can be provided by collecting information in consideration of the user's geographical location information.

[0039] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can collect information shared by the user on social media. The collection unit can also analyze the content of the user's posts on social media and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit can preferentially collect information from information sources that the user has previously rated highly. The collection unit can also adjust the type of information to be collected based on the user's past feedback. The collection unit can also optimize the collection method by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0041] The vectorization unit can adjust the level of detail of the vectorization based on the importance of the information during vectorization. For example, the vectorization unit can vectorize important meeting materials in detail to improve search accuracy. The vectorization unit can also simplify and vectorize everyday messages to improve processing speed. The vectorization unit can also vectorize highly important events in detail to make it easier to search for related information. In this way, adjusting the level of detail of the vectorization based on the importance of the information enables efficient information provision.

[0042] The vectorization unit can apply different vectorization algorithms depending on the category of information during vectorization. For example, the vectorization unit can vectorize email content using a natural language processing algorithm. The vectorization unit can also vectorize image data using a computer vision algorithm. The vectorization unit can also vectorize location information using a geographic information system (GIS) algorithm. By applying different vectorization algorithms depending on the category of information, it is possible to provide highly accurate information.

[0043] The vectorization unit can improve the accuracy of vectorization by referring to the user's past vectorization results during vectorization. For example, the vectorization unit can improve the accuracy of vectorization by referring to the user's past vectorization results during vectorization. For example, the vectorization unit can improve the accuracy when vectorizing similar information based on the user's past vectorization results. The vectorization unit can also analyze the user's past vectorization results and adjust algorithm parameters. The vectorization unit can also optimize the vectorization method by referring to the user's past vectorization results. In this way, the accuracy of vectorization is improved by referring to the user's past vectorization results.

[0044] The vectorization unit can determine the priority of vectorization based on the time when information was collected during vectorization. The vectorization unit, for example, determines the priority of vectorization based on the time when information was collected during vectorization. For example, the vectorization unit can vectorize recently collected information preferentially to provide the latest information. The vectorization unit can also vectorize information of important events preferentially to make it easier to search for related information. The vectorization unit can also simplify and vectorize old information to improve processing speed. In this way, by determining the priority of vectorization based on the time when information was collected, the latest information can be preferentially provided.

[0045] The vectorization unit can adjust the order of vectorization based on the relevance of information during vectorization. For example, the vectorization unit can adjust the order of vectorization based on the relevance of information during vectorization. For example, the vectorization unit can preferentially vectorize information that is highly relevant to a user's question. The vectorization unit can also preferentially vectorize information related to an important event. The vectorization unit can also preferentially vectorize information that is highly relevant based on the user's past search history. In this way, by adjusting the order of vectorization based on the relevance of information, highly relevant information can be preferentially provided.

[0046] The vectorization unit can adjust the use of technical terms for vectorization according to the user's level of expertise during vectorization. For example, the vectorization unit can adjust the use of technical terms for vectorization according to the user's level of expertise during vectorization. For example, if the user has technical knowledge, the vectorization unit can vectorize using technical terms. Also, if the user has general knowledge, the vectorization unit can vectorize using simple terms. Also, the vectorization unit can vectorize using appropriate terms based on the user's past search history. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide information that is easier to understand.

[0047] The neighborhood search unit can improve the accuracy of the search by taking into account the interrelationships of information during neighborhood search. For example, the neighborhood search unit can improve the accuracy of the search by taking into account the interrelationships of information during neighborhood search. For example, the neighborhood search unit can perform a highly accurate neighborhood search based on information on related events and actions. The neighborhood search unit can also analyze the interrelationships of information and preferentially search for highly related information. The neighborhood search unit can also perform a highly accurate search by taking into account the interrelationships of information and excluding unnecessary information. In this way, by taking into account the interrelationships of information, a highly accurate neighborhood search is possible.

[0048] The neighborhood search unit can perform a neighborhood search while taking into account attribute information of the information provider. For example, the neighborhood search unit can perform a neighborhood search while taking into account attribute information of the information provider. For example, if the information provider is trustworthy, the neighborhood search unit can search for that information preferentially. The neighborhood search unit can also search for highly relevant information while taking into account the specialized knowledge of the information provider. The neighborhood search unit can also search for highly reliable information preferentially based on the information provider's past provision history. In this way, highly reliable information can be provided by taking into account the attribute information of the information provider.

[0049] The neighborhood search unit can weight the search based on the frequency of information provision during the neighborhood search. For example, the neighborhood search unit can weight the search based on the frequency of information provision during the neighborhood search. For example, the neighborhood search unit can prioritize searching for information that is provided more frequently and provide highly relevant information. The neighborhood search unit can also lower the weight of information that is provided less frequently and exclude it from the search results. The neighborhood search unit can also evaluate the reliability of information based on the frequency of information provision and improve the accuracy of the search. As a result, by weighting the search based on the frequency of information provision, highly relevant information can be provided preferentially.

[0050] The neighborhood search unit can perform a search taking into account the geographical distribution of information during a neighborhood search. For example, the neighborhood search unit can perform a search taking into account the geographical distribution of information during a neighborhood search. For example, the neighborhood search unit can prioritize searching for information close to the user's current location. The neighborhood search unit can also prioritize searching for information that is highly geographically relevant. The neighborhood search unit can also search for highly relevant information based on the user's past location information. In this way, highly relevant information can be provided by taking into account the geographical distribution of information.

[0051] The neighborhood search unit can improve the accuracy of the search during a neighborhood search by referring to literature related to the information. For example, the neighborhood search unit can improve the accuracy of the search by referring to literature related to the information during a neighborhood search. For example, the neighborhood search unit can perform a highly accurate neighborhood search by referring to related academic papers. The neighborhood search unit can also search for highly relevant information by referring to related patent documents. The neighborhood search unit can also improve the accuracy of the search by referring to related technical literature and excluding unnecessary information. As a result, by referring to literature related to the information, a highly accurate neighborhood search is possible.

[0052] The neighborhood search unit can perform a neighborhood search while taking into account the market value of the information. For example, the neighborhood search unit can perform a neighborhood search while taking into account the market value of the information. For example, the neighborhood search unit can prioritize searching for information with high market value and provide highly relevant information. The neighborhood search unit can also lower the weight of information with low market value and exclude it from the search results. The neighborhood search unit can also evaluate the reliability of information based on market value and improve the accuracy of the search. In this way, highly relevant information can be provided by taking into account the market value of the information.

[0053] The providing unit can select the optimal display method by referring to the user's past operation history when providing information. For example, the providing unit can select the optimal display method by referring to the user's past operation history when providing information. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. The providing unit can also analyze the user's past operation history and customize the display method. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0054] The providing unit can customize the display content according to the user's current task when providing information. For example, the providing unit can customize the display content according to the user's current task when providing information. For example, the providing unit can prioritize displaying information related to the task the user is currently performing. The providing unit can also exclude unnecessary information and display highly relevant information based on the user's current task. The providing unit can also analyze the user's current task and suggest optimal display content. In this way, highly relevant information can be provided by customizing the display content according to the user's current task.

[0055] The providing unit can improve the display method by reflecting user feedback when providing information. For example, the providing unit can improve the display method by reflecting user feedback when providing information. For example, the providing unit can customize the display method based on user feedback. The providing unit can also analyze user feedback and optimize the display method. The providing unit can also improve the display method by referring to user feedback. In this way, the display method can be optimized by reflecting user feedback.

[0056] The providing unit can select the optimal display method by taking into consideration the user's device information when providing information. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, the optimal display method can be provided by taking into consideration the user's device information.

