System

Abstract

An object of a system according to an embodiment is to efficiently collect, summarize, and provide information on a specific area.SOLUTION: A system includes a collection part, an analysis part, a summarization part, and a provision part. The collection unit collects information based on a point designated by a user. The analysis unit analyzes the information collected by the collection unit. The summarizing unit summarizes the information analyzed by the analyzing unit. The providing unit provides the information summarized by the summarizing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently collect, summarize, and provide information about a specific region.

[0005] The system according to the embodiment aims to efficiently collect, summarize, and provide information about a specific region. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects information based on points specified by a user. The analysis unit analyzes the information collected by the collection unit. The summarization unit summarizes the information analyzed by the analysis unit. The provision unit provides the information summarized by the summarization unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect, summarize, and provide information about a specific region. [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) An information provision system according to an embodiment of the present invention collects, analyzes, summarizes, and provides information based on user-specified points. The information provision system collects, analyzes, and summarizes information such as map information and local bulletin boards based on user-specified points, thereby providing the user with an introduction to the area. The information provision system also provides information on shopping conditions, crime prevention, and child-rearing conditions when searching for a home. For example, it provides information such as the locations of nearby supermarkets and convenience stores, the installation status of security cameras, and the number of daycare centers and schools. Furthermore, the information provision system also provides information on local characteristics and the needs of residents when planning a business opening. For example, it collects and summarizes information such as the local population composition, consumption trends, and the locations of competing stores, thereby providing the expected number of visitors and average customer spending. This allows the information provision system to efficiently collect information and make decisions based on the summarized information when searching for a home or planning a business opening. For example, when searching for a home, knowing information about nearby facilities and crime prevention conditions allows the user to choose a home with peace of mind. When planning a business opening, understanding the local characteristics and the needs of residents allows the user to create an effective store opening plan.

[0029] An information provision system according to an embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects information based on points designated by a user. For example, it can collect map information and information from local bulletin boards. For example, the collection unit acquires information about nearby facilities from a map database. The collection unit can also collect word-of-mouth information from residents from local bulletin boards. The analysis unit analyzes the information collected by the collection unit. For example, it can analyze the collected text data using text mining technology. The analysis unit can also analyze the collected image data using image analysis technology. The analysis unit can also analyze the collected audio data using audio analysis technology. The summarization unit summarizes the information analyzed by the analysis unit. For example, it summarizes based on the length of the sentence or the importance of the information to be summarized. For example, the summarization unit concisely summarizes the analysis results using text generation AI (e.g., LLM). The summarization unit can also summarize the analysis results using multimodal generation AI. The provision unit provides the information summarized by the summarization unit to a user. For example, the information can be provided in a text format or a graphical format. The providing unit provides information through, for example, a web application or a mobile application. The providing unit can also provide information in audio format. As a result, the information providing system according to the embodiment can efficiently obtain local information by collecting, analyzing, summarizing, and providing information based on points specified by a user.

[0030] The collection unit can collect map information or information from local bulletin boards. Examples of map information include, but are not limited to, digital maps, paper maps, and GIS data. The collection unit, for example, acquires information about nearby facilities from a map database. The collection unit can also collect word-of-mouth information from residents from local bulletin boards. For example, information is collected from online bulletin boards and physical bulletin boards. By collecting map information and local bulletin board information, detailed information about the area can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input map information acquired from a map database into a generation AI and have the generation AI analyze the map information.

[0031] The analysis unit can analyze the collected information. The collected information includes, but is not limited to, text data, image data, and audio data. The analysis unit can analyze the collected text data using, for example, text mining technology. For example, keywords can be extracted from the text data and the relevance of the information can be analyzed. The analysis unit can also analyze the collected image data using image analysis technology. For example, specific objects can be detected from the image data and the relevance of the information can be analyzed. The analysis unit can also analyze the collected audio data using audio analysis technology. For example, specific phrases can be extracted from the audio data and the relevance of the information can be analyzed. By analyzing the collected information, the meaning and relevance of the information can be understood. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected text data to a generation AI and have the generation AI analyze the text data.

[0032] The summarization unit can summarize the analysis results. Examples of the analysis results include, but are not limited to, statistical data, graphs, text reports, etc. The summarization unit performs summarization based on, for example, the length of the sentences and the importance of the information to be summarized. For example, the summarization unit uses a text generation AI (e.g., LLM) to concisely summarize the analysis results. The summarization unit can also summarize the analysis results using a multimodal generation AI. For example, the summarization unit summarizes the analysis results when the generation AI receives a prompt such as "Please summarize these analysis results." This allows the summarization of the analysis results to provide information in a form that is easy for the user to understand. Some or all of the above-described processing in the summarization unit may be performed using AI, or may be performed without AI. For example, the summarization unit may input the analysis results into the generation AI and have the generation AI perform a summary.

