System

Abstract

The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.SOLUTION: A system includes a reception part, an analysis part, a retrieval part, and a provision part. The reception unit receives an image from a user. The analyzer analyzes the image received by the receiver and extracts a feature of the member. The retrieval unit retrieves information from the database based on the feature extracted by the analysis unit. The providing unit provides a search result obtained by the search unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to identify components when searching for base stations, making it difficult for new employees or transferred employees with little knowledge to become immediately productive.

[0005] The system according to the embodiment aims to quickly search for and provide information about components based on images. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, and a provision unit. The reception unit receives an image from a user. The analysis unit analyzes the image received by the reception unit and extracts features of the component. The search unit searches for information from a database based on the features extracted by the analysis unit. The provision unit provides the search results obtained by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly search for and provide information about components based on images. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The search system according to an embodiment of the present invention analyzes the characteristics of components and searches for and provides related information simply by uploading an image. The search system allows users to upload an image of the component they are searching for to an app, where a generation AI analyzes the image and extracts the component's characteristics. Based on the extracted characteristics, the generation AI searches a database for relevant product names and related information and provides the search results to the user. This system allows even new employees and transferees with limited knowledge to contribute immediately to the company. For example, the search system allows users to take a photo of a radio, antenna, or concrete pole with a smartphone and upload it to the app. This image is then input into the generation AI. The generation AI then analyzes the input image, extracts image features, and identifies information such as the component's type, shape, and color. Based on the extracted features, the generation AI searches the database for the corresponding product name and related information. For example, based on the identified antenna shape and color, the database is searched for information such as the corresponding product name, manufacturer, and specifications. The search results, including the product name and related information, are displayed on the app screen. Using this information, users can identify the necessary components and proceed with their work. This allows the search system to quickly and accurately identify components, improving work efficiency and overall productivity. This allows the search system to quickly and accurately identify components, improving work efficiency and overall productivity.

[0029] A search system according to an embodiment includes a receiving unit, an analysis unit, a search unit, and a providing unit. The receiving unit receives an image from a user. The image from the user may be in a format such as, but not limited to, JPEG, PNG, or GIF. The receiving unit may receive, for example, an image taken with a smartphone. The receiving unit may also receive an image taken with a digital camera. The receiving unit may also receive an image scanned with a scanner. For example, the receiving unit may receive an image in JPEG format and transmit the image to the analysis unit. The analysis unit may use a generative AI to analyze the image received by the receiving unit and extract features of the components. Features include, but are not limited to, edge detection, color histograms, and shape analysis. For example, the analysis unit may use edge detection technology to extract the contours of the image. The analysis unit may also use a color histogram to analyze the color distribution of the image. The analysis unit may also use shape analysis technology to identify the shape of the image. For example, the analysis unit may use edge detection technology to identify the shape of an antenna and a color histogram to identify the color of the antenna. The search unit searches for information from the database based on the features extracted by the analysis unit. Examples of searches include, but are not limited to, keyword search and similar image search. For example, the search unit searches the database for a relevant product name using a keyword search. The search unit can also search for related information from the database using a similar image search. The search unit can also search for product specification information from the database based on the extracted features. For example, the search unit searches the database for a relevant product name and manufacturer based on the shape and color of the antenna. The providing unit provides the search results obtained by the search unit to the user. Examples of providing include, but are not limited to, displaying text, displaying images, providing links, etc. For example, the providing unit displays the search results as text. The providing unit can also display the search results as images. The providing unit can also provide links related to the search results. For example, the providing unit displays the product name and manufacturer as text and the product specification information as images.As a result, the search system according to the embodiment can analyze the characteristics of components and search for and provide related information simply by the user uploading an image, allowing the user to quickly obtain the information they need.

[0030] The system includes an update unit that updates the learning data of the generation AI. The update unit updates the learning data of the generation AI. Examples of updates include, but are not limited to, periodic updates and updates based on user feedback. For example, the update unit periodically updates the learning data of the generation AI. The update unit can also update the learning data of the generation AI based on user feedback. The update unit can also update the learning data of the generation AI based on information from a new data source. For example, the update unit periodically updates the learning data of the generation AI to reflect the latest information. In this way, by updating the learning data of the generation AI, analysis accuracy can be improved.

[0031] The system includes a management unit that manages the database. The management unit manages the database. Examples of management include, but are not limited to, data backup, access control, and data consistency check. For example, the management unit periodically backs up the database. The management unit can also perform access control to prevent unauthorized access to the database. The management unit can also perform data consistency checks to maintain data accuracy. For example, the management unit periodically backs up the database to prevent data loss. In this way, by managing the database, the accuracy and reliability of information can be maintained.

[0032] The device includes a guide unit that guides a user through an operation procedure. The guide unit guides the user through the operation procedure. Examples of guides include, but are not limited to, step-by-step instructions, video tutorials, tooltips, and the like. For example, the guide unit displays step-by-step instructions to make it easier for the user to understand the operation procedure. The guide unit can also provide a video tutorial to visually explain the operation procedure. The guide unit can also display tooltips to explain key points of the operation procedure. For example, the guide unit displays step-by-step instructions to make it easier for the user to understand the operation procedure. This can improve the efficiency and accuracy of operations by guiding the user through the operation procedure.

[0033] The analysis unit can extract image features and identify information such as the type, shape, and color of components. The analysis unit extracts the contours of the image using, for example, edge detection technology. For example, the analysis unit identifies the shape of an antenna using edge detection technology. The analysis unit can also analyze the color distribution of the image using a color histogram. For example, the analysis unit identifies the color of an antenna using a color histogram. The analysis unit can also identify the shape of the image using shape analysis technology. For example, the analysis unit identifies the shape of a radio using shape analysis technology. In this way, by extracting image features and identifying information such as the type, shape, and color of components, more accurate search results can be provided.

[0034] The search unit can search the database for the corresponding product name and related information based on the extracted features. The search unit can, for example, use a keyword search to search for the corresponding product name from the database. For example, the search unit can use a keyword search to search for the product name of an antenna. The search unit can also search for related information from the database using a similar image search. For example, the search unit can use a similar image search to search for related information about a radio. The search unit can also search for product specification information from the database based on the extracted features. For example, the search unit can search for the corresponding product name and manufacturer from the database based on the shape and color of the antenna. This makes it possible to quickly provide the user with the information they need by searching the database for the corresponding product name and related information based on the extracted features.

[0035] The providing unit can provide the search results to the user. The providing unit, for example, displays the search results as text. For example, the providing unit displays the search results as text and provides them to the user. The providing unit can also display the search results as images. For example, the providing unit displays the search results as images and provides them to the user. The providing unit can also provide links related to the search results. For example, the providing unit provides links related to the search results and provides them to the user. In this way, by providing the search results to the user, the user can quickly obtain the information he or she needs.

[0036] The reception unit can analyze the user's past image upload history and select the optimal reception method. The reception unit can, for example, suggest the optimal reception method based on the types of images the user has frequently uploaded in the past. For example, the reception unit can suggest the optimal reception method based on the types of images the user has frequently uploaded in the past. The reception unit can also select the optimal reception method for a specific time period from the user's past upload history. For example, the reception unit can select the optimal reception method for a specific time period from the user's past upload history. The reception unit can also preferentially suggest upload methods (camera, gallery, etc.) that the user has used in the past. For example, the reception unit suggests the optimal reception method based on the upload methods the user has used in the past. In this way, the optimal reception method can be provided by analyzing the user's past image upload history. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0037] The reception unit may filter images based on the user's current project or field of interest when receiving the images. The reception unit, for example, receives only images related to a project currently in progress by the user. For example, the reception unit may receive only images related to a project currently in progress by the user. The reception unit may also preferentially receive highly relevant images based on the user's field of interest. For example, the reception unit may preferentially receive highly relevant images based on the user's field of interest. The reception unit may also filter and receive necessary images according to the progress of the user's project. For example, the reception unit may filter and receive necessary images according to the progress of the user's project. In this way, by filtering based on the user's current project or field of interest, highly relevant images can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0038] When accepting an image, the acceptance unit can select the optimal acceptance means according to the user's input method. For example, when the user uses voice input, the acceptance unit accepts the image using voice recognition. For example, when the user uses voice input, the acceptance unit accepts the image using voice recognition. Furthermore, when the user uses text input, the acceptance unit can also accept the image using text analysis. For example, when the user uses text input, the acceptance unit accepts the image using text analysis. Furthermore, when the user uses image input, the acceptance unit can also select the optimal acceptance means using image recognition. For example, when the user uses image input, the acceptance unit selects the optimal acceptance means using image recognition. This allows for selecting the optimal acceptance means according to the user's input method, thereby improving user convenience. Some or all of the above-described processing in the acceptance unit may be performed, for example, using AI or without AI.