[0057] The providing unit can make the display content multilingual in accordance with the user's language setting when providing information. For example, the providing unit can make the display content multilingual in accordance with the user's language setting when providing information. For example, the providing unit can automatically set the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide information in that language. This makes it possible to provide more appropriate information by making the display content multilingual in accordance with the user's language setting.

[0058] The providing unit can customize the method of providing information by reflecting the user's past feedback when providing information. For example, the providing unit customizes the method of providing information by reflecting the user's past feedback when providing information. For example, the providing unit can customize the method of providing information based on the user's past feedback. The providing unit can also analyze the user's past feedback and optimize the method of providing information. The providing unit can also improve the method of providing information by referring to the user's past feedback. In this way, the method of providing information can be optimized by reflecting the user's past feedback.

[0059] The neighborhood search unit learns the history of past questions and answers and can provide more accurate answers. The neighborhood search unit, for example, learns the history of past questions and answers. For example, the neighborhood search unit can provide more accurate answers based on the history of past questions and answers. The neighborhood search unit can also analyze the history of past questions and answers and adjust the parameters of the algorithm. The neighborhood search unit can also improve the accuracy of the answers by referring to the history of past questions and answers. In this way, the accuracy of the answers is improved by learning the history of past questions and answers.

[0060] The collection unit can analyze the user's past behavioral patterns and select the optimal information collection method. The collection unit, for example, analyzes the user's past behavioral patterns. For example, the collection unit can select the optimal information collection method based on the user's past behavioral patterns. The collection unit can also prioritize collecting information from apps that the user has frequently used in the past. The collection unit can also concentrate information collection during a specific time period based on the user's past behavioral patterns. The collection unit can also prioritize collecting information related to a specific event based on the user's past behavioral patterns. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavioral patterns.

[0061] The collection unit can filter information based on the user's current activities and areas of interest when collecting information. For example, the collection unit can preferentially collect information related to events in which the user is currently participating. The collection unit can also filter and collect information related to topics in which the user has recently become interested. The collection unit can also exclude unnecessary information and collect only highly relevant information based on the user's current activities. In this way, highly relevant information can be collected by filtering information based on the user's current activities and areas of interest.

[0062] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting information. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Also, if the user is uploading images, the collection unit can prioritize collecting image data. This enables efficient information collection by selecting the optimal collection means depending on the user's input method.

[0063] The collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information when collecting information. For example, the collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information when collecting information. For example, the collection unit can prioritize collecting event information related to the user's current location. The collection unit can also prioritize collecting information about nearby stores and facilities based on the user's location information. The collection unit can also prioritize collecting local news and weather information based on the user's location information. In this way, highly relevant information can be provided by collecting information in consideration of the user's geographical location information.

[0064] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can collect information shared by the user on social media. The collection unit can also analyze the content of the user's posts on social media and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0065] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit can preferentially collect information from information sources that the user has previously rated highly. The collection unit can also adjust the type of information to be collected based on the user's past feedback. The collection unit can also optimize the collection method by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0066] The vectorization unit can adjust the level of detail of the vectorization based on the importance of the information during vectorization. For example, the vectorization unit can vectorize important meeting materials in detail to improve search accuracy. The vectorization unit can also simplify and vectorize everyday messages to improve processing speed. The vectorization unit can also vectorize highly important events in detail to make it easier to search for related information. In this way, adjusting the level of detail of the vectorization based on the importance of the information enables efficient information provision.

[0067] The vectorization unit can apply different vectorization algorithms depending on the category of information during vectorization. For example, the vectorization unit can vectorize email content using a natural language processing algorithm. The vectorization unit can also vectorize image data using a computer vision algorithm. The vectorization unit can also vectorize location information using a geographic information system (GIS) algorithm. By applying different vectorization algorithms depending on the category of information, it is possible to provide highly accurate information.

[0068] The vectorization unit can improve the accuracy of vectorization by referring to the user's past vectorization results during vectorization. For example, the vectorization unit can improve the accuracy of vectorization by referring to the user's past vectorization results during vectorization. For example, the vectorization unit can improve the accuracy when vectorizing similar information based on the user's past vectorization results. The vectorization unit can also analyze the user's past vectorization results and adjust algorithm parameters. The vectorization unit can also optimize the vectorization method by referring to the user's past vectorization results. In this way, the accuracy of vectorization is improved by referring to the user's past vectorization results.

[0069] The vectorization unit can determine the priority of vectorization based on the time when information was collected during vectorization. The vectorization unit, for example, determines the priority of vectorization based on the time when information was collected during vectorization. For example, the vectorization unit can vectorize recently collected information preferentially to provide the latest information. The vectorization unit can also vectorize information of important events preferentially to make it easier to search for related information. The vectorization unit can also simplify and vectorize old information to improve processing speed. In this way, by determining the priority of vectorization based on the time when information was collected, the latest information can be preferentially provided.

[0070] The vectorization unit can adjust the order of vectorization based on the relevance of information during vectorization. For example, the vectorization unit can adjust the order of vectorization based on the relevance of information during vectorization. For example, the vectorization unit can preferentially vectorize information that is highly relevant to a user's question. The vectorization unit can also preferentially vectorize information related to an important event. The vectorization unit can also preferentially vectorize information that is highly relevant based on the user's past search history. In this way, by adjusting the order of vectorization based on the relevance of information, highly relevant information can be preferentially provided.

[0071] The vectorization unit can adjust the use of technical terms for vectorization according to the user's level of expertise during vectorization. For example, the vectorization unit can adjust the use of technical terms for vectorization according to the user's level of expertise during vectorization. For example, if the user has technical knowledge, the vectorization unit can vectorize using technical terms. Also, if the user has general knowledge, the vectorization unit can vectorize using simple terms. Also, the vectorization unit can vectorize using appropriate terms based on the user's past search history. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide information that is easier to understand.

[0072] The neighborhood search unit can improve the accuracy of the search by taking into account the interrelationships of information during neighborhood search. For example, the neighborhood search unit can improve the accuracy of the search by taking into account the interrelationships of information during neighborhood search. For example, the neighborhood search unit can perform a highly accurate neighborhood search based on information on related events and actions. The neighborhood search unit can also analyze the interrelationships of information and preferentially search for highly related information. The neighborhood search unit can also perform a highly accurate search by taking into account the interrelationships of information and excluding unnecessary information. In this way, by taking into account the interrelationships of information, a highly accurate neighborhood search is possible.

[0073] The neighborhood search unit can perform a neighborhood search while taking into account attribute information of the information provider. For example, the neighborhood search unit can perform a neighborhood search while taking into account attribute information of the information provider. For example, if the information provider is trustworthy, the neighborhood search unit can search for that information preferentially. The neighborhood search unit can also search for highly relevant information while taking into account the specialized knowledge of the information provider. The neighborhood search unit can also search for highly reliable information preferentially based on the information provider's past provision history. In this way, highly reliable information can be provided by taking into account the attribute information of the information provider.

[0074] The neighborhood search unit can weight the search based on the frequency of information provision during the neighborhood search. For example, the neighborhood search unit can weight the search based on the frequency of information provision during the neighborhood search. For example, the neighborhood search unit can prioritize searching for information that is provided more frequently and provide highly relevant information. The neighborhood search unit can also lower the weight of information that is provided less frequently and exclude it from the search results. The neighborhood search unit can also evaluate the reliability of information based on the frequency of information provision and improve the accuracy of the search. As a result, by weighting the search based on the frequency of information provision, highly relevant information can be provided preferentially.