[0033] The providing unit can provide summarized information to a user. Examples of summarized information include, but are not limited to, short text, bullet points, and summary reports. The providing unit can provide information in, for example, text format or graphical format. For example, the providing unit can provide information through a web application or a mobile application. The providing unit can also provide information in audio format. For example, the providing unit can provide the summarized information by audio using speech synthesis technology. This allows the user to efficiently obtain information by providing the summarized information to the user. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the summarized information to a generation AI and have the generation AI execute the information provision format.

[0034] The providing unit can provide information on shopping conditions, crime prevention conditions, and child-rearing conditions during a housing search. Shopping conditions include, but are not limited to, the number of stores, business hours, and product prices. Crime prevention conditions include, but are not limited to, the crime rate and the installation status of security cameras. Child-rearing conditions include, but are not limited to, the number of childcare facilities, the educational environment, and child-rearing support services. The providing unit can provide, for example, the locations of nearby supermarkets and convenience stores. The providing unit can also provide the installation status of security cameras. The providing unit can also provide the number of daycare centers and schools. This allows users to efficiently select a home by providing the information necessary for a housing search. Some or all of the above-described processing by the providing unit can be performed, for example, using AI, or without AI. For example, the providing unit can input the collected information into a generation AI and have the generation AI execute the information provision format.

[0035] The providing unit can provide information on local characteristics and resident needs in the business store opening plan. Local characteristics include, but are not limited to, demographic composition, economic situation, and cultural background. Resident needs information includes, but is not limited to, survey and interview results. The providing unit can provide, for example, the local demographic composition. The providing unit can also provide consumption trends. The providing unit can also provide the locations of competing stores. This allows business owners to create effective store opening plans by providing information necessary for the business store opening plan. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the collected information into a generating AI and have the generating AI execute the information provision format.

[0036] The collection unit can analyze the user's past search history and select an appropriate information collection method. For example, the collection unit prioritizes collecting related information based on keywords that the user has frequently searched for in the past. The collection unit can also prioritize collecting information sources that the user has used in the past. The collection unit can also select information to collect during a specific time period from the user's past search history. This allows more relevant information to be collected by analyzing the user's past search history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past search history data into a generation AI and have the generation AI select the optimal information collection method.

[0037] The collection unit can filter information based on the user's current areas of interest when collecting information. For example, the collection unit prioritizes collecting information related to topics in which the user is currently interested. The collection unit can also filter and collect information related to areas in which the user has expressed interest. The collection unit can also exclude unnecessary information based on the user's current areas of interest. This allows for filtering information based on the user's current areas of interest, thereby providing more relevant information. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data on the user's current areas of interest to a generation AI and have the generation AI perform information filtering.

[0038] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0039] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information about the area where the user is currently located. The collection unit can also prioritize collecting information about an area designated by the user. The collection unit can also collect related event information based on the user's geographical location information. This makes it possible to provide more relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to collect information.

[0040] When collecting information, the collection unit can analyze the user's social media activity and collect highly relevant information. For example, the collection unit collects information on accounts the user follows on social media. The collection unit can also analyze the content of the user's social media posts and collect relevant information. The collection unit can also refer to the activities of the user's friends on social media to collect relevant information. In this way, by analyzing the user's social media activity, more relevant information can be provided. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect information.

[0041] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes collecting information sources that the user has previously rated highly. The collection unit can also adjust the type of information to be collected based on the user's past feedback. The collection unit can also improve the collection method by referring to the user's past feedback. In this way, a more appropriate information collection method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a simplified analysis of information with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. This allows for adjusting the level of detail of the analysis based on the importance of the information, making it possible to provide more appropriate analysis results. 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. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply an algorithm that analyzes the installation status of security cameras to crime prevention information. The analysis unit can also apply an algorithm that analyzes the number of daycare centers and schools to child-rearing information. The analysis unit can also apply an algorithm that analyzes the locations of nearby supermarkets and convenience stores to shopping information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can improve the accuracy of the analysis, for example, by referring to analysis results that the user has previously given high ratings. The analysis unit can also adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also determine the priority of the analysis by referring to 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-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the analysis priority according to the time when the information was collected. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also analyze older information as needed. The analysis unit can also adjust the analysis priority based on the time when the information was collected. This allows the most recent information to be analyzed preferentially by determining the analysis priority based on the time when the information was collected. 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. For example, the analysis unit can input information collection time data to the generation AI and have the generation AI determine the analysis priority.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also determine the order of analysis based on the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, more relevant information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the information to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of knowledge. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user does not have specialized knowledge, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's knowledge level data into a generation AI and have the generation AI use technical terminology.

[0048] The summarization unit can adjust the level of detail of the summary based on the importance of the information when generating a summary. For example, the summarization unit generates a detailed summary for information with high importance. The summarization unit can also generate a simplified summary for information with low importance. The summarization unit can also determine the priority of the summary according to the importance of the information. This allows for adjusting the level of detail of the summary based on the importance of the information, making it possible to provide a more appropriate summary. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, AI. For example, the summarization unit can input information importance data into the generation AI and have the generation AI adjust the level of detail of the summary.

