System

Abstract

An object of the system according to the embodiment is to automate acquisition of Web information and time-series analysis thereof.SOLUTION: A system according to an embodiment includes an acquisition unit, an accumulation unit, an analysis unit, and a time-series analysis unit. The acquisition unit acquires Web information. The accumulation unit accumulates the data acquired by the acquisition unit. The analysis unit compares and analyzes the data accumulated by the accumulation unit. The time series analysis unit analyzes the result obtained by the analysis unit in a time series.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 require manual acquisition of web information and time-series analysis, which makes them inefficient and difficult to implement.

[0005] The system according to the embodiment aims to automate the acquisition of web information and its time-series analysis. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a storage unit, an analysis unit, and a time series analysis unit. The acquisition unit acquires web information. The storage unit stores the data acquired by the acquisition unit. The analysis unit compares and analyzes the data stored by the storage unit. The time series analysis unit analyzes the results obtained by the analysis unit in time series. [Effects of the Invention]

[0007] The system according to the embodiment can automate the acquisition of web information and its time-series analysis. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A data collection and analysis system according to an embodiment of the present invention automatically acquires web information, performs comparative analysis using a generation AI, and analyzes the data over time. This system automatically acquires text data from web pages, converts it into text data, and automatically stores it. For example, it can periodically collect information from specific news sites or blogs. Next, dedicated prompts are assigned to the generation AI, which then performs comparative analysis using the acquired data. The generation AI performs comparative analysis of the data based on the specified prompts and stores the results as output. For example, it can compare the content of news articles collected at different times and analyze changes and trends. Finally, the accumulated time-series data is analyzed. This allows for understanding changes and trends in data over a long period of time. For example, it can analyze the frequency and content of news reports on a specific topic over time. This mechanism allows for efficient acquisition and comparative analysis of web information, which would be difficult and cumbersome to achieve manually. This allows the data collection and analysis system to efficiently acquire, store, compare, and analyze web information over time.

[0029] A data collection and analysis system according to an embodiment includes an acquisition unit, a storage unit, an analysis unit, and a time series analysis unit. The acquisition unit acquires web information. Examples of web information include, but are not limited to, news articles, blog posts, and social media posts. The acquisition unit acquires information from web pages using, for example, scraping technology. The acquisition unit can also acquire data using an API. For example, the acquisition unit periodically collects information from specific news sites. The storage unit stores the data acquired by the acquisition unit. The storage unit saves the data in, for example, a database. Examples of database types include, but are not limited to, relational databases and NoSQL databases. The storage unit can automatically store data. For example, the storage unit periodically stores data using a scheduling function. The storage unit can also store data based on a trigger event. The analysis unit compares and analyzes the data stored by the storage unit. The analysis unit compares and analyzes the data using a generation AI. Examples of generation AI include, but are not limited to, models such as GPT-4 (registered trademark) and Gemini. The generation AI performs comparative analysis of data based on specified prompts. For example, the generation AI compares the content of news articles collected at different times and analyzes changes and trends. The time series analysis unit analyzes the results obtained by the analysis unit in time series. The time series analysis unit analyzes changes in data, for example, using a time series data model. The time series analysis unit can grasp changes and trends in data over a long period of time. For example, the time series analysis unit analyzes changes in the frequency and content of news reports on a specific topic in time series. This allows the data collection and analysis system according to the embodiment to efficiently acquire, accumulate, compare, analyze, and time series analyze web information.

[0030] The acquisition unit can acquire information from a specific website. The acquisition unit acquires information from the specific website. Examples of specific websites include, but are not limited to, news sites and specialized blogs. For example, the acquisition unit periodically collects information from a specific news site. The acquisition unit can also acquire information from a specific specialized blog. For example, the acquisition unit specifies the URL of a specific website and acquires information from that site. This allows information to be acquired efficiently from a specific website.

[0031] The analysis unit can perform comparative analysis of data using the generative AI. The analysis unit performs comparative analysis of data using the generative AI. Examples of the generative AI include, but are not limited to, models such as GPT-4 and Gemini. The generative AI performs comparative analysis of data based on specified prompts. For example, the generative AI compares the content of news articles collected at different points in time and analyzes changes and trends. The generative AI can also calculate the similarity of data and calculate a similarity score. For example, the generative AI analyzes the content of news articles and performs comparative analysis of data based on the similarity score. This allows the use of the generative AI to efficiently perform comparative analysis of data.

[0032] The time series analysis unit can analyze changes and trends in data over a long period of time. The time series analysis unit analyzes changes and trends in data over a long period of time. The time series analysis unit analyzes changes in data, for example, using a time series data model. The time series analysis unit can grasp changes and trends in data over a long period of time. For example, the time series analysis unit analyzes changes in the frequency and content of news reports on a specific topic over time. The time series analysis unit can also predict changes in data. For example, the time series analysis unit predicts future trends based on past data. This makes it possible to grasp changes and trends in data over a long period of time.

[0033] The storage unit can automatically store the acquired data. The storage unit automatically stores the acquired data. The storage unit, for example, saves the data in a database. Types of databases include, but are not limited to, relational databases and NoSQL databases. The storage unit can automatically store the data. For example, the storage unit stores data periodically using a scheduling function. The storage unit can also store data based on a trigger event. This enables automatic data storage.