[0075] The neighborhood search unit can perform a search taking into account the geographical distribution of information during a neighborhood search. For example, the neighborhood search unit can perform a search taking into account the geographical distribution of information during a neighborhood search. For example, the neighborhood search unit can prioritize searching for information close to the user's current location. The neighborhood search unit can also prioritize searching for information that is highly geographically relevant. The neighborhood search unit can also search for highly relevant information based on the user's past location information. In this way, highly relevant information can be provided by taking into account the geographical distribution of information.

[0076] The neighborhood search unit can improve the accuracy of the search during a neighborhood search by referring to literature related to the information. For example, the neighborhood search unit can improve the accuracy of the search by referring to literature related to the information during a neighborhood search. For example, the neighborhood search unit can perform a highly accurate neighborhood search by referring to related academic papers. The neighborhood search unit can also search for highly relevant information by referring to related patent documents. The neighborhood search unit can also improve the accuracy of the search by referring to related technical literature and excluding unnecessary information. As a result, by referring to literature related to the information, a highly accurate neighborhood search is possible.

[0077] The neighborhood search unit can perform a neighborhood search while taking into account the market value of the information. For example, the neighborhood search unit can perform a neighborhood search while taking into account the market value of the information. For example, the neighborhood search unit can prioritize searching for information with high market value and provide highly relevant information. The neighborhood search unit can also lower the weight of information with low market value and exclude it from the search results. The neighborhood search unit can also evaluate the reliability of information based on market value and improve the accuracy of the search. In this way, highly relevant information can be provided by taking into account the market value of the information.

[0078] The providing unit can select the optimal display method by referring to the user's past operation history when providing information. For example, the providing unit can select the optimal display method by referring to the user's past operation history when providing information. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. The providing unit can also analyze the user's past operation history and customize the display method. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0079] The providing unit can customize the display content according to the user's current task when providing information. For example, the providing unit can customize the display content according to the user's current task when providing information. For example, the providing unit can prioritize displaying information related to the task the user is currently performing. The providing unit can also exclude unnecessary information and display highly relevant information based on the user's current task. The providing unit can also analyze the user's current task and suggest optimal display content. In this way, highly relevant information can be provided by customizing the display content according to the user's current task.

[0080] The providing unit can improve the display method by reflecting user feedback when providing information. For example, the providing unit can improve the display method by reflecting user feedback when providing information. For example, the providing unit can customize the display method based on user feedback. The providing unit can also analyze user feedback and optimize the display method. The providing unit can also improve the display method by referring to user feedback. In this way, the display method can be optimized by reflecting user feedback.

[0081] The providing unit can select the optimal display method by taking into consideration the user's device information when providing information. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, the optimal display method can be provided by taking into consideration the user's device information.

[0082] The providing unit can make the display content multilingual in accordance with the user's language setting when providing information. For example, the providing unit can make the display content multilingual in accordance with the user's language setting when providing information. For example, the providing unit can automatically set the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide information in that language. This makes it possible to provide more appropriate information by making the display content multilingual in accordance with the user's language setting.

[0083] The providing unit can customize the method of providing information by reflecting the user's past feedback when providing information. For example, the providing unit customizes the method of providing information by reflecting the user's past feedback when providing information. For example, the providing unit can customize the method of providing information based on the user's past feedback. The providing unit can also analyze the user's past feedback and optimize the method of providing information. The providing unit can also improve the method of providing information by referring to the user's past feedback. In this way, the method of providing information can be optimized by reflecting the user's past feedback.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The collection unit can collect biometric information of the user and monitor the health condition. For example, the collection unit can collect the user's heart rate and blood pressure. The collection unit can also collect the user's sleep pattern. Furthermore, the collection unit can also collect the user's exercise amount. This makes it possible to monitor the user's health condition and provide health advice as needed.

[0086] The local search unit can learn the user's past search history and provide personalized search results. For example, the local search unit can prioritize providing highly relevant information based on keywords the user has searched for in the past. The local search unit can also analyze the user's past search patterns to improve the accuracy of search results. Furthermore, the local search unit can also make predictions for new search queries by referring to the user's past search history. In this way, by learning the user's past search history, more personalized search results can be provided.

[0087] The collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information shared by the user on social media. The collection unit can also analyze the content posted by the user on social media and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0088] The local search unit can prioritize providing geographically relevant information based on the user's current location information. For example, the local search unit can prioritize providing event information related to the user's current location. The local search unit can also provide information about nearby stores and facilities based on the user's location information. Furthermore, the local search unit can also provide local news and weather information based on the user's location information. This makes it possible to provide geographically relevant information based on the user's current location information.

[0089] The collection unit can analyze the user's past behavioral patterns and select the optimal information collection method. For example, the collection unit can select the optimal information collection method based on the user's past behavioral patterns. The collection unit can also prioritize collection of information from apps that the user has frequently used in the past. Furthermore, the collection unit can concentrate information collection during specific time periods based on the user's past behavioral patterns. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavioral patterns.

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

[0091] Step 1: The collection unit collects information about the user's behavior and events. For example, the collection unit may collect the user's email sending and receiving history, calendar events, location information, photos, and messaging app history. Step 2: The vectorization unit vectorizes the information collected by the collection unit. For example, the vectorization unit converts the collected information into numerical data, making it easier to search and analyze the information. Information about specific events can also be vectorized. Step 3: The neighborhood search unit performs a neighborhood search for the user's question based on the information vectorized by the vectorization unit. For example, it searches for information that is closest to the user's question, related information, or the most relevant information. Step 4: The providing unit provides the information obtained by the local search unit to the user, for example, by displaying a response to the user's question or a search result.

[0092] (Example 2) A search system according to an embodiment of the present invention allows a user to search for events that the user has experienced in the past. When the user grants tracking permission, the search system collects and vectorizes information about the user's behavior and events, performs a proximity search in response to the user's questions, and provides the obtained information. For example, the search system collects the user's email sending and receiving history, calendar appointments, location information, photos, and messaging app history. The collected information is then vectorized and converted into numerical data. When the user inputs a question, the search system performs a proximity search based on the vectorized information to search for the most relevant information. For example, in response to the question, "Where was I and what was I doing on Christmas five years ago?", the search system searches for information related to Christmas five years ago and provides it to the user. This allows the user to easily search for and recall past events and experiences. This allows the search system to efficiently collect, vectorize, perform a proximity search, and provide information about the user's behavior and events. For example, the search system can support the user's memory, such as where important meeting materials were shared or where and what they were doing on a particular day.

[0093] A search system according to an embodiment includes a collection unit, a vectorization unit, a neighborhood search unit, and a providing unit. The collection unit collects information related to user behavior and events. For example, the collection unit can collect a user's email sending and receiving history. The collection unit can also collect calendar schedules. The collection unit can also collect location information. The collection unit can also collect photos. The collection unit can also collect messaging app history. The vectorization unit vectorizes the information collected by the collection unit. For example, the vectorization unit converts the collected information into numerical data. The vectorization unit can also vectorize the information to make it easier to search and analyze. The vectorization unit can also vectorize information related to a specific event. The neighborhood search unit performs a neighborhood search for a user's question based on the information vectorized by the vectorization unit. For example, the neighborhood search unit searches for information closest to the user's question. The neighborhood search unit can also search for information related to the user's question. The neighborhood search unit can also search for information most relevant to the user's question. The providing unit provides the information obtained by the neighborhood search unit to the user. For example, the providing unit provides a response to a user's question. The providing unit can also display search results to the user. The providing unit can also provide information to the user. This enables the search system according to the embodiment to efficiently collect, vectorize, search nearby, and provide information about user behavior and events.