[0049] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of information. For example, the summarization unit can apply an algorithm that summarizes the installation status of security cameras to security information. The summarization unit can also apply an algorithm that summarizes the number of daycare centers and schools to child-rearing information. The summarization unit can also apply an algorithm that summarizes the locations of nearby supermarkets and convenience stores to shopping information. In this way, by applying different summarization algorithms depending on the category of information, more appropriate summaries can be provided. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input information category data into the generation AI and have the generation AI apply the summarization algorithm.

[0050] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. The summarization unit can improve the accuracy of the summary by referring to, for example, summary results that the user has previously given high ratings. The summarization unit can also adjust the summarization algorithm based on the user's past summarization results. The summarization unit can also determine the priority of summarization by referring to the user's past summarization results. In this way, the accuracy of the summary can be improved by referring to the user's past summarization results. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the user's past summary result data into the generation AI and cause the generation AI to improve the accuracy of the summary.

[0051] When generating summaries, the summarization unit can determine the priority of summaries based on the time when the information was collected. For example, the summarization unit prioritizes summarization of the most recent information. The summarization unit can also summarize older information as needed. The summarization unit can also adjust the priority of summaries based on the time when the information was collected. This allows the most recent information to be summarized preferentially by determining the priority of summaries based on the time when the information was collected. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input information collection time data into the generation AI and have the generation AI determine the priority of summaries.

[0052] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the information. For example, the summarization unit prioritizes summarization of highly relevant information. The summarization unit can also postpone summarization of less relevant information. The summarization unit can also determine the order of summaries based on the relevance of the information. In this way, by adjusting the order of summaries based on the relevance of the information, more relevant information can be prioritized for summarization. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input relevance data of information to the generation AI and have the generation AI adjust the order of summaries.

[0053] When generating a summary, the summarization unit can adjust the use of technical terms in the summary according to the user's level of knowledge. For example, if the user has specialized knowledge, the summarization unit can provide a summary that uses a lot of technical terms. Alternatively, if the user does not have specialized knowledge, the summarization unit can provide a summary in simple language. The summarization unit can also adjust the way the summary is expressed according to the user's level of expertise. This allows for the provision of a more understandable summary by adjusting the use of technical terms in the summary according to the user's level of expertise. Some or all of the above-described processing in the summarization unit may be performed using, for example, AI, or may be performed without AI. For example, the summarization unit can input user knowledge level data into the generation AI and have the generation AI execute the use of technical terms.

[0054] When providing information, the providing unit can select the optimal information providing method by referring to the user's past usage history. For example, the providing unit preferentially selects an information providing method that the user has used in the past. The providing unit can also suggest the optimal information providing method based on the user's past usage history. The providing unit can also customize the information providing method based on the user's past usage history. This makes it possible to select a more appropriate information providing method by referring to the user's past usage history. 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. For example, the providing unit can input the user's past usage history data into the generation AI and cause the generation AI to select the optimal information providing method.

[0055] The providing unit can customize the content of information provided based on the user's current needs when providing information. For example, the providing unit can prioritize providing information related to topics in which the user is currently interested. The providing unit can also adjust the content of the information provided based on the user's current needs. The providing unit can also customize the method of providing information according to the user's current needs. This allows more appropriate information to be provided by customizing the content to be provided based on the user's current needs. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current needs data into a generating AI and cause the generating AI to customize the content to be provided.

[0056] The providing unit can improve the information providing method by reflecting user feedback when providing information. For example, the providing unit preferentially selects information providing methods that users have previously rated highly. The providing unit can also improve the information providing method based on user feedback. The providing unit can also adjust the content of the information provided by referring to user feedback. In this way, a more appropriate information providing method can be provided by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the information providing method.

[0057] When providing information, the providing unit can select an appropriate providing method based on the user's geographical location information. For example, the providing unit can prioritize providing information about the area where the user is currently located. The providing unit can also prioritize providing information about an area designated by the user. The providing unit can also provide related event information based on the user's geographical location information. This makes it possible to provide more appropriate information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generating AI and cause the generating AI to select the optimal providing method.

[0058] When providing information, the providing unit can analyze the user's social media activity and customize the provided content to be highly relevant. The providing unit, for example, provides information on accounts the user follows on social media. The providing unit can also analyze the content posted by the user on social media and provide related information. The providing unit can also provide related information by referring to the activities of the user's friends on social media. In this way, more appropriate information can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to customize the provided content.