[0034] The analysis unit can compare data collected at different points in time and analyze changes and trends. The analysis unit compares data collected at different points in time and analyzes changes and trends. The analysis unit performs comparative analysis of data using, for example, a generative AI. Examples of generative AI include, but are not limited to, models such as GPT-4 and Gemini. The generative AI performs comparative analysis of data based on specified prompts. For example, the generative AI compares the content of news articles collected at different points in time and analyzes changes and trends. The generative AI can also calculate the similarity of data and calculate a similarity score. For example, the generative AI analyzes the content of news articles and performs comparative analysis of data based on the similarity score. This makes it possible to understand changes and trends in data collected at different points in time.

[0035] When acquiring information from a specific website, the acquisition unit can select an acquisition target by taking into consideration the user's past browsing history. When acquiring information from a specific website, the acquisition unit selects an acquisition target by taking into consideration the user's past browsing history. The acquisition unit, for example, preferentially acquires information from websites that the user has frequently visited in the past. The acquisition unit can also analyze the user's past browsing history and preferentially acquire highly relevant information. Furthermore, the acquisition unit can also acquire information from websites related to topics in which the user has shown interest in the past. This makes it possible to acquire highly relevant information by taking into consideration the user's past browsing history.

[0036] The acquisition unit can perform filtering based on the user's current areas of interest when acquiring web information. The acquisition unit can perform filtering based on the user's current areas of interest when acquiring web information. For example, the acquisition unit acquires only information related to topics in which the user is currently interested. The acquisition unit can also filter and acquire highly relevant information based on the user's current search history. Furthermore, the acquisition unit can customize the information to be acquired based on the areas of interest set by the user. This makes it possible to acquire highly relevant information based on the user's current areas of interest.

[0037] When acquiring web information, the acquisition unit can select an acquisition means based on specific criteria according to the user's input method. When acquiring web information, the acquisition unit selects an acquisition means based on specific criteria according to the user's input method. For example, if the user uses voice input, the acquisition unit can acquire information using voice recognition technology. Also, if the user uses text input, the acquisition unit can acquire information using text analysis technology. Furthermore, if the user uses image input, the acquisition unit can acquire information using image recognition technology. This makes it possible to select the optimal acquisition means according to the user's input method.

[0038] When acquiring web information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. When acquiring web information, the acquisition unit prioritizes acquiring highly relevant information taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring information related to the area where the user is currently located. The acquisition unit can also acquire highly relevant information based on the user's past location information. Furthermore, the acquisition unit can customize the information to be acquired based on location information set by the user. This makes it possible to acquire highly relevant information taking into account the user's geographical location information.

[0039] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring web information. The acquisition unit can analyze the user's social media activity and acquire related information when acquiring web information. The acquisition unit can acquire information from accounts the user follows on social media, for example. The acquisition unit can also analyze the content of the user's posts on social media and acquire related information. Furthermore, the acquisition unit can also acquire related information by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be acquired.

[0040] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring web information. The acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring web information. For example, the acquisition unit preferentially acquires information from information sources that the user has previously rated highly. The acquisition unit can also analyze the user's past feedback and improve the quality of the acquired information. Furthermore, the acquisition unit can optimize the acquisition method based on feedback the user has provided in the past. This allows the acquisition method to be optimized by reflecting the user's past feedback.

[0041] The storage unit can determine the storage priority based on the importance of the data when storing the data. The storage unit determines the storage priority based on the importance of the data when storing the data. For example, the storage unit preferentially stores data with high importance. The storage unit can also analyze the importance of the data and optimize the order of storage. Furthermore, the storage unit can store data with low importance at a later date. In this way, the storage priority can be determined based on the importance of the data.

[0042] The storage unit can apply different storage algorithms depending on the category of data when storing the data. The storage unit applies different storage algorithms depending on the category of data when storing the data. For example, the storage unit applies a storage algorithm dedicated to text to text data. The storage unit can also apply a storage algorithm dedicated to images to image data. Furthermore, the storage unit can apply a storage algorithm dedicated to audio to audio data. This makes it possible to apply the optimal storage algorithm depending on the category of data.

[0043] The storage unit can improve the accuracy of storage by referring to the user's past storage results when storing data. The storage unit can improve the accuracy of storage by referring to the user's past storage results when storing data. The storage unit, for example, analyzes data stored by the user in the past and improves the accuracy of storage. The storage unit can also suggest an optimal storage method based on the user's past storage results. Furthermore, the storage unit can also improve the accuracy of storage by referring to feedback provided by the user in the past. In this way, the accuracy of storage is improved by referring to the user's past storage results.

[0044] The storage unit can determine the storage priority based on the time of data submission when storing data. The storage unit can determine the storage priority based on the time of data submission when storing data. The storage unit, for example, stores the most recent data preferentially. The storage unit can also analyze the time of data submission and optimize the order of storage. Furthermore, the storage unit can store data that was submitted earlier at a later date. This allows the storage priority to be determined based on the time of data submission.

[0045] The storage unit can adjust the order of storage based on the relevance of the data when storing the data. The storage unit adjusts the order of storage based on the relevance of the data when storing the data. For example, the storage unit preferentially stores highly relevant data. The storage unit can also analyze the relevance of the data and optimize the order of storage. Furthermore, the storage unit can store less relevant data later. This makes it possible to optimize the order of storage based on the relevance of the data.

[0046] The storage unit can adjust the storage method according to the user's level of expertise when storing data. The storage unit can adjust the storage method according to the user's level of expertise when storing data. For example, the storage unit stores detailed data for users with high levels of expertise. The storage unit can also store concise data for users with low levels of expertise. Furthermore, the storage unit can analyze the user's level of expertise and suggest the optimal storage method. This makes it possible to suggest the optimal storage method according to the user's level of expertise.