[0094] The collection unit can collect information from emails, calendars, location information, photos, and messaging apps. The collection unit, for example, collects a user's email sending and receiving history. For example, the collection unit can collect email subjects and sending dates and times. The collection unit can also collect calendar appointments. For example, the collection unit can collect calendar event names and dates and times. The collection unit can also collect location information. For example, the collection unit can collect GPS data and the accuracy of location information. The collection unit can also collect photos. For example, the collection unit can collect the date, time, and location at which a photo was taken. The collection unit can also collect messaging app history. For example, the collection unit can collect the date, time, and content of messages sent and received. In this way, by collecting information from a variety of information sources, it is possible to comprehensively obtain information about the user's behavior and events.

[0095] The collection unit encrypts the collected information and can provide it to a third party only with the user's permission. The collection unit, for example, encrypts the collected information. For example, the collection unit can encrypt the information using an encryption algorithm such as AES (Advanced Encryption Standard). The collection unit can also securely manage keys for the encrypted information. The collection unit also provides the information to a third party only with the user's permission. For example, the collection unit can provide the information to an external service provider only with the user's permission. The collection unit can also provide the information to an advertising company only with the user's permission. This protects the user's privacy and ensures the safety of the information.

[0096] The vectorization unit can convert the collected information into numerical data. For example, the vectorization unit can vectorize text data using a natural language processing algorithm. The vectorization unit can also convert image data into numerical data. For example, the vectorization unit can vectorize image data using a computer vision algorithm. The vectorization unit can also convert location information into numerical data. For example, the vectorization unit can vectorize location information using a geographic information system (GIS) algorithm. By converting the information into numerical data, it becomes easier to search and analyze.

[0097] The neighborhood search unit can search for highly relevant information based on the user's question. The neighborhood search unit, for example, searches for highly relevant information based on the user's question. For example, the neighborhood search unit can search for information that is most relevant to the user's question. The neighborhood search unit can also search for information related to the user's question. The neighborhood search unit can also search for information that is closest to the user's question. This makes it possible to provide the most relevant information to the user's question.

[0098] The providing unit can provide a response to a user's question. The providing unit, for example, provides a response to a user's question. For example, the providing unit can display search results for the user's question. The providing unit can also provide information to the user. The providing unit can also provide a response to the user. This makes it possible to provide an appropriate response to the user's question.

[0099] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using an emotion estimation algorithm. The collection unit also adjusts the timing of information collection based on the estimated user emotions. For example, the collection unit can reduce the frequency of information collection when the user is stressed, thereby reducing the user's burden. The collection unit can also increase the frequency of information collection when the user is relaxed, thereby collecting more detailed data. The collection unit can also temporarily stop information collection when the user is busy and resume it later. This allows the user's burden to be reduced by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The collection unit can analyze the user's past behavioral patterns and select the optimal information collection method. The collection unit, for example, analyzes the user's past behavioral patterns. For example, the collection unit can select the optimal information collection method based on the user's past behavioral patterns. The collection unit can also prioritize collecting information from apps that the user has frequently used in the past. The collection unit can also concentrate information collection during a specific time period based on the user's past behavioral patterns. The collection unit can also prioritize collecting information related to a specific event based on the user's past behavioral patterns. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavioral patterns.

[0101] The collection unit can filter information based on the user's current activities and areas of interest when collecting information. For example, the collection unit can preferentially collect information related to events in which the user is currently participating. The collection unit can also filter and collect information related to topics in which the user has recently become interested. The collection unit can also exclude unnecessary information and collect only highly relevant information based on the user's current activities. In this way, highly relevant information can be collected by filtering information based on the user's current activities and areas of interest.

[0102] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting information. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Also, if the user is uploading images, the collection unit can prioritize collecting image data. This enables efficient information collection by selecting the optimal collection means depending on the user's input method.

[0103] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using an emotion estimation algorithm. The collection unit also determines the priority of information to be collected based on the estimated user emotions. For example, if the user is stressed, the collection unit can prioritize collecting information that helps the user relax. If the user is excited, the collection unit can prioritize collecting information that piques the user's interest. If the user is tired, the collection unit can prioritize collecting information related to rest. In this way, by determining the priority of information to be collected according to the user's emotions, more appropriate information can be collected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information when collecting information. For example, the collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information when collecting information. For example, the collection unit can prioritize collecting event information related to the user's current location. The collection unit can also prioritize collecting information about nearby stores and facilities based on the user's location information. The collection unit can also prioritize collecting local news and weather information based on the user's location information. In this way, highly relevant information can be provided by collecting information in consideration of the user's geographical location information.

[0105] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can collect information shared by the user on social media. The collection unit can also analyze the content of the user's posts on social media and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0106] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit can preferentially collect information from information sources that the user has previously rated highly. The collection unit can also adjust the type of information to be collected based on the user's past feedback. The collection unit can also optimize the collection method by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0107] The vectorization unit can estimate the user's emotion and adjust the vectorization expression method based on the estimated user's emotion. The vectorization unit, for example, estimates the user's emotion. For example, the vectorization unit can estimate the user's emotion using an emotion estimation algorithm. The vectorization unit also adjusts the vectorization expression method based on the estimated user's emotion. For example, if the user is relaxed, the vectorization unit can perform detailed vectorization to increase the accuracy of the information. If the user is in a hurry, the vectorization unit can perform simplified vectorization to quickly provide information. If the user is excited, the vectorization unit can perform visually easy-to-understand vectorization. This allows more appropriate information to be provided by adjusting the vectorization expression method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] The vectorization unit can adjust the level of detail of the vectorization based on the importance of the information during vectorization. For example, the vectorization unit can vectorize important meeting materials in detail to improve search accuracy. The vectorization unit can also simplify and vectorize everyday messages to improve processing speed. The vectorization unit can also vectorize highly important events in detail to make it easier to search for related information. In this way, adjusting the level of detail of the vectorization based on the importance of the information enables efficient information provision.

[0109] The vectorization unit can apply different vectorization algorithms depending on the category of information during vectorization. For example, the vectorization unit can vectorize email content using a natural language processing algorithm. The vectorization unit can also vectorize image data using a computer vision algorithm. The vectorization unit can also vectorize location information using a geographic information system (GIS) algorithm. By applying different vectorization algorithms depending on the category of information, it is possible to provide highly accurate information.

[0110] The vectorization unit can improve the accuracy of vectorization by referring to the user's past vectorization results during vectorization. For example, the vectorization unit can improve the accuracy of vectorization by referring to the user's past vectorization results during vectorization. For example, the vectorization unit can improve the accuracy when vectorizing similar information based on the user's past vectorization results. The vectorization unit can also analyze the user's past vectorization results and adjust algorithm parameters. The vectorization unit can also optimize the vectorization method by referring to the user's past vectorization results. In this way, the accuracy of vectorization is improved by referring to the user's past vectorization results.

[0111] The vectorization unit can estimate the user's emotion and adjust the length of the vectorization based on the estimated user's emotion. The vectorization unit, for example, estimates the user's emotion. For example, the vectorization unit can estimate the user's emotion using an emotion estimation algorithm. The vectorization unit also adjusts the length of the vectorization based on the estimated user's emotion. For example, if the user is relaxed, the vectorization unit can generate a longer vector to retain more detailed information. If the user is in a hurry, the vectorization unit can generate a shorter vector to quickly provide information. If the user is excited, the vectorization unit can generate a vector with a length that is visually easy to understand. This allows for more appropriate information to be provided by adjusting the length of the vectorization according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0112] The vectorization unit can determine the priority of vectorization based on the time when information was collected during vectorization. The vectorization unit, for example, determines the priority of vectorization based on the time when information was collected during vectorization. For example, the vectorization unit can vectorize recently collected information preferentially to provide the latest information. The vectorization unit can also vectorize information of important events preferentially to make it easier to search for related information. The vectorization unit can also simplify and vectorize old information to improve processing speed. In this way, by determining the priority of vectorization based on the time when information was collected, the latest information can be preferentially provided.