[0039] When receiving images, the reception unit can preferentially receive highly relevant images taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially receives images related to that area. For example, when the user is in a specific area, the reception unit preferentially receives images related to that area. The reception unit can also filter highly relevant images based on the user's current location and receive them. For example, the reception unit filters highly relevant images based on the user's current location and receive them. The reception unit can also preferentially receive optimal images based on the user's geographical location information. For example, the reception unit preferentially receives optimal images based on the user's geographical location information. This makes it possible to preferentially receive highly relevant images taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0040] The reception unit can analyze the user's social media activity and receive related images when receiving images. The reception unit, for example, receives related images based on images shared by the user on social media. For example, the reception unit receives related images based on images shared by the user on social media. The reception unit can also analyze the user's social media activity and preferentially receive highly relevant images. For example, the reception unit analyzes the user's social media activity and preferentially receive highly relevant images. The reception unit can also receive related images by referring to the activity of the user's friends on social media. For example, the reception unit receives related images by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant images. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an image. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially select a specific reception method based on the user's past feedback. For example, the reception unit preferentially selects a specific reception method based on the user's past feedback. The reception unit can also customize the reception method by reflecting the user's feedback. For example, the reception unit customizes the reception method by reflecting the user's feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the component. For example, the analysis unit performs a detailed analysis on an important component. For example, the analysis unit performs a detailed analysis on an important component. The analysis unit can also perform a concise analysis on a general component. For example, the analysis unit performs a concise analysis on a general component. The analysis unit can also adjust the level of detail of the analysis based on the importance of the component. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the component. In this way, by adjusting the level of detail of the analysis based on the importance of the component, it is possible to provide necessary information with an appropriate level of detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the component. For example, the analysis unit applies an analysis algorithm dedicated to radios to radios. For example, the analysis unit applies an analysis algorithm dedicated to radios to radios. The analysis unit can also apply an analysis algorithm dedicated to antennas to antennas. For example, the analysis unit applies an analysis algorithm dedicated to antennas to antennas. The analysis unit can also apply an analysis algorithm dedicated to concrete poles to concrete poles. For example, the analysis unit applies an analysis algorithm dedicated to concrete poles to concrete poles. In this way, by applying different analysis algorithms depending on the category of the component, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on the analysis results previously performed by the user. For example, the analysis unit improves the accuracy of the analysis based on the analysis results previously performed by the user. The analysis unit can also select an optimal analysis method by referring to the user's past analysis history. For example, the analysis unit selects an optimal analysis method by referring to the user's past analysis history. The analysis unit can also improve the accuracy of the analysis by reflecting the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by reflecting the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the components. For example, the analysis unit prioritizes analysis of recently submitted components. For example, the analysis unit prioritizes analysis of recently submitted components. The analysis unit can also postpone analysis of components that have been submitted earlier. For example, the analysis unit postpones analysis of components that have been submitted earlier. The analysis unit can also determine the priority of analysis based on the submission date of the components. For example, the analysis unit determines the priority of analysis based on the submission date of the components. In this way, by determining the priority of analysis based on the submission date of the components, it is possible to prioritize analysis of the latest information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of components. The analysis unit, for example, prioritizes analysis of highly related components. For example, the analysis unit prioritizes analysis of highly related components. The analysis unit can also postpone analysis of less related components. For example, the analysis unit postpones analysis of less related components. The analysis unit can also adjust the order of analysis based on the relevance of components. For example, the analysis unit adjusts the order of analysis based on the relevance of components. In this way, by adjusting the order of analysis based on the relevance of components, it is possible to prioritize analysis of highly related components. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit uses a lot of technical terms. For example, if the user has specialized knowledge, the analysis unit uses a lot of technical terms. Furthermore, if the user is a beginner, the analysis unit can provide analysis results in simple language, avoiding technical terms. For example, if the user is a beginner, the analysis unit can provide analysis results in simple language, avoiding technical terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The search unit can improve search accuracy by taking into account interrelationships between components during a search. The search unit provides highly relevant search results based on, for example, interrelationships between components. For example, the search unit provides highly relevant search results based on interrelationships between components. The search unit can also improve search accuracy by taking into account interrelationships between components. For example, the search unit improves search accuracy by taking into account interrelationships between components. The search unit can also provide optimal search results by reflecting interrelationships between components. For example, the search unit provides optimal search results by reflecting interrelationships between components. In this way, highly relevant search results can be provided by improving search accuracy by taking into account interrelationships between components. Some or all of the above-described processing in the search unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The search unit can perform a search taking into account the attribute information of the submitter of the component. The search unit provides optimal search results based on, for example, the attribute information of the submitter. For example, the search unit provides optimal search results based on the attribute information of the submitter. The search unit can also improve search accuracy by taking into account the attribute information of the submitter. For example, the search unit improves search accuracy by taking into account the attribute information of the submitter. The search unit can also provide highly relevant search results by reflecting the attribute information of the submitter. For example, the search unit provides highly relevant search results by reflecting the attribute information of the submitter. In this way, by performing a search taking into account the attribute information of the submitter of the component, highly relevant search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI.

[0050] During a search, the search unit can weight the search based on the frequency of submission of components. For example, the search unit prioritizes searching for components with a high submission frequency. For example, the search unit prioritizes searching for components with a high submission frequency. The search unit can also postpone searching for components with a low submission frequency. For example, the search unit postpones searching for components with a low submission frequency. The search unit can also weight the search based on the frequency of submission of components. For example, the search unit weights the search based on the frequency of submission of components. In this way, by weighting the search based on the frequency of submission of components, it is possible to prioritize searching for frequently used components. Some or all of the above-described processing in the search unit may be performed, for example, using AI, or may be performed without using AI.

[0051] The search unit can perform a search taking into account the geographical distribution of components. The search unit provides optimal search results based on, for example, the geographical distribution of components. For example, the search unit provides optimal search results based on the geographical distribution of components. The search unit can also improve search accuracy by taking into account the geographical distribution of components. For example, the search unit improves search accuracy by taking into account the geographical distribution of components. The search unit can also provide highly relevant search results by reflecting the geographical distribution of components. For example, the search unit provides highly relevant search results by reflecting the geographical distribution of components. In this way, by performing a search taking into account the geographical distribution of components, highly relevant search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI.

[0052] The search unit can improve search accuracy by referring to related literature of components during a search. The search unit provides optimal search results based on, for example, related literature of components. For example, the search unit provides optimal search results based on related literature of components. The search unit can also improve search accuracy by taking related literature of components into account. For example, the search unit improves search accuracy by taking related literature of components into account. The search unit can also provide highly relevant search results by reflecting related literature of components. For example, the search unit provides highly relevant search results by reflecting related literature of components. In this way, by improving search accuracy by referring to related literature of components, highly relevant search results can be provided. Some or all of the above-described processing in the search unit may be performed, for example, using AI, or may be performed without using AI.

[0053] The search unit can perform a search taking into account the market value of the components. The search unit provides optimal search results based on, for example, the market value of the components. For example, the search unit provides optimal search results based on, for example, the market value of the components. The search unit can also improve the accuracy of the search by taking into account the market value of the components. For example, the search unit improves the accuracy of the search by taking into account the market value of the components. The search unit can also provide highly relevant search results by reflecting the market value of the components. For example, the search unit provides highly relevant search results by reflecting the market value of the components. In this way, by performing a search taking into account the market value of the components, highly relevant search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI.

[0054] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. The providing unit, for example, suggests the optimal display method based on the display method used by the user in the past. For example, the providing unit suggests the optimal display method based on the display method used by the user in the past. The providing unit can also preferentially select a specific display method based on the user's past operation history. For example, the providing unit preferentially selects a specific display method based on the user's past operation history. The providing unit can also customize the display method by reflecting the user's operation history. For example, the providing unit customizes the display method by reflecting the user's operation history. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0055] The providing unit can customize the display content according to the user's current task when providing the information. The providing unit, for example, prioritizes displaying information related to the user's current task. For example, the providing unit prioritizes displaying information related to the user's current task. The providing unit can also suggest optimal display content based on the user's current task. For example, the providing unit suggests optimal display content based on the user's current task. The providing unit can also customize the display content according to the progress of the user's task. For example, the providing unit customizes the display content according to the progress of the user's task. In this way, by customizing the display content according to the user's current task, it is possible to provide optimal information for the user. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0056] The providing unit can improve the display method by reflecting user feedback when providing the information. The providing unit, for example, suggests an optimal display method based on feedback provided by the user. For example, the providing unit suggests an optimal display method based on feedback provided by the user. The providing unit can also preferentially select a specific display method based on user feedback. For example, the providing unit preferentially selects a specific display method based on user feedback. The providing unit can also improve the display method by reflecting user feedback. For example, the providing unit improves the display method by reflecting user feedback. In this way, the display method can be improved by reflecting user feedback, and optimal information can be provided to the user. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0057] The providing unit can provide optimal information by taking into consideration the geographical location information of the user at the time of providing the information. The providing unit provides highly relevant information based on, for example, the user's current location. For example, the providing unit provides highly relevant information based on the user's current location. The providing unit can also provide optimal information by taking into consideration the geographical location information of the user. For example, the providing unit provides optimal information by taking into consideration the geographical location information of the user. The providing unit can also provide highly relevant information by reflecting the geographical location information of the user. For example, the providing unit provides highly relevant information by reflecting the geographical location information of the user. In this way, highly relevant information can be provided by taking into consideration the geographical location information of the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0058] At the time of providing, the providing unit can analyze the user's social media activity and provide related information. The providing unit can provide the related information based on, for example, information shared by the user on social media. For example, the providing unit can provide the related information based on information shared by the user on social media. The providing unit can also analyze the user's social media activity and provide highly relevant information. For example, the providing unit can analyze the user's social media activity and provide highly relevant information. The providing unit can also provide the related information by referring to the activity of the user's friends on social media. For example, the providing unit can provide the related information by referring to the activity of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI.