[0047] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also analyze the importance of the data and optimize the level of detail of the analysis. Furthermore, the analysis unit can perform a brief analysis on data with low importance. This makes it possible to optimize the level of detail of the analysis based on the importance of the data.

[0048] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies an analysis algorithm dedicated to text to text data. The analysis unit can also apply an analysis algorithm dedicated to images to image data. Furthermore, the analysis unit can apply an analysis algorithm dedicated to audio to audio data. This makes it possible to apply the optimal analysis algorithm depending on the category of data.

[0049] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit improves the accuracy of the analysis, for example, based on analysis results performed by the user in the past. The analysis unit can also suggest an optimal analysis method by referring to the user's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by referring to feedback provided by the user in the past. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0050] The analysis unit can determine the priority of analysis based on the time of data submission during analysis. The analysis unit can determine the priority of analysis based on the time of data submission during analysis. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also analyze the time of data submission and optimize the order of analysis. Furthermore, the analysis unit can postpone analysis of data that was submitted earlier. This makes it possible to determine the priority of analysis based on the time of data submission.

[0051] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also analyze the relevance of the data and optimize the order of analysis. Furthermore, the analysis unit can postpone analysis of less relevant data. In this way, the order of analysis can be optimized based on the relevance of the data.

[0052] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during the analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during the analysis. For example, the analysis unit can use detailed technical terms for users with high levels of expertise. The analysis unit can also use concise terms for users with low levels of expertise. Furthermore, the analysis unit can analyze the user's level of expertise and suggest optimal terms. This makes it possible to suggest optimal terms according to the user's level of expertise.

[0053] During time series analysis, the time series analysis unit can predict current data by referring to past time series data. During time series analysis, the time series analysis unit predicts current data by referring to past time series data. The time series analysis unit, for example, predicts current trends based on past data. The time series analysis unit can also predict changes in current data by referring to past data. Furthermore, the time series analysis unit can analyze past data and predict future data. This makes it possible to predict current trends based on past data.

[0054] The time series analysis unit can apply different time series analysis methods to each data category during time series analysis. The time series analysis unit applies different time series analysis methods to each data category during time series analysis. For example, the time series analysis unit applies a time series analysis method dedicated to text to text data. The time series analysis unit can also apply a time series analysis method dedicated to images to image data. Furthermore, the time series analysis unit can also apply a time series analysis method dedicated to audio to audio data. This makes it possible to apply the most appropriate time series analysis method depending on the data category.

[0055] The time series analysis unit can analyze the time series taking into account the attribute information of the data submitter during the time series analysis. The time series analysis unit analyzes the time series taking into account the attribute information of the data submitter during the time series analysis. The time series analysis unit performs the time series analysis based on, for example, the attribute information of the data submitter. The time series analysis unit can also analyze changes in the time series data by referring to the attribute information of the data submitter. Furthermore, the time series analysis unit can analyze trends in the time series data taking into account the attribute information of the data submitter. This makes it possible to analyze changes in the time series data taking into account the attribute information of the data submitter.

[0056] The time series analysis unit can analyze time series changes based on the time of data submission during time series analysis. The time series analysis unit analyzes time series changes based on the time of data submission during time series analysis. The time series analysis unit analyzes time series changes based on, for example, the time series submission time. The time series analysis unit can also prioritize analysis of data that was submitted recently. Furthermore, the time series analysis unit can also postpone analysis of data that was submitted earlier. This makes it possible to analyze time series changes based on the time of data submission.

[0057] The time series analysis unit can analyze the time series by referring to market data related to the data during the time series analysis. The time series analysis unit analyzes the time series by referring to market data related to the data during the time series analysis. The time series analysis unit, for example, analyzes changes in the time series based on the related market data. The time series analysis unit can also analyze trends in the time series data by referring to market data. Furthermore, the time series analysis unit can analyze changes in the time series data by taking the related market data into consideration. This makes it possible to analyze changes in the time series based on the related market data.

[0058] The time series analysis unit can analyze the time series taking into account the technological maturity of the data when analyzing the time series. The time series analysis unit analyzes the time series taking into account the technological maturity of the data when analyzing the time series. The time series analysis unit analyzes changes in the time series, for example, based on the technological maturity of the data. The time series analysis unit can also analyze trends in the time series data by referring to the technological maturity. Furthermore, the time series analysis unit can analyze changes in the time series data taking into account the technological maturity of the data. This makes it possible to analyze changes in the time series based on the technological maturity of the data.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The storage unit can also determine the priority of storage based on the importance of the data. For example, it can store data with high importance first, and store data with low importance later. It can also analyze the importance of data and optimize the order of storage. Furthermore, it can automatically delete data with low importance after a certain period of time. This enables efficient data management based on the importance of the data.

[0061] The time series analysis unit can also apply different time series analysis methods to different data categories. For example, a text-specific time series analysis method can be applied to text data, and an image-specific time series analysis method can be applied to image data. It can also apply an audio-specific time series analysis method to audio data. This enables optimal time series analysis according to the data category.

[0062] The acquisition unit can also prioritize acquisition of highly relevant information taking into account the user's geographical location information. For example, it can prioritize acquisition of news and event information related to the user's current location. It can also acquire highly relevant information based on the user's past location information. Furthermore, it can customize the information to be acquired based on the location information set by the user. This makes it possible to provide information taking into account the user's geographical location information.