[0059] The providing unit can customize the information providing method by reflecting the user's past feedback when providing information. For example, the providing unit preferentially selects information providing methods that the user has previously rated highly. The providing unit can also customize the information providing method based on the user's feedback. The providing unit can also adjust the content of the information provided by referring to the user's feedback. In this way, a more appropriate information providing 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 using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's feedback data into the generating AI and cause the generating AI to customize the information providing method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The collection unit can analyze the user's past behavioral patterns and determine the optimal timing for collecting information. For example, if the user has collected information during a specific time period in the past, the collection unit can collect information according to that time period. Also, if the user has collected information on a specific day of the week in the past, the collection unit can collect information according to that day of the week. Furthermore, if the user has collected information before and after a specific event in the past, the collection unit can collect information according to that event. This allows for more effective information collection by optimizing the timing of information collection based on the user's past behavioral patterns.

[0062] The summarization unit can analyze the user's past summarization results and improve the accuracy of the summarization. For example, the summarization unit can improve the accuracy of the summarization by referring to summary results that the user has previously given high ratings. The summarization unit can also adjust the summarization algorithm based on the user's past summarization results. Furthermore, the summarization unit can determine the priority of summarization by referring to the user's past summarization results. In this way, the accuracy of the summarization can be improved by referring to the user's past summarization results.

[0063] The collection unit can collect relevant information in real time based on the user's current location information. For example, if the user is in a specific area, the collection unit can collect the latest information about that area. In addition, if the user is traveling, the collection unit can also collect information about the area to which the user is traveling. Furthermore, if the user is participating in a specific event, the collection unit can also collect information related to the event. In this way, by collecting information in real time based on the user's current location information, more relevant information can be provided.

[0064] The summarization unit can customize the content of the summary based on the user's current areas of interest. For example, it can prioritize summarizing information related to topics that the user is currently interested in. The summarization unit can also summarize information related to regions that the user has expressed interest in. Furthermore, the summarization unit can filter out unnecessary information based on the user's current areas of interest. This allows the summary content to be customized based on the user's current areas of interest, thereby providing more relevant information.

[0065] The providing unit can improve the information providing method by reflecting the user's past feedback. For example, the providing unit can preferentially select information providing methods that the user has previously rated highly. The providing unit can also improve the information providing method based on the user's feedback. Furthermore, the providing unit can also adjust the content of the information provided by referring to the user's feedback. In this way, a more appropriate information providing method can be provided by reflecting the user's past feedback.

[0066] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to analysis results that the user has given high ratings to in the past. The analysis unit can also adjust the analysis algorithm based on the user's past analysis results. Furthermore, the analysis unit can also determine the priority of the analysis by referring to 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.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects information based on points specified by the user. For example, map information and information from local bulletin boards can be collected. The collection unit obtains information about nearby facilities from the map database and collects word-of-mouth information from residents from the local bulletin boards. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected text data using text mining technology, the collected image data using image analysis technology, and the collected voice data using voice analysis technology. Step 3: The summarization unit summarizes the information analyzed by the analysis unit. For example, it summarizes based on the length of the sentence or the importance of the information being summarized. The summarization unit concisely summarizes the analysis results using text generation AI (e.g., LLM) or multimodal generation AI. Step 4: The providing unit provides the information summarized by the summarizing unit to the user. For example, the information may be provided in text format, graphical format, through a web application or a mobile application, or in audio format.

[0069] (Example 2) An information provision system according to an embodiment of the present invention collects, analyzes, summarizes, and provides information based on user-specified points. The information provision system collects, analyzes, and summarizes information such as map information and local bulletin boards based on user-specified points, thereby providing the user with an introduction to the area. The information provision system also provides information on shopping conditions, crime prevention, and child-rearing conditions when searching for a home. For example, it provides information such as the locations of nearby supermarkets and convenience stores, the installation status of security cameras, and the number of daycare centers and schools. Furthermore, the information provision system also provides information on local characteristics and the needs of residents when planning a business opening. For example, it collects and summarizes information such as the local population composition, consumption trends, and the locations of competing stores, thereby providing the expected number of visitors and average customer spending. This allows the information provision system to efficiently collect information and make decisions based on the summarized information when searching for a home or planning a business opening. For example, when searching for a home, knowing information about nearby facilities and crime prevention conditions allows the user to choose a home with peace of mind. When planning a business opening, understanding the local characteristics and the needs of residents allows the user to create an effective store opening plan.

[0070] An information provision system according to an embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects information based on points designated by a user. For example, it can collect map information and information from local bulletin boards. For example, the collection unit acquires information about nearby facilities from a map database. The collection unit can also collect word-of-mouth information from residents from local bulletin boards. The analysis unit analyzes the information collected by the collection unit. For example, it can analyze the collected text data using text mining technology. The analysis unit can also analyze the collected image data using image analysis technology. The analysis unit can also analyze the collected audio data using audio analysis technology. The summarization unit summarizes the information analyzed by the analysis unit. For example, it summarizes based on the length of the sentence or the importance of the information to be summarized. For example, the summarization unit concisely summarizes the analysis results using text generation AI (e.g., LLM). The summarization unit can also summarize the analysis results using multimodal generation AI. The provision unit provides the information summarized by the summarization unit to a user. For example, the information can be provided in a text format or a graphical format. The providing unit provides information through, for example, a web application or a mobile application. The providing unit can also provide information in audio format. As a result, the information providing system according to the embodiment can efficiently obtain local information by collecting, analyzing, summarizing, and providing information based on points specified by a user.