[0113] The vectorization unit can adjust the order of vectorization based on the relevance of information during vectorization. For example, the vectorization unit can adjust the order of vectorization based on the relevance of information during vectorization. For example, the vectorization unit can preferentially vectorize information that is highly relevant to a user's question. The vectorization unit can also preferentially vectorize information related to an important event. The vectorization unit can also preferentially vectorize information that is highly relevant based on the user's past search history. In this way, by adjusting the order of vectorization based on the relevance of information, highly relevant information can be preferentially provided.

[0114] The vectorization unit can adjust the use of technical terms for vectorization according to the user's level of expertise during vectorization. For example, the vectorization unit can adjust the use of technical terms for vectorization according to the user's level of expertise during vectorization. For example, if the user has technical knowledge, the vectorization unit can vectorize using technical terms. Also, if the user has general knowledge, the vectorization unit can vectorize using simple terms. Also, the vectorization unit can vectorize using appropriate terms based on the user's past search history. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide information that is easier to understand.

[0115] The neighborhood search unit can estimate the user's emotion and adjust the criteria for the neighborhood search based on the estimated user's emotion. The neighborhood search unit, for example, estimates the user's emotion. For example, the neighborhood search unit can estimate the user's emotion using an emotion estimation algorithm. The neighborhood search unit also adjusts the criteria for the neighborhood search based on the estimated user's emotion. For example, if the user is relaxed, the neighborhood search unit can perform a neighborhood search that includes detailed information. If the user is in a hurry, the neighborhood search unit can perform a simplified neighborhood search to quickly provide information. If the user is excited, the neighborhood search unit can perform a visually easy-to-understand neighborhood search. This allows for more appropriate information to be provided by adjusting the criteria for the neighborhood search according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] The neighborhood search unit can improve the accuracy of the search by taking into account the interrelationships of information during neighborhood search. For example, the neighborhood search unit can improve the accuracy of the search by taking into account the interrelationships of information during neighborhood search. For example, the neighborhood search unit can perform a highly accurate neighborhood search based on information on related events and actions. The neighborhood search unit can also analyze the interrelationships of information and preferentially search for highly related information. The neighborhood search unit can also perform a highly accurate search by taking into account the interrelationships of information and excluding unnecessary information. In this way, by taking into account the interrelationships of information, a highly accurate neighborhood search is possible.

[0117] The neighborhood search unit can perform a neighborhood search while taking into account attribute information of the information provider. For example, the neighborhood search unit can perform a neighborhood search while taking into account attribute information of the information provider. For example, if the information provider is trustworthy, the neighborhood search unit can search for that information preferentially. The neighborhood search unit can also search for highly relevant information while taking into account the specialized knowledge of the information provider. The neighborhood search unit can also search for highly reliable information preferentially based on the information provider's past provision history. In this way, highly reliable information can be provided by taking into account the attribute information of the information provider.

[0118] The neighborhood search unit can weight the search based on the frequency of information provision during the neighborhood search. For example, the neighborhood search unit can weight the search based on the frequency of information provision during the neighborhood search. For example, the neighborhood search unit can prioritize searching for information that is provided more frequently and provide highly relevant information. The neighborhood search unit can also lower the weight of information that is provided less frequently and exclude it from the search results. The neighborhood search unit can also evaluate the reliability of information based on the frequency of information provision and improve the accuracy of the search. As a result, by weighting the search based on the frequency of information provision, highly relevant information can be provided preferentially.

[0119] The neighborhood search unit can estimate a user's emotion and adjust the order in which the neighborhood search results are displayed based on the estimated user's emotion. The neighborhood search unit, for example, estimates the user's emotion. For example, the neighborhood search unit can estimate the user's emotion using an emotion estimation algorithm. The neighborhood search unit also adjusts the order in which the neighborhood search results are displayed based on the estimated user's emotion. For example, the neighborhood search unit can display detailed information at the top if the user is relaxed. The neighborhood search unit can also display simplified information at the top if the user is in a hurry. The neighborhood search unit can also display visually easy-to-understand information at the top if the user is excited. This allows for more appropriate information to be provided by adjusting the order in which the neighborhood search results are displayed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0120] The neighborhood search unit can perform a search taking into account the geographical distribution of information during a neighborhood search. For example, the neighborhood search unit can perform a search taking into account the geographical distribution of information during a neighborhood search. For example, the neighborhood search unit can prioritize searching for information close to the user's current location. The neighborhood search unit can also prioritize searching for information that is highly geographically relevant. The neighborhood search unit can also search for highly relevant information based on the user's past location information. In this way, highly relevant information can be provided by taking into account the geographical distribution of information.

[0121] The neighborhood search unit can improve the accuracy of the search during a neighborhood search by referring to literature related to the information. For example, the neighborhood search unit can improve the accuracy of the search by referring to literature related to the information during a neighborhood search. For example, the neighborhood search unit can perform a highly accurate neighborhood search by referring to related academic papers. The neighborhood search unit can also search for highly relevant information by referring to related patent documents. The neighborhood search unit can also improve the accuracy of the search by referring to related technical literature and excluding unnecessary information. As a result, by referring to literature related to the information, a highly accurate neighborhood search is possible.

[0122] The neighborhood search unit can perform a neighborhood search while taking into account the market value of the information. For example, the neighborhood search unit can perform a neighborhood search while taking into account the market value of the information. For example, the neighborhood search unit can prioritize searching for information with high market value and provide highly relevant information. The neighborhood search unit can also lower the weight of information with low market value and exclude it from the search results. The neighborhood search unit can also evaluate the reliability of information based on market value and improve the accuracy of the search. In this way, highly relevant information can be provided by taking into account the market value of the information.

[0123] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit can estimate the user's emotion using an emotion estimation algorithm. The providing unit also adjusts the display method of the information to be provided based on the estimated user's emotion. For example, the providing unit can display detailed information when the user is relaxed. The providing unit can also display simplified information when the user is in a hurry. The providing unit can also display visually easy-to-understand information when the user is excited. This allows more appropriate information to be provided by adjusting the display method of information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0124] The providing unit can select the optimal display method by referring to the user's past operation history when providing information. For example, the providing unit can select the optimal display method by referring to the user's past operation history when providing information. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. The providing unit can also analyze the user's past operation history and customize the display method. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0125] The providing unit can customize the display content according to the user's current task when providing information. For example, the providing unit can customize the display content according to the user's current task when providing information. For example, the providing unit can prioritize displaying information related to the task the user is currently performing. The providing unit can also exclude unnecessary information and display highly relevant information based on the user's current task. The providing unit can also analyze the user's current task and suggest optimal display content. In this way, highly relevant information can be provided by customizing the display content according to the user's current task.

[0126] The providing unit can improve the display method by reflecting user feedback when providing information. For example, the providing unit can improve the display method by reflecting user feedback when providing information. For example, the providing unit can customize the display method based on user feedback. The providing unit can also analyze user feedback and optimize the display method. The providing unit can also improve the display method by referring to user feedback. In this way, the display method can be optimized by reflecting user feedback.

[0127] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit can estimate the user's emotion using an emotion estimation algorithm. The providing unit also determines the priority of information to be provided based on the estimated user's emotion. For example, the providing unit can prioritize detailed information when the user is relaxed. The providing unit can also prioritize simplified information when the user is in a hurry. The providing unit can also prioritize visually easy-to-understand information when the user is excited. This allows more appropriate information to be provided by determining the priority of information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0128] The providing unit can select the optimal display method by taking into consideration the user's device information when providing information. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, the optimal display method can be provided by taking into consideration the user's device information.