[0063] The storage unit can also apply different storage algorithms depending on the data category. For example, a storage algorithm specifically for text data can be applied to text data, and a storage algorithm specifically for images can be applied to image data. It can also apply a storage algorithm specifically for audio data. This allows for optimal storage depending on the data category.

[0064] The acquisition unit can also analyze the user's social media activity and acquire related information. For example, it can acquire information from accounts the user follows on social media. It can also analyze the content posted by the user on social media to acquire related information. It can also acquire related information by referring to the activities of the user's friends on social media. This makes it possible to provide information that takes the user's social media activity into consideration.

[0065] The analysis department can also determine the priority of analysis based on the time of data submission. For example, the latest data can be analyzed first, and older data can be left for later. The analysis order can also be optimized by analyzing the time of data submission. Furthermore, older data can be automatically deleted after a certain period of time. This allows for efficient analysis based on the time of data submission.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The acquisition unit acquires web information. Web information includes news articles, blog posts, social media posts, etc. The acquisition unit acquires information from web pages using scraping technology or APIs. For example, the acquisition unit periodically collects information from a specific news site. Step 2: The storage unit stores the data acquired by the acquisition unit. The storage unit saves the data in a database. The type of database includes relational databases and NoSQL databases. The storage unit automatically stores data based on scheduling functions and trigger events. Step 3: The analysis unit compares and analyzes the data accumulated by the accumulation unit. The analysis unit uses generation AI to compare and analyze the data. The generation AI includes models such as GPT-4 and Gemini, and performs comparative analysis of the data based on specified prompts. For example, it compares the content of news articles collected at different times and analyzes changes and trends. Step 4: The time series analysis unit analyzes the results obtained by the analysis unit in a time series. The time series analysis unit uses a time series data model to analyze changes in the data. For example, it analyzes changes in the frequency and content of news reports on a specific topic over time to understand changes and trends in the data over a long period of time.

[0068] (Example 2) A data collection and analysis system according to an embodiment of the present invention automatically acquires web information, performs comparative analysis using a generation AI, and analyzes the data over time. This system automatically acquires text data from web pages, converts it into text data, and automatically stores it. For example, it can periodically collect information from specific news sites or blogs. Next, dedicated prompts are assigned to the generation AI, which then performs comparative analysis using the acquired data. The generation AI performs comparative analysis of the data based on the specified prompts and stores the results as output. For example, it can compare the content of news articles collected at different times and analyze changes and trends. Finally, the accumulated time-series data is analyzed. This allows for understanding changes and trends in data over a long period of time. For example, it can analyze the frequency and content of news reports on a specific topic over time. This mechanism allows for efficient acquisition and comparative analysis of web information, which would be difficult and cumbersome to achieve manually. This allows the data collection and analysis system to efficiently acquire, store, compare, and analyze web information over time.

[0069] A data collection and analysis system according to an embodiment includes an acquisition unit, a storage unit, an analysis unit, and a time series analysis unit. The acquisition unit acquires web information. Examples of web information include, but are not limited to, news articles, blog posts, and social media posts. The acquisition unit acquires information from web pages using, for example, scraping technology. The acquisition unit can also acquire data using an API. For example, the acquisition unit periodically collects information from specific news sites. The storage unit stores the data acquired by the acquisition unit. The storage unit stores the data in, for example, a database. Examples of database types include, but are not limited to, relational databases and NoSQL databases. The storage unit can automatically store data. For example, the storage unit periodically stores data using a scheduling function. The storage unit can also store data based on a trigger event. The analysis unit compares and analyzes the data stored by the storage unit. The analysis unit compares and analyzes the data using a generation AI. Examples of generation AI include, but are not limited to, models such as GPT-4 and Gemini. The generation AI performs comparative analysis of data based on specified prompts. For example, the generation AI compares the content of news articles collected at different times and analyzes changes and trends. The time series analysis unit analyzes the results obtained by the analysis unit in time series. The time series analysis unit analyzes changes in data, for example, using a time series data model. The time series analysis unit can grasp changes and trends in data over a long period of time. For example, the time series analysis unit analyzes changes in the frequency and content of news reports on a specific topic in time series. This allows the data collection and analysis system according to the embodiment to efficiently acquire, accumulate, compare, analyze, and time series analyze web information.

[0070] The acquisition unit can acquire information from a specific website. The acquisition unit acquires information from the specific website. Examples of specific websites include, but are not limited to, news sites and specialized blogs. For example, the acquisition unit periodically collects information from a specific news site. The acquisition unit can also acquire information from a specific specialized blog. For example, the acquisition unit specifies the URL of a specific website and acquires information from that site. This allows information to be acquired efficiently from a specific website.

[0071] The analysis unit can perform comparative analysis of data using the generative AI. The analysis unit performs comparative analysis of data using the generative AI. Examples of the generative AI include, but are not limited to, models such as GPT-4 and Gemini. The generative AI performs comparative analysis of data based on specified prompts. For example, the generative AI compares the content of news articles collected at different points in time and analyzes changes and trends. The generative AI can also calculate the similarity of data and calculate a similarity score. For example, the generative AI analyzes the content of news articles and performs comparative analysis of data based on the similarity score. This allows the use of the generative AI to efficiently perform comparative analysis of data.