[0059] The providing unit can customize the display method by reflecting the user's past feedback when providing the display method. The providing unit, for example, suggests an optimal display method based on feedback provided by the user in the past. For example, the providing unit suggests an optimal display method based on feedback provided by the user in the past. The providing unit can also preferentially select a specific display method based on the user's past feedback. For example, the providing unit preferentially selects a specific display method based on the user's past feedback. The providing unit can also customize the display method by reflecting the user's feedback. For example, the providing unit customizes the display method by reflecting the user's feedback. In this way, the optimal display method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0060] The update unit can optimize the learning algorithm by referring to past learning data during updating. The update unit, for example, selects an optimal learning algorithm based on past learning data. For example, the update unit selects an optimal learning algorithm based on past learning data. The update unit can also optimize the learning algorithm by referring to past learning data. For example, the update unit optimizes the learning algorithm by referring to past learning data. The update unit can also improve the learning algorithm by reflecting past learning data. For example, the update unit improves the learning algorithm by reflecting past learning data. In this way, by referring to past learning data, the learning algorithm can be optimized and accuracy can be improved. Some or all of the above-mentioned processing in the update unit may be performed, for example, using AI or without using AI.

[0061] The update unit can update the learning data by reflecting user feedback during updating. The update unit updates the learning data based on, for example, feedback provided by the user. For example, the update unit updates the learning data based on feedback provided by the user. The update unit can also improve the learning data by reflecting user feedback. For example, the update unit improves the learning data by reflecting user feedback. The update unit can also select optimal learning data based on user feedback. For example, the update unit selects optimal learning data based on user feedback. In this way, by reflecting user feedback, the learning data can be optimized and accuracy can be improved. Some or all of the above-described processing in the update unit may be performed, for example, using AI or without using AI.

[0062] The update unit can weight the learning data based on the submission date of the components when updating. The update unit, for example, updates the learning data by placing emphasis on data of recently submitted components. For example, the update unit updates the learning data by placing emphasis on data of recently submitted components. The update unit can also update data of components that have been submitted recently by lowering the weight. For example, the update unit updates data of components that have been submitted recently by lowering the weight. The update unit can also weight the learning data based on the submission date of the components. For example, the update unit weights the learning data based on the submission date of the components. In this way, by weighting the learning data based on the submission date of the components, it is possible to update the learning data by placing emphasis on the most recent information. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI.

[0063] The update unit can integrate information from different data sources to expand the training data during updating. The update unit, for example, integrates information from different data sources to expand the training data. For example, the update unit integrates information from different data sources to expand the training data. The update unit can also update the training data based on information from different data sources. For example, the update unit updates the training data based on information from different data sources. The update unit can also improve the training data by reflecting information from different data sources. For example, the update unit improves the training data by reflecting information from different data sources. In this way, by integrating information from different data sources, the training data can be expanded and its accuracy can be improved. Some or all of the above-described processing in the update unit may be performed, for example, using AI or without using AI.

[0064] During management, the management unit can select the optimal management method by referring to the past usage history of the database. The management unit, for example, proposes the optimal management method based on the past usage history of the database. For example, the management unit proposes the optimal management method based on the past usage history of the database. The management unit can also preferentially select a specific management method based on the past usage history of the database. For example, the management unit preferentially selects a specific management method based on the past usage history of the database. The management unit can also customize the management method by reflecting the usage history of the database. For example, the management unit customizes the management method by reflecting the usage history of the database. In this way, the optimal management method can be provided by referring to the past usage history of the database. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI.

[0065] During management, the management unit can customize the management means based on the current state of the database. The management unit, for example, proposes the optimal management means based on the current state of the database. For example, the management unit proposes the optimal management means based on the current state of the database. The management unit can also preferentially select a specific management means based on the current state of the database. For example, the management unit preferentially selects a specific management means based on the current state of the database. The management unit can also customize the management means by reflecting the state of the database. For example, the management unit customizes the management means by reflecting the state of the database. In this way, by customizing the management means based on the current state of the database, it is possible to provide an optimal management method. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI.

[0066] During management, the management unit can select an optimal management method by taking into account the geographical location information of the database. The management unit, for example, proposes an optimal management method based on the geographical location information of the database. For example, the management unit proposes an optimal management method based on the geographical location information of the database. The management unit can also select an optimal management method by taking into account the geographical location information of the database. For example, the management unit selects an optimal management method by taking into account the geographical location information of the database. The management unit can also customize the management method by reflecting the geographical location information of the database. For example, the management unit customizes the management method by reflecting the geographical location information of the database. In this way, it is possible to provide an optimal management method by taking into account the geographical location information of the database. Some or all of the above-described processing in the management unit may be performed, for example, using AI, or may be performed without using AI.

[0067] During management, the management unit can improve the management method by referring to the related literature in the database. For example, the management unit proposes an optimal management method based on the related literature in the database. For example, the management unit proposes an optimal management method based on the related literature in the database. The management unit can also improve the management method by taking into account the related literature in the database. For example, the management unit improves the management method by taking into account the related literature in the database. The management unit can also customize the management method by reflecting the related literature in the database. For example, the management unit customizes the management method by reflecting the related literature in the database. In this way, by referring to the related literature in the database, the management method can be improved and an optimal management method can be provided. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI or without using AI.

[0068] During guidance, the guide unit can select the optimal guide method by referring to the user's past operation history. The guide unit, for example, suggests the optimal guide method based on guide methods used by the user in the past. For example, the guide unit suggests the optimal guide method based on guide methods used by the user in the past. The guide unit can also preferentially select a specific guide method based on the user's past operation history. For example, the guide unit preferentially selects a specific guide method based on the user's past operation history. The guide unit can also customize the guide method by reflecting the user's operation history. For example, the guide unit customizes the guide method by reflecting the user's operation history. In this way, the optimal guide method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the guide unit may be performed using AI, for example, or may be performed without using AI.

[0069] The guide unit can customize the guide content according to the user's current task during guidance. For example, the guide unit prioritizes providing guide content related to the task the user is currently performing. For example, the guide unit prioritizes providing guide content related to the task the user is currently performing. The guide unit can also suggest optimal guide content based on the user's current task. For example, the guide unit suggests optimal guide content based on the user's current task. The guide unit can also customize the guide content according to the progress of the user's task. For example, the guide unit customizes the guide content according to the progress of the user's task. In this way, by customizing the guide content according to the user's current task, it is possible to provide the optimal guide for the user. Some or all of the above-described processing in the guide unit may be performed using, for example, AI, or may be performed without using AI.

[0070] When providing guidance, the guide unit can select the optimal guidance method by taking into consideration device information of the user. For example, if the user is using a smartphone, the guide unit provides a guidance method that matches the screen size. For example, if the user is using a smartphone, the guide unit provides a guidance method that matches the screen size. Furthermore, if the user is using a tablet, the guide unit can also provide a guidance method optimized for a large screen. For example, if the user is using a tablet, the guide unit provides a guidance method optimized for a large screen. Furthermore, if the user is using a smartwatch, the guide unit can also provide a guidance method that is concise and highly visible. For example, if the user is using a smartwatch, the guide unit provides a guidance method that is concise and highly visible. This makes it possible to provide the optimal guidance method by taking into consideration device information of the user. Some or all of the above-described processing in the guide unit may be performed using AI, for example, or may be performed without using AI.