[0071] The collection unit can collect map information or information from local bulletin boards. Examples of map information include, but are not limited to, digital maps, paper maps, and GIS data. The collection unit, for example, acquires information about nearby facilities from a map database. The collection unit can also collect word-of-mouth information from residents from local bulletin boards. For example, information is collected from online bulletin boards and physical bulletin boards. By collecting map information and local bulletin board information, detailed information about the area can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input map information acquired from a map database into a generation AI and have the generation AI analyze the map information.

[0072] The analysis unit can analyze the collected information. The collected information includes, but is not limited to, text data, image data, and audio data. The analysis unit can analyze the collected text data using, for example, text mining technology. For example, keywords can be extracted from the text data and the relevance of the information can be analyzed. The analysis unit can also analyze the collected image data using image analysis technology. For example, specific objects can be detected from the image data and the relevance of the information can be analyzed. The analysis unit can also analyze the collected audio data using audio analysis technology. For example, specific phrases can be extracted from the audio data and the relevance of the information can be analyzed. By analyzing the collected information, the meaning and relevance of the information can be understood. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected text data to a generation AI and have the generation AI analyze the text data.

[0073] The summarization unit can summarize the analysis results. Examples of the analysis results include, but are not limited to, statistical data, graphs, text reports, etc. The summarization unit performs summarization based on, for example, the length of the sentences and the importance of the information to be summarized. For example, the summarization unit uses a text generation AI (e.g., LLM) to concisely summarize the analysis results. The summarization unit can also summarize the analysis results using a multimodal generation AI. For example, the summarization unit summarizes the analysis results when the generation AI receives a prompt such as "Please summarize these analysis results." This allows the summarization of the analysis results to provide information in a form that is easy for the user to understand. Some or all of the above-described processing in the summarization unit may be performed using AI, or may be performed without AI. For example, the summarization unit may input the analysis results into the generation AI and have the generation AI perform a summary.

[0074] The providing unit can provide summarized information to a user. Examples of summarized information include, but are not limited to, short text, bullet points, and summary reports. The providing unit can provide information in, for example, text format or graphical format. For example, the providing unit can provide information through a web application or a mobile application. The providing unit can also provide information in audio format. For example, the providing unit can provide the summarized information by audio using speech synthesis technology. This allows the user to efficiently obtain information by providing the summarized information to the user. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the summarized information to a generation AI and have the generation AI execute the information provision format.

[0075] The providing unit can provide information on shopping conditions, crime prevention conditions, and child-rearing conditions during a housing search. Shopping conditions include, but are not limited to, the number of stores, business hours, and product prices. Crime prevention conditions include, but are not limited to, the crime rate and the installation status of security cameras. Child-rearing conditions include, but are not limited to, the number of childcare facilities, the educational environment, and child-rearing support services. The providing unit can provide, for example, the locations of nearby supermarkets and convenience stores. The providing unit can also provide the installation status of security cameras. The providing unit can also provide the number of daycare centers and schools. This allows users to efficiently select a home by providing the information necessary for a housing search. Some or all of the above-described processing by the providing unit can be performed, for example, using AI, or without AI. For example, the providing unit can input the collected information into a generation AI and have the generation AI execute the information provision format.

[0076] The providing unit can provide information on local characteristics and resident needs in the business store opening plan. Local characteristics include, but are not limited to, demographic composition, economic situation, and cultural background. Resident needs information includes, but is not limited to, survey and interview results. The providing unit can provide, for example, the local demographic composition. The providing unit can also provide consumption trends. The providing unit can also provide the locations of competing stores. This allows business owners to create effective store opening plans by providing information necessary for the business store opening plan. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the collected information into a generating AI and have the generating AI execute the information provision format.

[0077] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can collect information during a time when the user can relax. Furthermore, if the user is excited, the collection unit can immediately start collecting information. Furthermore, if the user is tired, the collection unit can collect information after the user has rested. By adjusting the timing of information collection according to the user's emotions, information can be collected at a more appropriate time. The 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. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0078] The collection unit can analyze the user's past search history and select an appropriate information collection method. For example, the collection unit prioritizes collecting related information based on keywords that the user has frequently searched for in the past. The collection unit can also prioritize collecting information sources that the user has used in the past. The collection unit can also select information to collect during a specific time period from the user's past search history. This allows more relevant information to be collected by analyzing the user's past search history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past search history data into a generation AI and have the generation AI select the optimal information collection method.

[0079] The collection unit can filter information based on the user's current areas of interest when collecting information. For example, the collection unit prioritizes collecting information related to topics in which the user is currently interested. The collection unit can also filter and collect information related to areas in which the user has expressed interest. The collection unit can also exclude unnecessary information based on the user's current areas of interest. This allows for filtering information based on the user's current areas of interest, thereby providing more relevant information. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data on the user's current areas of interest to a generation AI and have the generation AI perform information filtering.