[0072] The time series analysis unit can analyze changes and trends in data over a long period of time. The time series analysis unit analyzes changes and trends in data over a long period of time. The time series analysis unit analyzes changes in data, for example, using a time series data model. The time series analysis unit can grasp changes and trends in data over a long period of time. For example, the time series analysis unit analyzes changes in the frequency and content of news reports on a specific topic over time. The time series analysis unit can also predict changes in data. For example, the time series analysis unit predicts future trends based on past data. This makes it possible to grasp changes and trends in data over a long period of time.

[0073] The storage unit can automatically store the acquired data. The storage unit automatically stores the acquired data. The storage unit, for example, saves the data in a database. Types of databases include, but are not limited to, relational databases and NoSQL databases. The storage unit can automatically store the data. For example, the storage unit stores data periodically using a scheduling function. The storage unit can also store data based on a trigger event. This enables automatic data storage.

[0074] The analysis unit can compare data collected at different points in time and analyze changes and trends. The analysis unit compares data collected at different points in time and analyzes changes and trends. The analysis unit performs comparative analysis of data using, for example, a generative AI. Examples of generative AI include, but are not limited to, models such as GPT-4 and Gemini. The generative AI performs comparative analysis of data based on specified prompts. For example, the generative AI compares the content of news articles collected at different points in time and analyzes changes and trends. The generative AI can also calculate the similarity of data and calculate a similarity score. For example, the generative AI analyzes the content of news articles and performs comparative analysis of data based on the similarity score. This makes it possible to understand changes and trends in data collected at different points in time.

[0075] The data collection and analysis system further includes an acquisition unit that estimates a user's emotions and adjusts the timing of web information acquisition based on the estimated user emotions. The acquisition unit estimates the user's emotions and adjusts the timing of web information acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit reduces the frequency of web information acquisition to reduce the user's burden. The acquisition unit can also increase the frequency of web information acquisition to collect more information if the user is relaxed. Furthermore, if the user is in a hurry, the acquisition unit can prioritize acquiring only important information. This allows the timing of web information acquisition to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] When acquiring information from a specific website, the acquisition unit can select an acquisition target by taking into consideration the user's past browsing history. When acquiring information from a specific website, the acquisition unit selects an acquisition target by taking into consideration the user's past browsing history. The acquisition unit, for example, preferentially acquires information from websites that the user has frequently visited in the past. The acquisition unit can also analyze the user's past browsing history and preferentially acquire highly relevant information. Furthermore, the acquisition unit can also acquire information from websites related to topics in which the user has shown interest in the past. This makes it possible to acquire highly relevant information by taking into consideration the user's past browsing history.

[0077] The acquisition unit can perform filtering based on the user's current areas of interest when acquiring web information. The acquisition unit can perform filtering based on the user's current areas of interest when acquiring web information. For example, the acquisition unit acquires only information related to topics in which the user is currently interested. The acquisition unit can also filter and acquire highly relevant information based on the user's current search history. Furthermore, the acquisition unit can customize the information to be acquired based on the areas of interest set by the user. This makes it possible to acquire highly relevant information based on the user's current areas of interest.

[0078] When acquiring web information, the acquisition unit can select an acquisition means based on specific criteria according to the user's input method. When acquiring web information, the acquisition unit selects an acquisition means based on specific criteria according to the user's input method. For example, if the user uses voice input, the acquisition unit can acquire information using voice recognition technology. Also, if the user uses text input, the acquisition unit can acquire information using text analysis technology. Furthermore, if the user uses image input, the acquisition unit can acquire information using image recognition technology. This makes it possible to select the optimal acquisition means according to the user's input method.

[0079] The acquisition unit can estimate the user's emotions and determine the priority of the web information to be acquired based on the estimated user emotions. The acquisition unit can estimate the user's emotions and determine the priority of the web information to be acquired based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring information of high importance. The acquisition unit can also acquire a wide range of information when the user is relaxed. Furthermore, when the user is in a hurry, the acquisition unit can prioritize information that can be acquired quickly. This makes it possible to determine the priority of the web information to be acquired according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] When acquiring web information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. When acquiring web information, the acquisition unit prioritizes acquiring highly relevant information taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring information related to the area where the user is currently located. The acquisition unit can also acquire highly relevant information based on the user's past location information. Furthermore, the acquisition unit can customize the information to be acquired based on location information set by the user. This makes it possible to acquire highly relevant information taking into account the user's geographical location information.

[0081] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring web information. The acquisition unit can analyze the user's social media activity and acquire related information when acquiring web information. The acquisition unit can acquire information from accounts the user follows on social media, for example. The acquisition unit can also analyze the content of the user's posts on social media and acquire related information. Furthermore, the acquisition unit can also acquire related information by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be acquired.

[0082] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring web information. The acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring web information. For example, the acquisition unit preferentially acquires information from information sources that the user has previously rated highly. The acquisition unit can also analyze the user's past feedback and improve the quality of the acquired information. Furthermore, the acquisition unit can optimize the acquisition method based on feedback the user has provided in the past. This allows the acquisition method to be optimized by reflecting the user's past feedback.