[0071] The guide unit can make the guide content multilingual during guidance in accordance with the user's language setting. The guide unit automatically sets the guide content based on, for example, the language setting of the user's device. For example, the guide unit automatically sets the guide content based on the language setting of the user's device. The guide unit can also provide a language switching function when the user uses multiple languages. For example, the guide unit provides a language switching function when the user uses multiple languages. The guide unit can also provide the guide content in a specific language when the user selects that language. For example, the guide unit provides the guide content in that language when the user selects a specific language. In this way, by making the guide content multilingual in accordance with the user's language setting, it is possible to provide the optimal guide for the user. Some or all of the above-described processing in the guide unit may be performed, for example, using AI or without AI.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The reception unit can analyze the user's past search history and provide optimal search results. For example, the reception unit can prioritize highly relevant search results based on keywords and images the user has searched for in the past. The reception unit can also provide optimal search results for a specific time period based on the user's past search history. Furthermore, the reception unit can prioritize suggesting search methods (keyword search, image search, etc.) that the user has used in the past. In this way, optimal search results can be provided by analyzing the user's past search history.

[0074] The search unit can filter search results based on the user's current project or area of ​​interest. For example, only information related to the user's current project can be searched for. Also, the search unit can prioritize highly relevant information based on the user's area of ​​interest. Furthermore, the search unit can filter and search for necessary information according to the progress of the user's project. In this way, highly relevant information can be provided by filtering search results based on the user's current project or area of ​​interest.

[0075] The management unit can select the optimal management method by referring to the past usage history of the database. For example, it can propose the optimal management method based on the past usage history of the database. It can also preferentially select a specific management method based on the past usage history of the database. Furthermore, it can also customize the management method by reflecting the usage history of the database. In this way, it is possible to provide the optimal management method by referring to the past usage history of the database.

[0076] The update unit can integrate information from different data sources to expand the training data. For example, information from different data sources is integrated to expand the training data. The update unit can also update the training data based on information from different data sources. Furthermore, the update unit can improve the training data by reflecting information from different data sources. In this way, by integrating information from different data sources, the training data can be expanded and its accuracy can be improved.

[0077] The search unit can perform a search taking into account the market value of the component. For example, it can provide optimal search results based on the market value of the component. It can also improve search accuracy by taking into account the market value of the component. It can also provide highly relevant search results by reflecting the market value of the component. In this way, it is possible to provide highly relevant search results by performing a search taking into account the market value of the component.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The reception unit receives images from the user. Images from the user can be in formats such as JPEG, PNG, and GIF. The reception unit can receive images taken with a smartphone, a digital camera, or scanned with a scanner. Step 2: The analysis unit analyzes the image received by the reception unit and extracts the features of the component. These features include edge detection, color histogram, and shape analysis. For example, edge detection technology is used to extract the contours of the image, color histograms are used to analyze the color distribution of the image, and shape analysis technology is used to identify the shape of the image. Step 3: The search unit searches for information from the database based on the features extracted by the analysis unit. The search can be done using keyword search, similar image search, etc. For example, a keyword search can be used to search for the relevant product name from the database, and a similar image search can be used to search for related information. Step 4: The providing unit provides the search results obtained by the search unit to the user. The providing may include displaying text, displaying images, providing links, etc. For example, the search results may be displayed as text, displayed as images, and related links may be provided.

[0080] (Example 2) The search system according to an embodiment of the present invention analyzes the characteristics of components and searches for and provides related information simply by uploading an image. The search system allows users to upload an image of the component they are searching for to an app, where a generation AI analyzes the image and extracts the component's characteristics. Based on the extracted characteristics, the generation AI searches a database for relevant product names and related information and provides the search results to the user. This system allows even new employees and transferees with limited knowledge to contribute immediately to the company. For example, the search system allows users to take a photo of a radio, antenna, or concrete pole with a smartphone and upload it to the app. This image is then input into the generation AI. The generation AI then analyzes the input image, extracts image features, and identifies information such as the component's type, shape, and color. Based on the extracted features, the generation AI searches the database for the corresponding product name and related information. For example, based on the identified antenna shape and color, the database is searched for information such as the corresponding product name, manufacturer, and specifications. The search results, including the product name and related information, are displayed on the app screen. Using this information, users can identify the necessary components and proceed with their work. This allows the search system to quickly and accurately identify components, improving work efficiency and overall productivity. This allows the search system to quickly and accurately identify components, improving work efficiency and overall productivity.

[0081] A search system according to an embodiment includes a receiving unit, an analysis unit, a search unit, and a providing unit. The receiving unit receives an image from a user. The image from the user may be in a format such as, but not limited to, JPEG, PNG, or GIF. The receiving unit may receive, for example, an image taken with a smartphone. The receiving unit may also receive an image taken with a digital camera. The receiving unit may also receive an image scanned with a scanner. For example, the receiving unit may receive an image in JPEG format and transmit the image to the analysis unit. The analysis unit may use a generative AI to analyze the image received by the receiving unit and extract features of the components. Features include, but are not limited to, edge detection, color histograms, and shape analysis. For example, the analysis unit may use edge detection technology to extract the contours of the image. The analysis unit may also use a color histogram to analyze the color distribution of the image. The analysis unit may also use shape analysis technology to identify the shape of the image. For example, the analysis unit may use edge detection technology to identify the shape of an antenna and a color histogram to identify the color of the antenna. The search unit searches for information from the database based on the features extracted by the analysis unit. Examples of searches include, but are not limited to, keyword search and similar image search. For example, the search unit searches the database for a relevant product name using a keyword search. The search unit can also search for related information from the database using a similar image search. The search unit can also search for product specification information from the database based on the extracted features. For example, the search unit searches the database for a relevant product name and manufacturer based on the shape and color of the antenna. The providing unit provides the search results obtained by the search unit to the user. Examples of providing include, but are not limited to, displaying text, displaying images, providing links, etc. For example, the providing unit displays the search results as text. The providing unit can also display the search results as images. The providing unit can also provide links related to the search results. For example, the providing unit displays the product name and manufacturer as text and the product specification information as images.As a result, the search system according to the embodiment can analyze the characteristics of components and search for and provide related information simply by the user uploading an image, allowing the user to quickly obtain the information they need.

[0082] The system includes an update unit that updates the learning data of the generation AI. The update unit updates the learning data of the generation AI. Examples of updates include, but are not limited to, periodic updates and updates based on user feedback. For example, the update unit periodically updates the learning data of the generation AI. The update unit can also update the learning data of the generation AI based on user feedback. The update unit can also update the learning data of the generation AI based on information from a new data source. For example, the update unit periodically updates the learning data of the generation AI to reflect the latest information. In this way, by updating the learning data of the generation AI, analysis accuracy can be improved.

[0083] The system includes a management unit that manages the database. The management unit manages the database. Examples of management include, but are not limited to, data backup, access control, and data consistency check. For example, the management unit periodically backs up the database. The management unit can also perform access control to prevent unauthorized access to the database. The management unit can also perform data consistency checks to maintain data accuracy. For example, the management unit periodically backs up the database to prevent data loss. In this way, by managing the database, the accuracy and reliability of information can be maintained.

[0084] The device includes a guide unit that guides a user through an operation procedure. The guide unit guides the user through the operation procedure. Examples of guides include, but are not limited to, step-by-step instructions, video tutorials, tooltips, and the like. For example, the guide unit displays step-by-step instructions to make it easier for the user to understand the operation procedure. The guide unit can also provide a video tutorial to visually explain the operation procedure. The guide unit can also display tooltips to explain key points of the operation procedure. For example, the guide unit displays step-by-step instructions to make it easier for the user to understand the operation procedure. This can improve the efficiency and accuracy of operations by guiding the user through the operation procedure.

[0085] The analysis unit can extract image features and identify information such as the type, shape, and color of components. The analysis unit extracts the contours of the image using, for example, edge detection technology. For example, the analysis unit identifies the shape of an antenna using edge detection technology. The analysis unit can also analyze the color distribution of the image using a color histogram. For example, the analysis unit identifies the color of an antenna using a color histogram. The analysis unit can also identify the shape of the image using shape analysis technology. For example, the analysis unit identifies the shape of a radio using shape analysis technology. In this way, by extracting image features and identifying information such as the type, shape, and color of components, more accurate search results can be provided.

[0086] The search unit can search the database for the corresponding product name and related information based on the extracted features. The search unit can, for example, use a keyword search to search for the corresponding product name from the database. For example, the search unit can use a keyword search to search for the product name of an antenna. The search unit can also search for related information from the database using a similar image search. For example, the search unit can use a similar image search to search for related information about a radio. The search unit can also search for product specification information from the database based on the extracted features. For example, the search unit can search for the corresponding product name and manufacturer from the database based on the shape and color of the antenna. This makes it possible to quickly provide the user with the information they need by searching the database for the corresponding product name and related information based on the extracted features.