[0129] The providing unit can make the display content multilingual in accordance with the user's language setting when providing information. For example, the providing unit can make the display content multilingual in accordance with the user's language setting when providing information. For example, the providing unit can automatically set the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide information in that language. This makes it possible to provide more appropriate information by making the display content multilingual in accordance with the user's language setting.

[0130] The providing unit can customize the method of providing information by reflecting the user's past feedback when providing information. For example, the providing unit customizes the method of providing information by reflecting the user's past feedback when providing information. For example, the providing unit can customize the method of providing information based on the user's past feedback. The providing unit can also analyze the user's past feedback and optimize the method of providing information. The providing unit can also improve the method of providing information by referring to the user's past feedback. In this way, the method of providing information can be optimized by reflecting the user's past feedback.

[0131] The neighborhood search unit learns the history of past questions and answers and can provide more accurate answers. The neighborhood search unit, for example, learns the history of past questions and answers. For example, the neighborhood search unit can provide more accurate answers based on the history of past questions and answers. The neighborhood search unit can also analyze the history of past questions and answers and adjust the parameters of the algorithm. The neighborhood search unit can also improve the accuracy of the answers by referring to the history of past questions and answers. In this way, the accuracy of the answers is improved by learning the history of past questions and answers.

[0132] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using an emotion estimation algorithm. The collection unit also adjusts the timing of information collection based on the estimated user emotions. For example, the collection unit can reduce the frequency of information collection when the user is stressed, thereby reducing the user's burden. The collection unit can also increase the frequency of information collection when the user is relaxed, thereby collecting more detailed data. The collection unit can also temporarily stop information collection when the user is busy and resume it later. This allows the user's burden to be reduced by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0133] The collection unit can analyze the user's past behavioral patterns and select the optimal information collection method. The collection unit, for example, analyzes the user's past behavioral patterns. For example, the collection unit can select the optimal information collection method based on the user's past behavioral patterns. The collection unit can also prioritize collecting information from apps that the user has frequently used in the past. The collection unit can also concentrate information collection during a specific time period based on the user's past behavioral patterns. The collection unit can also prioritize collecting information related to a specific event based on the user's past behavioral patterns. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavioral patterns.

[0134] The collection unit can filter information based on the user's current activities and areas of interest when collecting information. For example, the collection unit can preferentially collect information related to events in which the user is currently participating. The collection unit can also filter and collect information related to topics in which the user has recently become interested. The collection unit can also exclude unnecessary information and collect only highly relevant information based on the user's current activities. In this way, highly relevant information can be collected by filtering information based on the user's current activities and areas of interest.

[0135] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting information. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Also, if the user is uploading images, the collection unit can prioritize collecting image data. This enables efficient information collection by selecting the optimal collection means depending on the user's input method.

[0136] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using an emotion estimation algorithm. The collection unit also determines the priority of information to be collected based on the estimated user emotions. For example, if the user is stressed, the collection unit can prioritize collecting information that helps the user relax. If the user is excited, the collection unit can prioritize collecting information that piques the user's interest. If the user is tired, the collection unit can prioritize collecting information related to rest. In this way, by determining the priority of information to be collected according to the user's emotions, more appropriate information can be collected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0137] The collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information when collecting information. For example, the collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information when collecting information. For example, the collection unit can prioritize collecting event information related to the user's current location. The collection unit can also prioritize collecting information about nearby stores and facilities based on the user's location information. The collection unit can also prioritize collecting local news and weather information based on the user's location information. In this way, highly relevant information can be provided by collecting information in consideration of the user's geographical location information.

[0138] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can collect information shared by the user on social media. The collection unit can also analyze the content of the user's posts on social media and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0139] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit can preferentially collect information from information sources that the user has previously rated highly. The collection unit can also adjust the type of information to be collected based on the user's past feedback. The collection unit can also optimize the collection method by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0140] The vectorization unit can estimate the user's emotion and adjust the vectorization expression method based on the estimated user's emotion. The vectorization unit, for example, estimates the user's emotion. For example, the vectorization unit can estimate the user's emotion using an emotion estimation algorithm. The vectorization unit also adjusts the vectorization expression method based on the estimated user's emotion. For example, if the user is relaxed, the vectorization unit can perform detailed vectorization to increase the accuracy of the information. If the user is in a hurry, the vectorization unit can perform simplified vectorization to quickly provide information. If the user is excited, the vectorization unit can perform visually easy-to-understand vectorization. This allows more appropriate information to be provided by adjusting the vectorization expression method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0141] The vectorization unit can adjust the level of detail of the vectorization based on the importance of the information during vectorization. For example, the vectorization unit can vectorize important meeting materials in detail to improve search accuracy. The vectorization unit can also simplify and vectorize everyday messages to improve processing speed. The vectorization unit can also vectorize highly important events in detail to make it easier to search for related information. In this way, adjusting the level of detail of the vectorization based on the importance of the information enables efficient information provision.

[0142] The vectorization unit can apply different vectorization algorithms depending on the category of information during vectorization. For example, the vectorization unit can vectorize email content using a natural language processing algorithm. The vectorization unit can also vectorize image data using a computer vision algorithm. The vectorization unit can also vectorize location information using a geographic information system (GIS) algorithm. By applying different vectorization algorithms depending on the category of information, it is possible to provide highly accurate information.

[0143] The vectorization unit can improve the accuracy of vectorization by referring to the user's past vectorization results during vectorization. For example, the vectorization unit can improve the accuracy of vectorization by referring to the user's past vectorization results during vectorization. For example, the vectorization unit can improve the accuracy when vectorizing similar information based on the user's past vectorization results. The vectorization unit can also analyze the user's past vectorization results and adjust algorithm parameters. The vectorization unit can also optimize the vectorization method by referring to the user's past vectorization results. In this way, the accuracy of vectorization is improved by referring to the user's past vectorization results.

[0144] The vectorization unit can estimate the user's emotion and adjust the length of the vectorization based on the estimated user's emotion. The vectorization unit, for example, estimates the user's emotion. For example, the vectorization unit can estimate the user's emotion using an emotion estimation algorithm. The vectorization unit also adjusts the length of the vectorization based on the estimated user's emotion. For example, if the user is relaxed, the vectorization unit can generate a longer vector to retain more detailed information. If the user is in a hurry, the vectorization unit can generate a shorter vector to quickly provide information. If the user is excited, the vectorization unit can generate a vector with a length that is visually easy to understand. This allows for more appropriate information to be provided by adjusting the length of the vectorization according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0145] The vectorization unit can determine the priority of vectorization based on the time when information was collected during vectorization. The vectorization unit, for example, determines the priority of vectorization based on the time when information was collected during vectorization. For example, the vectorization unit can vectorize recently collected information preferentially to provide the latest information. The vectorization unit can also vectorize information of important events preferentially to make it easier to search for related information. The vectorization unit can also simplify and vectorize old information to improve processing speed. In this way, by determining the priority of vectorization based on the time when information was collected, the latest information can be preferentially provided.

[0146] The vectorization unit can adjust the order of vectorization based on the relevance of information during vectorization. For example, the vectorization unit can adjust the order of vectorization based on the relevance of information during vectorization. For example, the vectorization unit can preferentially vectorize information that is highly relevant to a user's question. The vectorization unit can also preferentially vectorize information related to an important event. The vectorization unit can also preferentially vectorize information that is highly relevant based on the user's past search history. In this way, by adjusting the order of vectorization based on the relevance of information, highly relevant information can be preferentially provided.