[0083] The storage unit can estimate the user's emotions and adjust the data storage method based on the estimated user emotions. The storage unit can estimate the user's emotions and adjust the data storage method based on the estimated user emotions. For example, if the user is feeling stressed, the storage unit can reduce the data storage frequency to reduce the burden. Also, if the user is relaxed, the storage unit can increase the data storage frequency to store more information. Furthermore, if the user is in a hurry, the storage unit can prioritize storing only important data. This makes it possible to adjust the data storage method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The storage unit can determine the storage priority based on the importance of the data when storing the data. The storage unit determines the storage priority based on the importance of the data when storing the data. For example, the storage unit preferentially stores data with high importance. The storage unit can also analyze the importance of the data and optimize the order of storage. Furthermore, the storage unit can store data with low importance at a later date. In this way, the storage priority can be determined based on the importance of the data.

[0085] The storage unit can apply different storage algorithms depending on the category of data when storing the data. The storage unit applies different storage algorithms depending on the category of data when storing the data. For example, the storage unit applies a storage algorithm dedicated to text to text data. The storage unit can also apply a storage algorithm dedicated to images to image data. Furthermore, the storage unit can apply a storage algorithm dedicated to audio to audio data. This makes it possible to apply the optimal storage algorithm depending on the category of data.

[0086] The storage unit can improve the accuracy of storage by referring to the user's past storage results when storing data. The storage unit can improve the accuracy of storage by referring to the user's past storage results when storing data. The storage unit, for example, analyzes data stored by the user in the past and improves the accuracy of storage. The storage unit can also suggest an optimal storage method based on the user's past storage results. Furthermore, the storage unit can also improve the accuracy of storage by referring to feedback provided by the user in the past. In this way, the accuracy of storage is improved by referring to the user's past storage results.

[0087] The storage unit can estimate the user's emotions and adjust the data storage frequency based on the estimated user emotions. The storage unit can estimate the user's emotions and adjust the data storage frequency based on the estimated user emotions. For example, if the user is feeling stressed, the storage unit can reduce the data storage frequency to reduce the burden. Also, if the user is relaxed, the storage unit can increase the data storage frequency to store more information. Furthermore, if the user is in a hurry, the storage unit can prioritize storing only important data. This makes it possible to adjust the data storage frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The storage unit can determine the storage priority based on the time of data submission when storing data. The storage unit can determine the storage priority based on the time of data submission when storing data. The storage unit, for example, stores the most recent data preferentially. The storage unit can also analyze the time of data submission and optimize the order of storage. Furthermore, the storage unit can store data that was submitted earlier at a later date. This allows the storage priority to be determined based on the time of data submission.

[0089] The storage unit can adjust the order of storage based on the relevance of the data when storing the data. The storage unit adjusts the order of storage based on the relevance of the data when storing the data. For example, the storage unit preferentially stores highly relevant data. The storage unit can also analyze the relevance of the data and optimize the order of storage. Furthermore, the storage unit can store less relevant data later. This makes it possible to optimize the order of storage based on the relevance of the data.

[0090] The storage unit can adjust the storage method according to the user's level of expertise when storing data. The storage unit can adjust the storage method according to the user's level of expertise when storing data. For example, the storage unit stores detailed data for users with high levels of expertise. The storage unit can also store concise data for users with low levels of expertise. Furthermore, the storage unit can analyze the user's level of expertise and suggest the optimal storage method. This makes it possible to suggest the optimal storage method according to the user's level of expertise.

[0091] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. For example, if the user is feeling stressed, the analysis unit can provide a simple way of presenting the analysis. If the user is relaxed, the analysis unit can also provide a detailed way of presenting the analysis. Furthermore, if the user is in a hurry, the analysis unit can also provide a way of presenting the analysis that focuses on the main points. This makes it possible to adjust the way the analysis is presented based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also analyze the importance of the data and optimize the level of detail of the analysis. Furthermore, the analysis unit can perform a brief analysis on data with low importance. This makes it possible to optimize the level of detail of the analysis based on the importance of the data.

[0093] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies an analysis algorithm dedicated to text to text data. The analysis unit can also apply an analysis algorithm dedicated to images to image data. Furthermore, the analysis unit can apply an analysis algorithm dedicated to audio to audio data. This makes it possible to apply the optimal analysis algorithm depending on the category of data.

[0094] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit improves the accuracy of the analysis, for example, based on analysis results performed by the user in the past. The analysis unit can also suggest an optimal analysis method by referring to the user's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by referring to feedback provided by the user in the past. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0095] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. 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 stressed, the analysis unit can provide a short, to-the-point analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is in a hurry, the analysis unit can also provide a quick, to-the-point analysis. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] The analysis unit can determine the priority of analysis based on the time of data submission during analysis. The analysis unit can determine the priority of analysis based on the time of data submission during analysis. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also analyze the time of data submission and optimize the order of analysis. Furthermore, the analysis unit can postpone analysis of data that was submitted earlier. This makes it possible to determine the priority of analysis based on the time of data submission.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also analyze the relevance of the data and optimize the order of analysis. Furthermore, the analysis unit can postpone analysis of less relevant data. In this way, the order of analysis can be optimized based on the relevance of the data.

[0098] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during the analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during the analysis. For example, the analysis unit can use detailed technical terms for users with high levels of expertise. The analysis unit can also use concise terms for users with low levels of expertise. Furthermore, the analysis unit can analyze the user's level of expertise and suggest optimal terms. This makes it possible to suggest optimal terms according to the user's level of expertise.