[0087] The providing unit can provide the search results to the user. The providing unit, for example, displays the search results as text. For example, the providing unit displays the search results as text and provides them to the user. The providing unit can also display the search results as images. For example, the providing unit displays the search results as images and provides them to the user. The providing unit can also provide links related to the search results. For example, the providing unit provides links related to the search results and provides them to the user. In this way, by providing the search results to the user, the user can quickly obtain the information he or she needs.

[0088] The reception unit can estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions. For example, if the user is anxious, the reception unit immediately receives the images and quickly starts processing. For example, if the user is anxious, the reception unit immediately receives the images and quickly starts processing. Furthermore, if the user is relaxed, the reception unit can slightly delay the reception of the images to prioritize other tasks. For example, if the user is relaxed, the reception unit can slightly delay the reception of the images to prioritize other tasks. Furthermore, if the user is anxious, the reception unit can display a confirmation message before receiving the images to provide a sense of security. For example, if the user is anxious, the reception unit can display a confirmation message before receiving the images to provide a sense of security. This allows for adjusting the timing of image reception according to the user's emotions, thereby improving user convenience. Emotion estimation is realized using an emotion estimation function, for example, 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.

[0089] The reception unit can analyze the user's past image upload history and select the optimal reception method. The reception unit can, for example, suggest the optimal reception method based on the types of images the user has frequently uploaded in the past. For example, the reception unit can suggest the optimal reception method based on the types of images the user has frequently uploaded in the past. The reception unit can also select the optimal reception method for a specific time period from the user's past upload history. For example, the reception unit can select the optimal reception method for a specific time period from the user's past upload history. The reception unit can also preferentially suggest upload methods (camera, gallery, etc.) that the user has used in the past. For example, the reception unit suggests the optimal reception method based on the upload methods the user has used in the past. In this way, the optimal reception method can be provided by analyzing the user's past image upload history. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0090] The reception unit may filter images based on the user's current project or field of interest when receiving the images. The reception unit, for example, receives only images related to a project currently in progress by the user. For example, the reception unit may receive only images related to a project currently in progress by the user. The reception unit may also preferentially receive highly relevant images based on the user's field of interest. For example, the reception unit may preferentially receive highly relevant images based on the user's field of interest. The reception unit may also filter and receive necessary images according to the progress of the user's project. For example, the reception unit may filter and receive necessary images according to the progress of the user's project. In this way, by filtering based on the user's current project or field of interest, highly relevant images can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0091] When accepting an image, the acceptance unit can select the optimal acceptance means according to the user's input method. For example, when the user uses voice input, the acceptance unit accepts the image using voice recognition. For example, when the user uses voice input, the acceptance unit accepts the image using voice recognition. Furthermore, when the user uses text input, the acceptance unit can also accept the image using text analysis. For example, when the user uses text input, the acceptance unit accepts the image using text analysis. Furthermore, when the user uses image input, the acceptance unit can also select the optimal acceptance means using image recognition. For example, when the user uses image input, the acceptance unit selects the optimal acceptance means using image recognition. This allows for selecting the optimal acceptance means according to the user's input method, thereby improving user convenience. Some or all of the above-described processing in the acceptance unit may be performed, for example, using AI or without AI.

[0092] The reception unit can estimate the user's emotions and determine the priority of images to be received based on the estimated user's emotions. For example, when the user is anxious, the reception unit prioritizes receiving images with a high level of urgency. For example, when the user is anxious, the reception unit prioritizes receiving images with a high level of urgency. Furthermore, when the user is relaxed, the reception unit can also receive images with a normal priority. For example, when the user is relaxed, the reception unit can receive images with a normal priority. Furthermore, when the user is anxious, the reception unit can also prioritize receiving important images. For example, when the user is anxious, the reception unit prioritizes receiving important images. In this way, by determining the priority of images to be received according to the user's emotions, important images can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, 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.

[0093] When receiving images, the reception unit can preferentially receive highly relevant images taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially receives images related to that area. For example, when the user is in a specific area, the reception unit preferentially receives images related to that area. The reception unit can also filter highly relevant images based on the user's current location and receive them. For example, the reception unit filters highly relevant images based on the user's current location and receive them. The reception unit can also preferentially receive optimal images based on the user's geographical location information. For example, the reception unit preferentially receives optimal images based on the user's geographical location information. This makes it possible to preferentially receive highly relevant images taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0094] The reception unit can analyze the user's social media activity and receive related images when receiving images. The reception unit, for example, receives related images based on images shared by the user on social media. For example, the reception unit receives related images based on images shared by the user on social media. The reception unit can also analyze the user's social media activity and preferentially receive highly relevant images. For example, the reception unit analyzes the user's social media activity and preferentially receive highly relevant images. The reception unit can also receive related images by referring to the activity of the user's friends on social media. For example, the reception unit receives related images by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant images. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0095] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an image. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially select a specific reception method based on the user's past feedback. For example, the reception unit preferentially selects a specific reception method based on the user's past feedback. The reception unit can also customize the reception method by reflecting the user's feedback. For example, the reception unit customizes the reception method by reflecting the user's feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0096] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. The analysis unit can also provide concise analysis results if the user is in a hurry. For example, if the user is in a hurry, the analysis unit provides concise analysis results. The analysis unit can also provide analysis results that give the user a sense of security if the user is feeling anxious. For example, if the user is feeling anxious, the analysis unit provides analysis results that give the user a sense of security. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, 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.

[0097] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the component. For example, the analysis unit performs a detailed analysis on an important component. For example, the analysis unit performs a detailed analysis on an important component. The analysis unit can also perform a concise analysis on a general component. For example, the analysis unit performs a concise analysis on a general component. The analysis unit can also adjust the level of detail of the analysis based on the importance of the component. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the component. In this way, by adjusting the level of detail of the analysis based on the importance of the component, it is possible to provide necessary information with an appropriate level of detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0098] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the component. For example, the analysis unit applies an analysis algorithm dedicated to radios to radios. For example, the analysis unit applies an analysis algorithm dedicated to radios to radios. The analysis unit can also apply an analysis algorithm dedicated to antennas to antennas. For example, the analysis unit applies an analysis algorithm dedicated to antennas to antennas. The analysis unit can also apply an analysis algorithm dedicated to concrete poles to concrete poles. For example, the analysis unit applies an analysis algorithm dedicated to concrete poles to concrete poles. In this way, by applying different analysis algorithms depending on the category of the component, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0099] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on the analysis results previously performed by the user. For example, the analysis unit improves the accuracy of the analysis based on the analysis results previously performed by the user. The analysis unit can also select an optimal analysis method by referring to the user's past analysis history. For example, the analysis unit selects an optimal analysis method by referring to the user's past analysis history. The analysis unit can also improve the accuracy of the analysis by reflecting the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by reflecting the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0100] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit performs a short and to-the-point analysis. For example, if the user is in a hurry, the analysis unit performs a short and to-the-point analysis. The analysis unit can also perform a detailed analysis if the user is relaxed. For example, if the user is relaxed, the analysis unit performs a detailed analysis. The analysis unit can also perform an analysis that gives the user a sense of security if the user is feeling anxious. For example, if the user is feeling anxious, the analysis unit performs an analysis that gives the user a sense of security. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing the user with an optimal analysis result. 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.

[0101] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the components. For example, the analysis unit prioritizes analysis of recently submitted components. For example, the analysis unit prioritizes analysis of recently submitted components. The analysis unit can also postpone analysis of components that have been submitted earlier. For example, the analysis unit postpones analysis of components that have been submitted earlier. The analysis unit can also determine the priority of analysis based on the submission date of the components. For example, the analysis unit determines the priority of analysis based on the submission date of the components. In this way, by determining the priority of analysis based on the submission date of the components, it is possible to prioritize analysis of the latest information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0102] During analysis, the analysis unit can adjust the order of analysis based on the relevance of components. The analysis unit, for example, prioritizes analysis of highly related components. For example, the analysis unit prioritizes analysis of highly related components. The analysis unit can also postpone analysis of less related components. For example, the analysis unit postpones analysis of less related components. The analysis unit can also adjust the order of analysis based on the relevance of components. For example, the analysis unit adjusts the order of analysis based on the relevance of components. In this way, by adjusting the order of analysis based on the relevance of components, it is possible to prioritize analysis of highly related components. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0103] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit uses a lot of technical terms. For example, if the user has specialized knowledge, the analysis unit uses a lot of technical terms. Furthermore, if the user is a beginner, the analysis unit can provide analysis results in simple language, avoiding technical terms. For example, if the user is a beginner, the analysis unit can provide analysis results in simple language, avoiding technical terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0104] The search unit can estimate the user's emotions and adjust search criteria based on the estimated user emotions. For example, if the user is anxious, the search unit can quickly provide search results. For example, if the user is anxious, the search unit can quickly provide search results. The search unit can also provide detailed search results if the user is relaxed. For example, if the user is relaxed, the search unit can provide detailed search results. The search unit can also provide search results that give a sense of security if the user is anxious. For example, if the user is anxious, the search unit can provide search results that give a sense of security. In this way, by adjusting the search criteria according to the user's emotions, it is possible to provide optimal search results for the user. Emotion estimation is realized using an emotion estimation function using, for example, 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.