[0147] The vectorization unit can adjust the use of technical terms for vectorization according to the user's level of expertise during vectorization. For example, the vectorization unit can adjust the use of technical terms for vectorization according to the user's level of expertise during vectorization. For example, if the user has technical knowledge, the vectorization unit can vectorize using technical terms. Also, if the user has general knowledge, the vectorization unit can vectorize using simple terms. Also, the vectorization unit can vectorize using appropriate terms based on the user's past search history. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide information that is easier to understand.

[0148] The neighborhood search unit can estimate the user's emotion and adjust the criteria for the neighborhood search based on the estimated user's emotion. The neighborhood search unit, for example, estimates the user's emotion. For example, the neighborhood search unit can estimate the user's emotion using an emotion estimation algorithm. The neighborhood search unit also adjusts the criteria for the neighborhood search based on the estimated user's emotion. For example, if the user is relaxed, the neighborhood search unit can perform a neighborhood search that includes detailed information. If the user is in a hurry, the neighborhood search unit can perform a simplified neighborhood search to quickly provide information. If the user is excited, the neighborhood search unit can perform a visually easy-to-understand neighborhood search. This allows for more appropriate information to be provided by adjusting the criteria for the neighborhood search according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0149] The neighborhood search unit can improve the accuracy of the search by taking into account the interrelationships of information during neighborhood search. For example, the neighborhood search unit can improve the accuracy of the search by taking into account the interrelationships of information during neighborhood search. For example, the neighborhood search unit can perform a highly accurate neighborhood search based on information on related events and actions. The neighborhood search unit can also analyze the interrelationships of information and preferentially search for highly related information. The neighborhood search unit can also perform a highly accurate search by taking into account the interrelationships of information and excluding unnecessary information. In this way, by taking into account the interrelationships of information, a highly accurate neighborhood search is possible.

[0150] The neighborhood search unit can perform a neighborhood search while taking into account attribute information of the information provider. For example, the neighborhood search unit can perform a neighborhood search while taking into account attribute information of the information provider. For example, if the information provider is trustworthy, the neighborhood search unit can search for that information preferentially. The neighborhood search unit can also search for highly relevant information while taking into account the specialized knowledge of the information provider. The neighborhood search unit can also search for highly reliable information preferentially based on the information provider's past provision history. In this way, highly reliable information can be provided by taking into account the attribute information of the information provider.

[0151] The neighborhood search unit can weight the search based on the frequency of information provision during the neighborhood search. For example, the neighborhood search unit can weight the search based on the frequency of information provision during the neighborhood search. For example, the neighborhood search unit can prioritize searching for information that is provided more frequently and provide highly relevant information. The neighborhood search unit can also lower the weight of information that is provided less frequently and exclude it from the search results. The neighborhood search unit can also evaluate the reliability of information based on the frequency of information provision and improve the accuracy of the search. As a result, by weighting the search based on the frequency of information provision, highly relevant information can be provided preferentially.

[0152] The neighborhood search unit can estimate a user's emotion and adjust the order in which the neighborhood search results are displayed based on the estimated user's emotion. The neighborhood search unit, for example, estimates the user's emotion. For example, the neighborhood search unit can estimate the user's emotion using an emotion estimation algorithm. The neighborhood search unit also adjusts the order in which the neighborhood search results are displayed based on the estimated user's emotion. For example, the neighborhood search unit can display detailed information at the top if the user is relaxed. The neighborhood search unit can also display simplified information at the top if the user is in a hurry. The neighborhood search unit can also display visually easy-to-understand information at the top if the user is excited. This allows for more appropriate information to be provided by adjusting the order in which the neighborhood search results are displayed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0153] The neighborhood search unit can perform a search taking into account the geographical distribution of information during a neighborhood search. For example, the neighborhood search unit can perform a search taking into account the geographical distribution of information during a neighborhood search. For example, the neighborhood search unit can prioritize searching for information close to the user's current location. The neighborhood search unit can also prioritize searching for information that is highly geographically relevant. The neighborhood search unit can also search for highly relevant information based on the user's past location information. In this way, highly relevant information can be provided by taking into account the geographical distribution of information.

[0154] The neighborhood search unit can improve the accuracy of the search during a neighborhood search by referring to literature related to the information. For example, the neighborhood search unit can improve the accuracy of the search by referring to literature related to the information during a neighborhood search. For example, the neighborhood search unit can perform a highly accurate neighborhood search by referring to related academic papers. The neighborhood search unit can also search for highly relevant information by referring to related patent documents. The neighborhood search unit can also improve the accuracy of the search by referring to related technical literature and excluding unnecessary information. As a result, by referring to literature related to the information, a highly accurate neighborhood search is possible.

[0155] The neighborhood search unit can perform a neighborhood search while taking into account the market value of the information. For example, the neighborhood search unit can perform a neighborhood search while taking into account the market value of the information. For example, the neighborhood search unit can prioritize searching for information with high market value and provide highly relevant information. The neighborhood search unit can also lower the weight of information with low market value and exclude it from the search results. The neighborhood search unit can also evaluate the reliability of information based on market value and improve the accuracy of the search. In this way, highly relevant information can be provided by taking into account the market value of the information.

[0156] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit can estimate the user's emotion using an emotion estimation algorithm. The providing unit also adjusts the display method of the information to be provided based on the estimated user's emotion. For example, the providing unit can display detailed information when the user is relaxed. The providing unit can also display simplified information when the user is in a hurry. The providing unit can also display visually easy-to-understand information when the user is excited. This allows more appropriate information to be provided by adjusting the display method of information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0157] The providing unit can select the optimal display method by referring to the user's past operation history when providing information. For example, the providing unit can select the optimal display method by referring to the user's past operation history when providing information. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. The providing unit can also analyze the user's past operation history and customize the display method. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0158] The providing unit can customize the display content according to the user's current task when providing information. For example, the providing unit can customize the display content according to the user's current task when providing information. For example, the providing unit can prioritize displaying information related to the task the user is currently performing. The providing unit can also exclude unnecessary information and display highly relevant information based on the user's current task. The providing unit can also analyze the user's current task and suggest optimal display content. In this way, highly relevant information can be provided by customizing the display content according to the user's current task.

[0159] The providing unit can improve the display method by reflecting user feedback when providing information. For example, the providing unit can improve the display method by reflecting user feedback when providing information. For example, the providing unit can customize the display method based on user feedback. The providing unit can also analyze user feedback and optimize the display method. The providing unit can also improve the display method by referring to user feedback. In this way, the display method can be optimized by reflecting user feedback.

[0160] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit can estimate the user's emotion using an emotion estimation algorithm. The providing unit also determines the priority of information to be provided based on the estimated user's emotion. For example, the providing unit can prioritize detailed information when the user is relaxed. The providing unit can also prioritize simplified information when the user is in a hurry. The providing unit can also prioritize visually easy-to-understand information when the user is excited. This allows more appropriate information to be provided by determining the priority of information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0161] The providing unit can select the optimal display method by taking into consideration the user's device information when providing information. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, the optimal display method can be provided by taking into consideration the user's device information.

[0162] The providing unit can make the display content multilingual in accordance with the user's language setting when providing information. For example, the providing unit can make the display content multilingual in accordance with the user's language setting when providing information. For example, the providing unit can automatically set the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide information in that language. This makes it possible to provide more appropriate information by making the display content multilingual in accordance with the user's language setting.