[0099] The time series analysis unit can estimate the user's emotions and adjust the display method of the time series analysis based on the estimated user emotions. The time series analysis unit can estimate the user's emotions and adjust the display method of the time series analysis based on the estimated user emotions. For example, the time series analysis unit can provide a simple display method when the user is stressed. The time series analysis unit can also provide a detailed display method when the user is relaxed. Furthermore, the time series analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to adjust the display method of the time series analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] During time series analysis, the time series analysis unit can predict current data by referring to past time series data. During time series analysis, the time series analysis unit predicts current data by referring to past time series data. The time series analysis unit, for example, predicts current trends based on past data. The time series analysis unit can also predict changes in current data by referring to past data. Furthermore, the time series analysis unit can analyze past data and predict future data. This makes it possible to predict current trends based on past data.

[0101] The time series analysis unit can apply different time series analysis methods to each data category during time series analysis. The time series analysis unit applies different time series analysis methods to each data category during time series analysis. For example, the time series analysis unit applies a time series analysis method dedicated to text to text data. The time series analysis unit can also apply a time series analysis method dedicated to images to image data. Furthermore, the time series analysis unit can also apply a time series analysis method dedicated to audio to audio data. This makes it possible to apply the most appropriate time series analysis method depending on the data category.

[0102] The time series analysis unit can analyze the time series taking into account the attribute information of the data submitter during the time series analysis. The time series analysis unit analyzes the time series taking into account the attribute information of the data submitter during the time series analysis. The time series analysis unit performs the time series analysis based on, for example, the attribute information of the data submitter. The time series analysis unit can also analyze changes in the time series data by referring to the attribute information of the data submitter. Furthermore, the time series analysis unit can analyze trends in the time series data taking into account the attribute information of the data submitter. This makes it possible to analyze changes in the time series data taking into account the attribute information of the data submitter.

[0103] The time series analysis unit can estimate the user's emotions and adjust the importance of the time series analysis based on the estimated user emotions. The time series analysis unit can estimate the user's emotions and adjust the importance of the time series analysis based on the estimated user emotions. For example, when the user is feeling stressed, the time series analysis unit prioritizes analysis of data with high importance. The time series analysis unit can also analyze a wide range of data when the user is relaxed. Furthermore, when the user is in a hurry, the time series analysis unit can quickly analyze important data. This allows the importance of the time series analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The time series analysis unit can analyze time series changes based on the time of data submission during time series analysis. The time series analysis unit analyzes time series changes based on the time of data submission during time series analysis. The time series analysis unit analyzes time series changes based on, for example, the time series submission time. The time series analysis unit can also prioritize analysis of data that was submitted recently. Furthermore, the time series analysis unit can also postpone analysis of data that was submitted earlier. This makes it possible to analyze time series changes based on the time of data submission.

[0105] The time series analysis unit can analyze the time series by referring to market data related to the data during the time series analysis. The time series analysis unit analyzes the time series by referring to market data related to the data during the time series analysis. The time series analysis unit, for example, analyzes changes in the time series based on the related market data. The time series analysis unit can also analyze trends in the time series data by referring to market data. Furthermore, the time series analysis unit can analyze changes in the time series data by taking the related market data into consideration. This makes it possible to analyze changes in the time series based on the related market data.

[0106] The time series analysis unit can analyze the time series taking into account the technological maturity of the data when analyzing the time series. The time series analysis unit analyzes the time series taking into account the technological maturity of the data when analyzing the time series. The time series analysis unit analyzes changes in the time series, for example, based on the technological maturity of the data. The time series analysis unit can also analyze trends in the time series data by referring to the technological maturity. Furthermore, the time series analysis unit can analyze changes in the time series data taking into account the technological maturity of the data. This makes it possible to analyze changes in the time series based on the technological maturity of the data. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, storage unit, analysis unit, and time-series analysis 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 acquisition unit acquires web information using the control unit 46A of the smart device 14, and acquires data using scraping technology or an API using the specific processing unit 290 of the data processing device 12. The storage unit saves the data in the database 24 of the data processing device 12 and periodically accumulates the data using a scheduling function. The analysis unit compares and analyzes the data using a generated AI using the specific processing unit 290 of the data processing device 12, and the time-series analysis unit analyzes changes in the data using a time-series data model using the specific processing unit 290 of the data processing device 12. Furthermore, the acquisition unit has a function of estimating the user's emotions and adjusting the timing of acquiring web information based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, storage unit, analysis unit, and time-series analysis 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 acquisition unit acquires web information using the control unit 46A of the smart glasses 214, and acquires data using scraping technology or an API using the specific processing unit 290 of the data processing device 12. The storage unit saves the data in the database 24 of the data processing device 12 and periodically accumulates the data using a scheduling function. The analysis unit compares and analyzes the data using a generated AI using the specific processing unit 290 of the data processing device 12, and the time-series analysis unit analyzes changes in the data using a time-series data model using the specific processing unit 290 of the data processing device 12. Furthermore, the acquisition unit has a function of estimating the user's emotions and adjusting the timing of acquiring web information based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, storage unit, analysis unit, and time-series analysis unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit acquires web information using the control unit 46A of the headset-type terminal 314, and acquires data using scraping technology or an API using the specific processing unit 290 of the data processing device 12. The storage unit saves the data in the database 24 of the data processing device 12 and periodically accumulates the data using a scheduling function. The analysis unit compares and analyzes the data using a generated AI using the specific processing unit 290 of the data processing device 12, and the time-series analysis unit analyzes changes in the data using a time-series data model using the specific processing unit 290 of the data processing device 12. Furthermore, the acquisition unit has a function of estimating the user's emotions and adjusting the timing of acquiring web information based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, storage unit, analysis unit, and time-series analysis unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires web information using the control unit 46A of the robot 414, and acquires data using scraping technology or an API using the specific processing unit 290 of the data processing device 12. The storage unit saves the data in the database 24 of the data processing device 12 and periodically accumulates the data using a scheduling function. The analysis unit compares and analyzes the data using a generated AI using the specific processing unit 290 of the data processing device 12, and the time-series analysis unit analyzes changes in the data using a time-series data model using the specific processing unit 290 of the data processing device 12. Furthermore, the acquisition unit has a function of estimating the user's emotions and adjusting the timing of acquiring web information based on the estimated emotions.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The acquisition unit can also estimate the user's emotions and adjust the type of web information to be acquired based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize acquiring relaxing content. If the user is excited, it can acquire highly entertaining information. Furthermore, if the user is sad, it can acquire encouraging and comforting information. This makes it possible to provide information according to the user's emotions.