[0105] The search unit can improve search accuracy by taking into account interrelationships between components during a search. The search unit provides highly relevant search results based on, for example, interrelationships between components. For example, the search unit provides highly relevant search results based on interrelationships between components. The search unit can also improve search accuracy by taking into account interrelationships between components. For example, the search unit improves search accuracy by taking into account interrelationships between components. The search unit can also provide optimal search results by reflecting interrelationships between components. For example, the search unit provides optimal search results by reflecting interrelationships between components. In this way, highly relevant search results can be provided by improving search accuracy by taking into account interrelationships between components. Some or all of the above-described processing in the search unit may be performed, for example, using AI, or may be performed without using AI.

[0106] The search unit can perform a search taking into account the attribute information of the submitter of the component. The search unit provides optimal search results based on, for example, the attribute information of the submitter. For example, the search unit provides optimal search results based on the attribute information of the submitter. The search unit can also improve search accuracy by taking into account the attribute information of the submitter. For example, the search unit improves search accuracy by taking into account the attribute information of the submitter. The search unit can also provide highly relevant search results by reflecting the attribute information of the submitter. For example, the search unit provides highly relevant search results by reflecting the attribute information of the submitter. In this way, by performing a search taking into account the attribute information of the submitter of the component, highly relevant search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI.

[0107] During a search, the search unit can weight the search based on the frequency of submission of components. For example, the search unit prioritizes searching for components with a high submission frequency. For example, the search unit prioritizes searching for components with a high submission frequency. The search unit can also postpone searching for components with a low submission frequency. For example, the search unit postpones searching for components with a low submission frequency. The search unit can also weight the search based on the frequency of submission of components. For example, the search unit weights the search based on the frequency of submission of components. In this way, by weighting the search based on the frequency of submission of components, it is possible to prioritize searching for frequently used components. Some or all of the above-described processing in the search unit may be performed, for example, using AI, or may be performed without using AI.

[0108] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated user emotions. For example, if the user is anxious, the search unit prioritizes displaying important search results. For example, if the user is anxious, the search unit prioritizes displaying important search results. The search unit can also display detailed search results if the user is relaxed. For example, if the user is relaxed, the search unit displays detailed search results. The search unit can also prioritize displaying search results that give a sense of security if the user is anxious. For example, if the user is anxious, the search unit prioritizes displaying search results that give a sense of security. This allows the display order of search results to be adjusted according to the user's emotions, thereby providing optimal information for the user. 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.

[0109] The search unit can perform a search taking into account the geographical distribution of components. The search unit provides optimal search results based on, for example, the geographical distribution of components. For example, the search unit provides optimal search results based on the geographical distribution of components. The search unit can also improve search accuracy by taking into account the geographical distribution of components. For example, the search unit improves search accuracy by taking into account the geographical distribution of components. The search unit can also provide highly relevant search results by reflecting the geographical distribution of components. For example, the search unit provides highly relevant search results by reflecting the geographical distribution of components. In this way, by performing a search taking into account the geographical distribution of components, highly relevant search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI.

[0110] The search unit can improve search accuracy by referring to related literature of components during a search. The search unit provides optimal search results based on, for example, related literature of components. For example, the search unit provides optimal search results based on related literature of components. The search unit can also improve search accuracy by taking related literature of components into account. For example, the search unit improves search accuracy by taking related literature of components into account. The search unit can also provide highly relevant search results by reflecting related literature of components. For example, the search unit provides highly relevant search results by reflecting related literature of components. In this way, by improving search accuracy by referring to related literature of components, highly relevant search results can be provided. Some or all of the above-described processing in the search unit may be performed, for example, using AI, or may be performed without using AI.

[0111] The search unit can perform a search taking into account the market value of the components. The search unit provides optimal search results based on, for example, the market value of the components. For example, the search unit provides optimal search results based on, for example, the market value of the components. The search unit can also improve the accuracy of the search by taking into account the market value of the components. For example, the search unit improves the accuracy of the search by taking into account the market value of the components. The search unit can also provide highly relevant search results by reflecting the market value of the components. For example, the search unit provides highly relevant search results by reflecting the market value of the components. In this way, by performing a search taking into account the market value of the components, highly relevant search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI.

[0112] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit provides a simple, highly visible display method. For example, when the user is nervous, the providing unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. For example, when the user is relaxed, the providing unit provides a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, when the user is in a hurry, the providing unit provides a display method that focuses on the main points. In this way, by adjusting the information display method according to the user's emotions, it is possible to provide optimal information for the user. Emotion estimation is realized using an emotion estimation function using, for example, 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.

[0113] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. The providing unit, for example, suggests the optimal display method based on the display method used by the user in the past. For example, the providing unit suggests the optimal display method based on the display method used by the user in the past. The providing unit can also preferentially select a specific display method based on the user's past operation history. For example, the providing unit preferentially selects a specific display method based on the user's past operation history. The providing unit can also customize the display method by reflecting the user's operation history. For example, the providing unit customizes the display method by reflecting the user's operation history. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0114] The providing unit can customize the display content according to the user's current task when providing the information. The providing unit, for example, prioritizes displaying information related to the user's current task. For example, the providing unit prioritizes displaying information related to the user's current task. The providing unit can also suggest optimal display content based on the user's current task. For example, the providing unit suggests optimal display content based on the user's current task. The providing unit can also customize the display content according to the progress of the user's task. For example, the providing unit customizes the display content according to the progress of the user's task. In this way, by customizing the display content according to the user's current task, it is possible to provide optimal information for the user. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0115] The providing unit can improve the display method by reflecting user feedback when providing the information. The providing unit, for example, suggests an optimal display method based on feedback provided by the user. For example, the providing unit suggests an optimal display method based on feedback provided by the user. The providing unit can also preferentially select a specific display method based on user feedback. For example, the providing unit preferentially selects a specific display method based on user feedback. The providing unit can also improve the display method by reflecting user feedback. For example, the providing unit improves the display method by reflecting user feedback. In this way, the display method can be improved by reflecting user feedback, and optimal information can be provided to the user. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0116] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, when the user is anxious, the providing unit prioritizes providing important information. For example, when the user is anxious, the providing unit prioritizes providing important information. The providing unit can also provide detailed information when the user is relaxed. For example, when the user is relaxed, the providing unit provides detailed information. The providing unit can also prioritize providing information that gives a sense of security when the user is anxious. For example, when the user is anxious, the providing unit prioritizes providing information that gives a sense of security. In this way, by determining the priority of information according to the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, 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.

[0117] The providing unit can provide optimal information by taking into consideration the geographical location information of the user at the time of providing the information. The providing unit provides highly relevant information based on, for example, the user's current location. For example, the providing unit provides highly relevant information based on the user's current location. The providing unit can also provide optimal information by taking into consideration the geographical location information of the user. For example, the providing unit provides optimal information by taking into consideration the geographical location information of the user. The providing unit can also provide highly relevant information by reflecting the geographical location information of the user. For example, the providing unit provides highly relevant information by reflecting the geographical location information of the user. In this way, highly relevant information can be provided by taking into consideration the geographical location information of the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0118] At the time of providing, the providing unit can analyze the user's social media activity and provide related information. The providing unit can provide the related information based on, for example, information shared by the user on social media. For example, the providing unit can provide the related information based on information shared by the user on social media. The providing unit can also analyze the user's social media activity and provide highly relevant information. For example, the providing unit can analyze the user's social media activity and provide highly relevant information. The providing unit can also provide the related information by referring to the activity of the user's friends on social media. For example, the providing unit can provide the related information by referring to the activity of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI.

[0119] The providing unit can customize the display method by reflecting the user's past feedback when providing the display method. The providing unit, for example, suggests an optimal display method based on feedback provided by the user in the past. For example, the providing unit suggests an optimal display method based on feedback provided by the user in the past. The providing unit can also preferentially select a specific display method based on the user's past feedback. For example, the providing unit preferentially selects a specific display method based on the user's past feedback. The providing unit can also customize the display method by reflecting the user's feedback. For example, the providing unit customizes the display method by reflecting the user's feedback. In this way, the optimal display method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0120] The update unit can estimate the user's emotions and select training data based on the estimated user's emotions. For example, if the user is relaxed, the update unit selects detailed training data. For example, if the user is relaxed, the update unit selects detailed training data. The update unit can also select concise training data if the user is in a hurry. For example, if the user is in a hurry, the update unit selects concise training data. The update unit can also select training data that gives the user a sense of security if the user is feeling anxious. For example, if the user is feeling anxious, the update unit selects training data that gives the user a sense of security. In this way, optimal training data can be provided by selecting training data according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, 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.