[0080] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0081] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting security information. Furthermore, if the user is excited, the collection unit can prioritize collecting entertainment information. Furthermore, if the user is relaxed, the collection unit can prioritize collecting lifestyle information. This allows for information priority to be determined according to the user's emotions, thereby providing more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the information.

[0082] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information about the area where the user is currently located. The collection unit can also prioritize collecting information about an area designated by the user. The collection unit can also collect related event information based on the user's geographical location information. This makes it possible to provide more relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to collect information.

[0083] When collecting information, the collection unit can analyze the user's social media activity and collect highly relevant information. For example, the collection unit collects information on accounts the user follows on social media. The collection unit can also analyze the content of the user's social media posts and collect relevant information. The collection unit can also refer to the activities of the user's friends on social media to collect relevant information. In this way, by analyzing the user's social media activity, more relevant information can be provided. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect information.

[0084] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes collecting information sources that the user has previously rated highly. The collection unit can also adjust the type of information to be collected based on the user's past feedback. The collection unit can also improve the collection method by referring to the user's past feedback. In this way, a more appropriate information collection method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. The emotion estimation is achieved 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0086] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a simplified analysis of information with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. This allows for adjusting the level of detail of the analysis based on the importance of the information, making it possible to provide more appropriate analysis results. 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. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply an algorithm that analyzes the installation status of security cameras to crime prevention information. The analysis unit can also apply an algorithm that analyzes the number of daycare centers and schools to child-rearing information. The analysis unit can also apply an algorithm that analyzes the locations of nearby supermarkets and convenience stores to shopping information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can improve the accuracy of the analysis, for example, by referring to analysis results that the user has previously given high ratings. The analysis unit can also adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also determine the priority of the analysis by referring to 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-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0089] 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 can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0090] During analysis, the analysis unit can determine the analysis priority according to the time when the information was collected. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also analyze older information as needed. The analysis unit can also adjust the analysis priority based on the time when the information was collected. This allows the most recent information to be analyzed preferentially by determining the analysis priority based on the time when the information was collected. 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. For example, the analysis unit can input information collection time data to the generation AI and have the generation AI determine the analysis priority.

[0091] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also determine the order of analysis based on the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, more relevant information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the information to the generation AI and cause the generation AI to adjust the order of analysis.

[0092] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of knowledge. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user does not have specialized knowledge, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's knowledge level data into a generation AI and have the generation AI use technical terminology.

[0093] The summarization unit can estimate the user's emotions and adjust the summary presentation style based on the estimated user emotions. For example, if the user is nervous, the summarization unit can provide a simple, highly visible summary. The summarization unit can also provide a detailed summary if the user is relaxed. The summarization unit can also provide a summary that focuses on the main points if the user is in a hurry. This allows the summary presentation style to be adjusted according to the user's emotions, thereby providing a more appropriate summary. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the summarization unit can be performed using AI, or without AI. For example, the summarization unit can input the user's emotion data into the generation AI and have the generation AI adjust the summary presentation style.

[0094] The summarization unit can adjust the level of detail of the summary based on the importance of the information when generating a summary. For example, the summarization unit generates a detailed summary for information with high importance. The summarization unit can also generate a simplified summary for information with low importance. The summarization unit can also determine the priority of the summary according to the importance of the information. This allows for adjusting the level of detail of the summary based on the importance of the information, making it possible to provide a more appropriate summary. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, AI. For example, the summarization unit can input information importance data into the generation AI and have the generation AI adjust the level of detail of the summary.

[0095] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of information. For example, the summarization unit can apply an algorithm that summarizes the installation status of security cameras to security information. The summarization unit can also apply an algorithm that summarizes the number of daycare centers and schools to child-rearing information. The summarization unit can also apply an algorithm that summarizes the locations of nearby supermarkets and convenience stores to shopping information. In this way, by applying different summarization algorithms depending on the category of information, more appropriate summaries can be provided. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input information category data into the generation AI and have the generation AI apply the summarization algorithm.

[0096] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. The summarization unit can improve the accuracy of the summary by referring to, for example, summary results that the user has previously given high ratings. The summarization unit can also adjust the summarization algorithm based on the user's past summarization results. The summarization unit can also determine the priority of summarization by referring to the user's past summarization results. In this way, the accuracy of the summary can be improved by referring to the user's past summarization results. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the user's past summary result data into the generation AI and cause the generation AI to improve the accuracy of the summary.

[0097] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated user emotions. For example, if the user is in a hurry, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can provide a detailed summary. If the user is excited, the summarization unit can provide a summary with visually stimulating effects. This allows for a more appropriate summary to be provided by adjusting the length of the summary according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the summarization unit can be performed using, for example, AI, or without AI. For example, the summarization unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the summary.