[0163] The providing unit can customize the method of providing information by reflecting the user's past feedback when providing information. For example, the providing unit customizes the method of providing information by reflecting the user's past feedback when providing information. For example, the providing unit can customize the method of providing information based on the user's past feedback. The providing unit can also analyze the user's past feedback and optimize the method of providing information. The providing unit can also improve the method of providing information by referring to the user's past feedback. In this way, the method of providing information can be optimized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, vectorization unit, neighborhood search unit, and providing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on user behavior and events using the camera 42 and microphone 38B of the smart device 14. The vectorization unit, for example, vectorizes the information collected by the specific processing unit 290 of the data processing device 12 and converts it into numerical data. The neighborhood search unit, for example, performs neighborhood search in response to a user's question based on the information vectorized by the specific processing unit 290 of the data processing device 12. The providing unit provides search results to the user via, for example, the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, vectorization unit, neighborhood search unit, and providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information on user behavior and events using the camera 42 and microphone 238 of the smart glasses 214. The vectorization unit, for example, vectorizes the information collected by the specific processing unit 290 of the data processing device 12 and converts it into numerical data. The neighborhood search unit, for example, performs neighborhood search in response to a user's question based on the information vectorized by the specific processing unit 290 of the data processing device 12. The providing unit provides search results to the user, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, vectorization unit, neighborhood search unit, and providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects information on user behavior and events using the camera 42 and microphone 238 of the headset type terminal 314. The vectorization unit, for example, vectorizes the information collected by the specific processing unit 290 of the data processing device 12 and converts it into numerical data. The neighborhood search unit, for example, performs a neighborhood search in response to a user's question based on the information vectorized by the specific processing unit 290 of the data processing device 12. The providing unit provides the search results to the user by, for example, the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, vectorization unit, neighborhood search unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on user behavior and events using the camera 42 and microphone 238 of the robot 414. The vectorization unit, for example, vectorizes the information collected by the specific processing unit 290 of the data processing device 12 and converts it into numerical data. The neighborhood search unit, for example, performs a neighborhood search in response to a user's question based on the information vectorized by the specific processing unit 290 of the data processing device 12. The providing unit provides the search results to the user by, for example, the control unit 46A of the robot 414.

[0164] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0165] The collection unit can collect biometric information of the user and monitor the health condition. For example, the collection unit can collect the user's heart rate and blood pressure. The collection unit can also collect the user's sleep pattern. Furthermore, the collection unit can also collect the user's exercise amount. This makes it possible to monitor the user's health condition and provide health advice as needed.

[0166] The vectorization unit can estimate the user's emotions and select a vectorization algorithm based on the estimated user's emotions. For example, if the user is feeling stressed, vectorization can be performed using an algorithm that takes emotions into consideration. Also, if the user is relaxed, detailed vectorization can be performed to increase the accuracy of the information. Furthermore, if the user is excited, vectorization that is visually easy to understand can be performed. In this way, by selecting a vectorization algorithm according to the user's emotions, more appropriate information can be provided.

[0167] The local search unit can learn the user's past search history and provide personalized search results. For example, the local search unit can prioritize providing highly relevant information based on keywords the user has searched for in the past. The local search unit can also analyze the user's past search patterns to improve the accuracy of search results. Furthermore, the local search unit can also make predictions for new search queries by referring to the user's past search history. In this way, by learning the user's past search history, more personalized search results can be provided.

[0168] The providing unit can estimate the user's emotion and customize the display format of information based on the estimated user's emotion. For example, if the user is relaxed, detailed information can be displayed. If the user is in a hurry, simplified information can be displayed. Furthermore, if the user is excited, information can be displayed in a visually easy-to-understand format. In this way, by customizing the display format of information according to the user's emotion, more appropriate information can be provided.

[0169] The collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information shared by the user on social media. The collection unit can also analyze the content posted by the user on social media and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0170] The vectorization unit can estimate the user's emotions and adjust the accuracy of vectorization based on the estimated user's emotions. For example, if the user is relaxed, detailed vectorization can be performed to increase the accuracy of information. If the user is in a hurry, simplified vectorization can be performed to quickly provide information. Furthermore, if the user is excited, visually easy-to-understand vectorization can be performed. In this way, by adjusting the accuracy of vectorization according to the user's emotions, more appropriate information can be provided.

[0171] The local search unit can prioritize providing geographically relevant information based on the user's current location information. For example, the local search unit can prioritize providing event information related to the user's current location. The local search unit can also provide information about nearby stores and facilities based on the user's location information. Furthermore, the local search unit can also provide local news and weather information based on the user's location information. This makes it possible to provide geographically relevant information based on the user's current location information.

[0172] The providing unit can estimate the user's emotions and determine the priority of information based on the estimated user's emotions. For example, if the user is relaxed, detailed information can be provided preferentially. If the user is in a hurry, simplified information can be provided preferentially. Furthermore, if the user is excited, information that is visually easy to understand can be provided preferentially. In this way, by determining the priority of information according to the user's emotions, more appropriate information can be provided.

[0173] The collection unit can analyze the user's past behavioral patterns and select the optimal information collection method. For example, the collection unit can select the optimal information collection method based on the user's past behavioral patterns. The collection unit can also prioritize collection of information from apps that the user has frequently used in the past. Furthermore, the collection unit can concentrate information collection during specific time periods based on the user's past behavioral patterns. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavioral patterns.

[0174] The local search unit can estimate the user's emotions and adjust the order in which the local search results are displayed based on the estimated user's emotions. For example, if the user is relaxed, detailed information can be displayed at the top. If the user is in a hurry, simplified information can be displayed at the top. Furthermore, if the user is excited, information that is visually easy to understand can be displayed at the top. In this way, by adjusting the order in which the local search results are displayed according to the user's emotions, more appropriate information can be provided.

[0175] The processing flow of the second embodiment will be briefly explained below.

[0176] Step 1: The collection unit collects information about the user's behavior and events. For example, the collection unit may collect the user's email sending and receiving history, calendar events, location information, photos, and messaging app history. Step 2: The vectorization unit vectorizes the information collected by the collection unit. For example, the vectorization unit converts the collected information into numerical data, making it easier to search and analyze the information. Information about specific events can also be vectorized. Step 3: The neighborhood search unit performs a neighborhood search for the user's question based on the information vectorized by the vectorization unit. For example, it searches for information that is closest to the user's question, related information, or the most relevant information. Step 4: The providing unit provides the information obtained by the local search unit to the user, for example, by displaying a response to the user's question or a search result.

[0177] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0182] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0183] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0184] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0185] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0186] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0187] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0188] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0189] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0190] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0191] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0192] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0193] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0194] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0195] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0196] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0197] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0198] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0199] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0200] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0201] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0202] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0203] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0204] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0205] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0206] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0207] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0208] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0209] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0210] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0211] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0212] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0213] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0214] 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.

[0215] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0216] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0217] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0218] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0219] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0220] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0221] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0222] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0224] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0225] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0226] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0227] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0228] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0229] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0230] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0231] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0232] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0233] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0234] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0235] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0236] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0237] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0238] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0239] 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.

[0240] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0241] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0242] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0243] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0244] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0245] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0246] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0247] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0248] [Explanation of symbols]

[0249] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information about user behavior and events; a vectorization unit that vectorizes the information collected by the collection unit; a neighborhood search unit that performs a neighborhood search for a user's question based on the information vectorized by the vectorization unit; a providing unit that provides the information obtained by the neighborhood searching unit to a user. A system characterized by:

2. The collecting unit Collect information from email, calendar, location, photos, and messaging apps 2. The system of claim 1.

3. The collecting unit Collected information is encrypted and provided to third parties only with the user's permission.

2. The system of claim 1.

4. The vectorization unit Converting collected information into numerical data 2. The system of claim 1.

5. The neighborhood search unit Find relevant information based on your questions 2. The system of claim 1.

6. The providing unit Providing answers to user questions 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze users' past behavioral patterns and select the most appropriate information collection method 2. The system of claim 1.

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