[0121] The update unit can optimize the learning algorithm by referring to past learning data during updating. The update unit, for example, selects an optimal learning algorithm based on past learning data. For example, the update unit selects an optimal learning algorithm based on past learning data. The update unit can also optimize the learning algorithm by referring to past learning data. For example, the update unit optimizes the learning algorithm by referring to past learning data. The update unit can also improve the learning algorithm by reflecting past learning data. For example, the update unit improves the learning algorithm by reflecting past learning data. In this way, by referring to past learning data, the learning algorithm can be optimized and accuracy can be improved. Some or all of the above-mentioned processing in the update unit may be performed, for example, using AI or without using AI.

[0122] The update unit can update the learning data by reflecting user feedback during updating. The update unit updates the learning data based on, for example, feedback provided by the user. For example, the update unit updates the learning data based on feedback provided by the user. The update unit can also improve the learning data by reflecting user feedback. For example, the update unit improves the learning data by reflecting user feedback. The update unit can also select optimal learning data based on user feedback. For example, the update unit selects optimal learning data based on user feedback. In this way, by reflecting user feedback, the learning data can be optimized and accuracy can be improved. Some or all of the above-described processing in the update unit may be performed, for example, using AI or without using AI.

[0123] The update unit can estimate the user's emotion and adjust the update frequency based on the estimated user's emotion. For example, the update unit increases the update frequency when the user is anxious. For example, the update unit increases the update frequency when the user is anxious. The update unit can also update at a normal update frequency when the user is relaxed. For example, the update unit updates at a normal update frequency when the user is relaxed. The update unit can also update at a frequency that gives the user a sense of security when the user is anxious. For example, the update unit updates at a frequency that gives the user a sense of security when the user is anxious. In this way, by adjusting the update frequency according to the user's emotion, it is possible to update the learning data at an optimal timing. Emotion estimation is realized using an emotion estimation function using, for example, 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 update unit can weight the learning data based on the submission date of the components when updating. The update unit, for example, updates the learning data by placing emphasis on data of recently submitted components. For example, the update unit updates the learning data by placing emphasis on data of recently submitted components. The update unit can also update data of components that have been submitted recently by lowering the weight. For example, the update unit updates data of components that have been submitted recently by lowering the weight. The update unit can also weight the learning data based on the submission date of the components. For example, the update unit weights the learning data based on the submission date of the components. In this way, by weighting the learning data based on the submission date of the components, it is possible to update the learning data by placing emphasis on the most recent information. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI.

[0125] The update unit can integrate information from different data sources to expand the training data during updating. The update unit, for example, integrates information from different data sources to expand the training data. For example, the update unit integrates information from different data sources to expand the training data. The update unit can also update the training data based on information from different data sources. For example, the update unit updates the training data based on information from different data sources. The update unit can also improve the training data by reflecting information from different data sources. For example, the update unit improves the training data by reflecting information from different data sources. In this way, by integrating information from different data sources, the training data can be expanded and its accuracy can be improved. Some or all of the above-described processing in the update unit may be performed, for example, using AI or without using AI.

[0126] The management unit can estimate the user's emotions and adjust the database management method based on the estimated user emotions. For example, if the user is nervous, the management unit provides a simple and highly visible management method. For example, if the user is nervous, the management unit provides a simple and highly visible management method. The management unit can also provide a management method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the management unit provides a management method that includes detailed information. The management unit can also provide a management method that focuses on the main points if the user is in a hurry. For example, if the user is in a hurry, the management unit provides a management method that focuses on the main points. This allows the database management method to be adjusted according to the user's emotions, thereby providing an optimal management method. 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.

[0127] During management, the management unit can select the optimal management method by referring to the past usage history of the database. The management unit, for example, proposes the optimal management method based on the past usage history of the database. For example, the management unit proposes the optimal management method based on the past usage history of the database. The management unit can also preferentially select a specific management method based on the past usage history of the database. For example, the management unit preferentially selects a specific management method based on the past usage history of the database. The management unit can also customize the management method by reflecting the usage history of the database. For example, the management unit customizes the management method by reflecting the usage history of the database. In this way, the optimal management method can be provided by referring to the past usage history of the database. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI.

[0128] During management, the management unit can customize the management means based on the current state of the database. The management unit, for example, proposes the optimal management means based on the current state of the database. For example, the management unit proposes the optimal management means based on the current state of the database. The management unit can also preferentially select a specific management means based on the current state of the database. For example, the management unit preferentially selects a specific management means based on the current state of the database. The management unit can also customize the management means by reflecting the state of the database. For example, the management unit customizes the management means by reflecting the state of the database. In this way, by customizing the management means based on the current state of the database, it is possible to provide an optimal management method. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI.

[0129] The management unit can estimate the user's emotions and determine the management priority of the database based on the estimated user's emotions. For example, when the user is anxious, the management unit prioritizes managing important data. For example, when the user is anxious, the management unit prioritizes managing important data. The management unit can also manage data with normal priority when the user is relaxed. For example, when the user is relaxed, the management unit manages data with normal priority. The management unit can also prioritize managing data that gives a sense of security when the user is anxious. For example, when the user is anxious, the management unit prioritizes managing data that gives a sense of security. In this way, by determining the management priority of the database according to the user's emotions, important data can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0130] During management, the management unit can select an optimal management method by taking into account the geographical location information of the database. The management unit, for example, proposes an optimal management method based on the geographical location information of the database. For example, the management unit proposes an optimal management method based on the geographical location information of the database. The management unit can also select an optimal management method by taking into account the geographical location information of the database. For example, the management unit selects an optimal management method by taking into account the geographical location information of the database. The management unit can also customize the management method by reflecting the geographical location information of the database. For example, the management unit customizes the management method by reflecting the geographical location information of the database. In this way, it is possible to provide an optimal management method by taking into account the geographical location information of the database. Some or all of the above-described processing in the management unit may be performed, for example, using AI, or may be performed without using AI.

[0131] During management, the management unit can improve the management method by referring to the related literature in the database. For example, the management unit proposes an optimal management method based on the related literature in the database. For example, the management unit proposes an optimal management method based on the related literature in the database. The management unit can also improve the management method by taking into account the related literature in the database. For example, the management unit improves the management method by taking into account the related literature in the database. The management unit can also customize the management method by reflecting the related literature in the database. For example, the management unit customizes the management method by reflecting the related literature in the database. In this way, by referring to the related literature in the database, the management method can be improved and an optimal management method can be provided. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI or without using AI.

[0132] The guide unit can estimate the user's emotions and adjust the guidance method for the operation procedures based on the estimated user emotions. For example, when the user is nervous, the guide unit provides a simple and highly visible guidance method. For example, when the user is nervous, the guide unit provides a simple and highly visible guidance method. Furthermore, when the user is relaxed, the guide unit can provide a guidance method that includes detailed information. For example, when the user is relaxed, the guide unit provides a guidance method that includes detailed information. Furthermore, when the user is in a hurry, the guide unit can provide a guidance method that focuses on the main points. For example, when the user is in a hurry, the guide unit provides a guidance method that focuses on the main points. In this way, by adjusting the guidance method for the operation procedures according to the user's emotions, it is possible to provide optimal guidance for the user. 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.

[0133] During guidance, the guide unit can select the optimal guide method by referring to the user's past operation history. The guide unit, for example, suggests the optimal guide method based on guide methods used by the user in the past. For example, the guide unit suggests the optimal guide method based on guide methods used by the user in the past. The guide unit can also preferentially select a specific guide method based on the user's past operation history. For example, the guide unit preferentially selects a specific guide method based on the user's past operation history. The guide unit can also customize the guide method by reflecting the user's operation history. For example, the guide unit customizes the guide method by reflecting the user's operation history. In this way, the optimal guide method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the guide unit may be performed using AI, for example, or may be performed without using AI.

[0134] The guide unit can customize the guide content according to the user's current task during guidance. For example, the guide unit prioritizes providing guide content related to the task the user is currently performing. For example, the guide unit prioritizes providing guide content related to the task the user is currently performing. The guide unit can also suggest optimal guide content based on the user's current task. For example, the guide unit suggests optimal guide content based on the user's current task. The guide unit can also customize the guide content according to the progress of the user's task. For example, the guide unit customizes the guide content according to the progress of the user's task. In this way, by customizing the guide content according to the user's current task, it is possible to provide the optimal guide for the user. Some or all of the above-described processing in the guide unit may be performed using, for example, AI, or may be performed without using AI.