[0098] When generating summaries, the summarization unit can determine the priority of summaries based on the time when the information was collected. For example, the summarization unit prioritizes summarization of the most recent information. The summarization unit can also summarize older information as needed. The summarization unit can also adjust the priority of summaries based on the time when the information was collected. This allows the most recent information to be summarized preferentially by determining the priority of summaries based on the time when the information was collected. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input information collection time data into the generation AI and have the generation AI determine the priority of summaries.

[0099] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the information. For example, the summarization unit prioritizes summarization of highly relevant information. The summarization unit can also postpone summarization of less relevant information. The summarization unit can also determine the order of summaries based on the relevance of the information. In this way, by adjusting the order of summaries based on the relevance of the information, more relevant information can be prioritized for summarization. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input relevance data of information to the generation AI and have the generation AI adjust the order of summaries.

[0100] When generating a summary, the summarization unit can adjust the use of technical terms in the summary according to the user's level of knowledge. For example, if the user has specialized knowledge, the summarization unit can provide a summary that uses a lot of technical terms. Alternatively, if the user does not have specialized knowledge, the summarization unit can provide a summary in simple language. The summarization unit can also adjust the way the summary is expressed according to the user's level of expertise. This allows for the provision of a more understandable summary by adjusting the use of technical terms in the summary according to the user's level of expertise. Some or all of the above-described processing in the summarization unit may be performed using, for example, AI, or may be performed without AI. For example, the summarization unit can input user knowledge level data into the generation AI and have the generation AI execute the use of technical terms.

[0101] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide information in a simple, highly visible manner. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide information that focuses on the main points. This allows for adjusting the method of providing information according to the user's emotions, thereby providing more appropriate information. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing information.

[0102] When providing information, the providing unit can select the optimal information providing method by referring to the user's past usage history. For example, the providing unit preferentially selects an information providing method that the user has used in the past. The providing unit can also suggest the optimal information providing method based on the user's past usage history. The providing unit can also customize the information providing method based on the user's past usage history. This makes it possible to select a more appropriate information providing method by referring to the user's past usage history. 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. For example, the providing unit can input the user's past usage history data into the generation AI and cause the generation AI to select the optimal information providing method.

[0103] The providing unit can customize the content of information provided based on the user's current needs when providing information. For example, the providing unit can prioritize providing information related to topics in which the user is currently interested. The providing unit can also adjust the content of the information provided based on the user's current needs. The providing unit can also customize the method of providing information according to the user's current needs. This allows more appropriate information to be provided by customizing the content to be provided based on the user's current needs. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current needs data into a generating AI and cause the generating AI to customize the content to be provided.

[0104] The providing unit can improve the information providing method by reflecting user feedback when providing information. For example, the providing unit preferentially selects information providing methods that users have previously rated highly. The providing unit can also improve the information providing method based on user feedback. The providing unit can also adjust the content of the information provided by referring to user feedback. In this way, a more appropriate information providing method can be provided by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the information providing method.

[0105] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can prioritize providing crime prevention information. Furthermore, if the user is excited, the providing unit can prioritize providing entertainment information. Furthermore, if the user is relaxed, the providing unit can prioritize providing lifestyle information. This allows for more appropriate information to be provided by determining the priority of information provision according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information provision.

[0106] When providing information, the providing unit can select an appropriate providing method based on the user's geographical location information. For example, the providing unit can prioritize providing information about the area where the user is currently located. The providing unit can also prioritize providing information about an area designated by the user. The providing unit can also provide related event information based on the user's geographical location information. This makes it possible to provide more appropriate information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generating AI and cause the generating AI to select the optimal providing method.

[0107] When providing information, the providing unit can analyze the user's social media activity and customize the provided content to be highly relevant. The providing unit, for example, provides information on accounts the user follows on social media. The providing unit can also analyze the content posted by the user on social media and provide related information. The providing unit can also provide related information by referring to the activities of the user's friends on social media. In this way, more appropriate information can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to customize the provided content.

[0108] The providing unit can customize the information providing method by reflecting the user's past feedback when providing information. For example, the providing unit preferentially selects information providing methods that the user has previously rated highly. The providing unit can also customize the information providing method based on the user's feedback. The providing unit can also adjust the content of the information provided by referring to the user's feedback. In this way, a more appropriate information providing 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 using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's feedback data into the generating AI and cause the generating AI to customize the information providing method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, summarization unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information using the camera 42 and microphone 38B of the smart device 14, and collects information based on points specified by the user via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the summarized information to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, summarization unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the smart glasses 214, and collects information based on points designated by the user via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the summarized information to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, summarization 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 collection unit collects information using the camera 42 and microphone 238 of the headset type terminal 314, and collects information based on points specified by the user via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the analyzed information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the summarized information to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, summarization 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 collection unit collects information using the camera 42 or microphone 238 of the robot 414, and collects information based on points specified by the user via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the analyzed information. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the summarized information to the user.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The collection unit can analyze the user's past behavioral patterns and determine the optimal timing for collecting information. For example, if the user has collected information during a specific time period in the past, the collection unit can collect information according to that time period. Also, if the user has collected information on a specific day of the week in the past, the collection unit can collect information according to that day of the week. Furthermore, if the user has collected information before and after a specific event in the past, the collection unit can collect information according to that event. This allows for more effective information collection by optimizing the timing of information collection based on the user's past behavioral patterns.