[0109] The storage unit can also determine the priority of storage based on the importance of the data. For example, it can store data with high importance first, and store data with low importance later. It can also analyze the importance of data and optimize the order of storage. Furthermore, it can automatically delete data with low importance after a certain period of time. This enables efficient data management based on the importance of the data.

[0110] The analysis unit can also estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, a simple and easy-to-understand display method can be provided. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, a concise display method that focuses on the main points can be provided. This makes it possible to provide flexible analysis results according to the user's emotions.

[0111] The time series analysis unit can also apply different time series analysis methods to different data categories. For example, a text-specific time series analysis method can be applied to text data, and an image-specific time series analysis method can be applied to image data. It can also apply an audio-specific time series analysis method to audio data. This enables optimal time series analysis according to the data category.

[0112] The acquisition unit can also prioritize acquisition of highly relevant information taking into account the user's geographical location information. For example, it can prioritize acquisition of news and event information related to the user's current location. It can also acquire highly relevant information based on the user's past location information. Furthermore, it can customize the information to be acquired based on the location information set by the user. This makes it possible to provide information taking into account the user's geographical location information.

[0113] The analysis unit can also estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is feeling stressed, a concise analysis can be provided. If the user is relaxed, a detailed analysis can be provided. Furthermore, if the user is in a hurry, a brief analysis can be provided. This allows for flexible analysis according to the user's emotions.

[0114] The storage unit can also apply different storage algorithms depending on the data category. For example, a storage algorithm specifically for text data can be applied to text data, and a storage algorithm specifically for images can be applied to image data. It can also apply a storage algorithm specifically for audio data. This allows for optimal storage depending on the data category.

[0115] The acquisition unit can also analyze the user's social media activity and acquire related information. For example, it can acquire information from accounts the user follows on social media. It can also analyze the content posted by the user on social media to acquire related information. It can also acquire related information by referring to the activities of the user's friends on social media. This makes it possible to provide information that takes the user's social media activity into consideration.

[0116] The time series analysis unit can also estimate the user's emotions and adjust the display method of the time series analysis based on the estimated emotions. For example, if the user is feeling stressed, a simple display method can be provided. If the user is relaxed, a detailed display method can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to display the time series analysis flexibly according to the user's emotions.

[0117] The analysis department can also determine the priority of analysis based on the time of data submission. For example, the latest data can be analyzed first, and older data can be left for later. The analysis order can also be optimized by analyzing the time of data submission. Furthermore, older data can be automatically deleted after a certain period of time. This allows for efficient analysis based on the time of data submission.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The acquisition unit acquires web information. Web information includes news articles, blog posts, social media posts, etc. The acquisition unit acquires information from web pages using scraping technology or APIs. For example, the acquisition unit periodically collects information from a specific news site. Step 2: The storage unit stores the data acquired by the acquisition unit. The storage unit saves the data in a database. The type of database includes relational databases and NoSQL databases. The storage unit automatically stores data based on scheduling functions and trigger events. Step 3: The analysis unit compares and analyzes the data accumulated by the accumulation unit. The analysis unit uses generation AI to compare and analyze the data. The generation AI includes models such as GPT-4 and Gemini, and performs comparative analysis of the data based on specified prompts. For example, it compares the content of news articles collected at different times and analyzes changes and trends. Step 4: The time series analysis unit analyzes the results obtained by the analysis unit in a time series. The time series analysis unit uses a time series data model to analyze changes in the data. For example, it analyzes changes in the frequency and content of news reports on a specific topic over time to understand changes and trends in the data over a long period of time.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit for acquiring web information; a storage unit that stores the data acquired by the acquisition unit; an analysis unit that compares and analyzes the data stored by the storage unit; a time series analysis unit that analyzes the results obtained by the analysis unit in time series; Equipped with A system characterized by:

2. The acquisition unit Retrieving information from a specific website 2. The system of claim 1.

3. The analysis unit Comparative analysis of data using generative AI 2. The system of claim 1.

4. The time series analysis unit Analyze changes and trends in data over specific time periods 2. The system of claim 1.

5. The storage unit is Automatically accumulate acquired data 2. The system of claim 1.

6. The analysis unit Compare data collected at different points in time to analyze changes and trends 2. The system of claim 1.

7. The acquisition unit Estimates user emotions and adjusts the timing of web information retrieval based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit When retrieving information from a specific website, the system selects the site to retrieve based on the user's past browsing history.

2. The system of claim 1.

9. The acquisition unit Filtering web information based on the user's current interests when retrieving it 2. The system of claim 1.

Images