[0135] The guide unit can estimate the user's emotions and determine the priority of operation procedures based on the estimated user emotions. For example, when the user is anxious, the guide unit prioritizes guiding important operation procedures. For example, when the user is anxious, the guide unit prioritizes guiding important operation procedures. Furthermore, when the user is relaxed, the guide unit can also guide operation procedures with normal priority. For example, when the user is relaxed, the guide unit prioritizes guiding operation procedures with normal priority. Furthermore, when the user is anxious, the guide unit can also prioritize guiding operation procedures that give the user a sense of security. For example, when the user is anxious, the guide unit prioritizes guiding operation procedures that give the user a sense of security. In this way, by determining the priority of operation procedures according to the user's emotions, it is possible to prioritize guiding important operation procedures. Emotion estimation is realized using an emotion estimation function using, for example, 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.

[0136] When providing guidance, the guide unit can select the optimal guidance method by taking into consideration device information of the user. For example, if the user is using a smartphone, the guide unit provides a guidance method that matches the screen size. For example, if the user is using a smartphone, the guide unit provides a guidance method that matches the screen size. Furthermore, if the user is using a tablet, the guide unit can also provide a guidance method optimized for a large screen. For example, if the user is using a tablet, the guide unit provides a guidance method optimized for a large screen. Furthermore, if the user is using a smartwatch, the guide unit can also provide a guidance method that is concise and highly visible. For example, if the user is using a smartwatch, the guide unit provides a guidance method that is concise and highly visible. This makes it possible to provide the optimal guidance method by taking into consideration device information of the user. Some or all of the above-described processing in the guide unit may be performed using AI, for example, or may be performed without using AI.

[0137] The guide unit can make the guide content multilingual during guidance in accordance with the user's language setting. The guide unit automatically sets the guide content based on, for example, the language setting of the user's device. For example, the guide unit automatically sets the guide content based on the language setting of the user's device. The guide unit can also provide a language switching function when the user uses multiple languages. For example, the guide unit provides a language switching function when the user uses multiple languages. The guide unit can also provide the guide content in a specific language when the user selects that language. For example, the guide unit provides the guide content in that language when the user selects a specific language. In this way, by making the guide content multilingual in accordance with the user's language setting, it is possible to provide the optimal guide for the user. Some or all of the above-described processing in the guide unit may be performed, for example, using AI or without AI. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, analysis unit, search unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit of the search system is realized by the reception device 38 of the smart device 14. For example, the reception unit receives an image from a user using the touch panel 38A or microphone 38B of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the image using a generative AI to extract component features. For example, the search unit is realized by the specific processing unit 290 of the data processing device 12 and searches the database 24 for a corresponding product name and related information. For example, the provision unit is realized by the output device 40 of the smart device 14 and provides the search results to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, search unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit of the search system is realized by the microphone 238 of the smart glasses 214. For example, the reception unit receives an image from a user using the microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the image using a generative AI to extract component features. For example, the search unit is realized by the specific processing unit 290 of the data processing device 12 and searches the database 24 for a corresponding product name and related information. For example, the provision unit is realized by the speaker 240 of the smart glasses 214 and provides the search results to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit of the search system is realized by the microphone 238 of the headset-type terminal 314. For example, the reception unit receives an image from a user using the microphone 238 of the headset-type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the image using a generative AI to extract component features. For example, the search unit is realized by the specific processing unit 290 of the data processing device 12, and searches the database 24 for a corresponding product name and related information. For example, the provision unit is realized by the display 343 of the headset-type terminal 314, and provides the search results to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit of the search system is realized by the microphone 238 of the robot 414. For example, the reception unit receives an image from a user using the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the image using a generative AI to extract component features. For example, the search unit is realized by the specific processing unit 290 of the data processing device 12 and searches the database 24 for a corresponding product name and related information. For example, the provision unit is realized by the speaker 240 of the robot 414 and provides the search results to the user.

[0138] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0139] The reception unit can analyze the user's past search history and provide optimal search results. For example, the reception unit can prioritize highly relevant search results based on keywords and images the user has searched for in the past. The reception unit can also provide optimal search results for a specific time period based on the user's past search history. Furthermore, the reception unit can prioritize suggesting search methods (keyword search, image search, etc.) that the user has used in the past. In this way, optimal search results can be provided by analyzing the user's past search history.

[0140] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results can be provided. Furthermore, if the user is feeling anxious, analysis results that give a sense of security can be provided. In this way, by adjusting the level of detail of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0141] The search unit can filter search results based on the user's current project or area of ​​interest. For example, only information related to the user's current project can be searched for. Also, the search unit can prioritize highly relevant information based on the user's area of ​​interest. Furthermore, the search unit can filter and search for necessary information according to the progress of the user's project. In this way, highly relevant information can be provided by filtering search results based on the user's current project or area of ​​interest.

[0142] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of information according to the user's emotions, it is possible to provide the user with the most suitable information.

[0143] The management unit can select the optimal management method by referring to the past usage history of the database. For example, it can propose the optimal management method based on the past usage history of the database. It can also preferentially select a specific management method based on the past usage history of the database. Furthermore, it can also customize the management method by reflecting the usage history of the database. In this way, it is possible to provide the optimal management method by referring to the past usage history of the database.

[0144] The guide unit can estimate the user's emotions and adjust the guidance method for the operation procedures based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible guidance method can be provided. If the user is relaxed, a guidance method including detailed information can be provided. Furthermore, if the user is in a hurry, a guidance method that focuses on the main points can be provided. In this way, by adjusting the guidance method for the operation procedures according to the user's emotions, it is possible to provide the optimal guidance for the user.

[0145] The update unit can integrate information from different data sources to expand the training data. For example, information from different data sources is integrated to expand the training data. The update unit can also update the training data based on information from different data sources. Furthermore, the update unit can improve the training data by reflecting information from different data sources. In this way, by integrating information from different data sources, the training data can be expanded and its accuracy can be improved.

[0146] The reception unit can estimate the user's emotions and determine the priority of images to be received based on the estimated user's emotions. For example, if the user is in a hurry, images with a high level of urgency can be received with priority. If the user is relaxed, images can be received with normal priority. Furthermore, if the user is feeling anxious, important images can be received with priority. In this way, by determining the priority of images to be received according to the user's emotions, important images can be received with priority.

[0147] The search unit can perform a search taking into account the market value of the component. For example, it can provide optimal search results based on the market value of the component. It can also improve search accuracy by taking into account the market value of the component. It can also provide highly relevant search results by reflecting the market value of the component. In this way, it is possible to provide highly relevant search results by performing a search taking into account the market value of the component.

[0148] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is anxious, important information can be provided preferentially. If the user is relaxed, detailed information can be provided preferentially. Furthermore, if the user is feeling anxious, information that gives a sense of security can be provided preferentially. In this way, by determining the priority of information according to the user's emotions, important information can be provided preferentially.

[0149] The processing flow of the second embodiment will be briefly explained below.

[0150] Step 1: The reception unit receives images from the user. Images from the user can be in formats such as JPEG, PNG, and GIF. The reception unit can receive images taken with a smartphone, a digital camera, or scanned with a scanner. Step 2: The analysis unit analyzes the image received by the reception unit and extracts the features of the component. These features include edge detection, color histogram, and shape analysis. For example, edge detection technology is used to extract the contours of the image, color histograms are used to analyze the color distribution of the image, and shape analysis technology is used to identify the shape of the image. Step 3: The search unit searches for information from the database based on the features extracted by the analysis unit. The search can be done using keyword search, similar image search, etc. For example, a keyword search can be used to search for the relevant product name from the database, and a similar image search can be used to search for related information. Step 4: The providing unit provides the search results obtained by the search unit to the user. The providing may include displaying text, displaying images, providing links, etc. For example, the search results may be displayed as text, displayed as images, and related links may be provided.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0172] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0188] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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."

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Explanation of symbols]

[0223] 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 reception unit that receives an image from a user; an analysis unit that analyzes the image received by the reception unit and extracts features of the component; a search unit that searches for information from a database based on the features extracted by the analysis unit; a providing unit that provides the search results obtained by the searching unit. A system characterized by:

2. Equipped with an update unit that updates the learning data of the generation AI The system of claim 1 .

3. Equipped with a management department that manages the database The system of claim 1 .

4. Equipped with a guide section that guides the user through the operation procedures The system of claim 1 .

5. The analysis unit Extracting image features and identifying information such as the type, shape, and color of components The system of claim 1 .

6. The search unit Search for relevant product names and related information from a database based on the extracted features The system of claim 1 .

7. The providing unit Providing search results to the user The system of claim 1 .

8. The reception unit Estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions. The system of claim 1 .

9. The reception unit Analyze the user's past image upload history and select the optimal reception method The system of claim 1 .

10. The reception unit Filtering images as they arrive based on the user's current project or area of ​​interest The system of claim 1 .

Images