[0111] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, it can provide a concise and to-the-point analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is excited, it can provide a visually appealing analysis result. In this way, by adjusting the depth of the analysis according to the user's emotions, it is possible to provide more appropriate analysis results.

[0112] The summarization unit can analyze the user's past summarization results and improve the accuracy of the summarization. For example, the summarization unit can improve the accuracy of the summarization by referring to summary results that the user has previously given high ratings. The summarization unit can also adjust the summarization algorithm based on the user's past summarization results. Furthermore, the summarization unit can determine the priority of summarization by referring to the user's past summarization results. In this way, the accuracy of the summarization can be improved by referring to the user's past summarization results.

[0113] The providing unit can estimate the user's emotions and adjust the format of information provision based on the estimated user's emotions. For example, if the user is nervous, information can be provided in a simple, highly visible format. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, information that focuses on the main points can be provided. In this way, by adjusting the format of information provision according to the user's emotions, more appropriate information can be provided.

[0114] The collection unit can collect relevant information in real time based on the user's current location information. For example, if the user is in a specific area, the collection unit can collect the latest information about that area. In addition, if the user is traveling, the collection unit can also collect information about the area to which the user is traveling. Furthermore, if the user is participating in a specific event, the collection unit can also collect information related to the event. In this way, by collecting information in real time based on the user's current location information, more relevant information can be provided.

[0115] The analysis unit can estimate the user's emotions and adjust the visual presentation of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible graph or chart can be provided. Alternatively, if the user is relaxed, a complex graph or chart containing detailed data can be provided. Furthermore, if the user is excited, analysis results can be provided with visually appealing effects. In this way, by adjusting the visual presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0116] The summarization unit can customize the content of the summary based on the user's current areas of interest. For example, it can prioritize summarizing information related to topics that the user is currently interested in. The summarization unit can also summarize information related to regions that the user has expressed interest in. Furthermore, the summarization unit can filter out unnecessary information based on the user's current areas of interest. This allows the summary content to be customized based on the user's current areas of interest, thereby providing more relevant information.

[0117] The providing unit can improve the information providing method by reflecting the user's past feedback. For example, the providing unit can preferentially select information providing methods that the user has previously rated highly. The providing unit can also improve the information providing method based on the user's feedback. Furthermore, the providing unit can also adjust the content of the information provided by referring to the user's feedback. In this way, a more appropriate information providing method can be provided by reflecting the user's past feedback.

[0118] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user feels anxious, crime prevention information can be collected with priority. Also, if the user feels excited, entertainment information can be collected with priority. Furthermore, if the user feels relaxed, lifestyle information can be collected with priority. In this way, by determining the priority of information according to the user's emotions, more appropriate information can be provided.

[0119] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to analysis results that the user has given high ratings to in the past. The analysis unit can also adjust the analysis algorithm based on the user's past analysis results. Furthermore, the analysis unit can also determine the priority of the analysis by referring to 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.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The collection unit collects information based on points specified by the user. For example, map information and information from local bulletin boards can be collected. The collection unit obtains information about nearby facilities from the map database and collects word-of-mouth information from residents from the local bulletin boards. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected text data using text mining technology, the collected image data using image analysis technology, and the collected voice data using voice analysis technology. Step 3: The summarization unit summarizes the information analyzed by the analysis unit. For example, it summarizes based on the length of the sentence or the importance of the information being summarized. The summarization unit concisely summarizes the analysis results using text generation AI (e.g., LLM) or multimodal generation AI. Step 4: The providing unit provides the information summarized by the summarizing unit to the user. For example, the information may be provided in text format, graphical format, through a web application or a mobile application, or in audio format.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0136] 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.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information based on points designated by a user; an analysis unit that analyzes the information collected by the collection unit; a summarizing unit that summarizes the information analyzed by the analyzing unit; a providing unit that provides the information summarized by the summarizing unit; Equipped with A system characterized by:

2. The collecting unit Collect map information or local bulletin board information 2. The system of claim 1.

3. The analysis unit Analyzing the collected information 2. The system of claim 1.

4. The summary section Summarize the analysis results 2. The system of claim 1.

5. The providing unit Providing summarized information to users 2. The system of claim 1.

6. The providing unit Providing information on shopping, crime prevention, and child-rearing when searching for a home 2. The system of claim 1.

7. The providing unit Providing information on local characteristics and residents' needs when planning business openings 2. The system of claim 1.

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

2. The system of claim 1.

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