system

Abstract

The system according to this embodiment aims to predict market price movements and issue warnings at appropriate times. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a prediction unit, a generation unit, and an alarm unit. The collection unit collects past price movements. The analysis unit analyzes the past price movements collected by the collection unit. The prediction unit predicts future price movements based on the analysis results obtained by the analysis unit. The generation unit generates the future price movements predicted by the prediction unit as a chart. The alarm unit issues an emergency alarm based on specific criteria determined by the prediction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, predicting market price movements and issuing warnings at appropriate times have not been fully carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to predict market price movements and issue warnings at appropriate times.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, a generation unit, and an alarm unit. The data collection unit collects past price movements. The analysis unit analyzes the past price movements collected by the data collection unit. The prediction unit predicts future price movements based on the analysis results obtained by the analysis unit. The generation unit generates the future price movements predicted by the prediction unit as a chart. The alarm unit issues an emergency alarm based on specific criteria determined by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict market price movements and issue alarms at the appropriate time. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The market forecasting system according to an embodiment of the present invention is a system that predicts market price movements using candlestick charts and generates future price movement predictions as a diagram. This market forecasting system visually analyzes past price movements and predicts future price movements. Furthermore, it issues an emergency alert and notifies the user when a large price movement is expected. This helps protect assets and maximize profits. First, it visually analyzes past price movements using a visual analysis tool. For example, it analyzes past candlestick charts and detects specific patterns. At this time, the AI ​​learns from past data and builds a model for predicting future price movements. This makes it possible to predict future price movements from past price movements. Next, it generates the predicted price movements as a chart. For example, it generates a candlestick chart showing future price movements and provides it to the user. This chart becomes an important tool for the user to understand future market trends. Furthermore, it issues an emergency alert when a large price movement is expected. For example, if the AI ​​predicts a large price movement, it issues an emergency alert and notifies the user. This allows the user to respond quickly and protect assets and maximize profits. This system allows users to predict future price movements based on past price movements and make appropriate investment decisions. Furthermore, emergency alerts enable quick responses to significant price fluctuations. For example, using it in markets such as FX, stocks, and cryptocurrencies can help protect assets and maximize profits. Thus, the market prediction system allows users to predict future price movements based on past price movements and make appropriate investment decisions. Furthermore, emergency alerts enable quick responses to significant price fluctuations.

[0029] The market forecasting system according to this embodiment comprises a data collection unit, an analysis unit, a forecasting unit, a generation unit, and an alarm unit. The data collection unit collects past price movements. Past price movements include, for example, data from the past year, data from the past five years, etc., but are not limited to such examples. The data collection unit collects past price movements using, for example, a visual analysis tool. Visual analysis tools include, for example, a charting tool, data visualization software, etc. The analysis unit analyzes past price movements using AI and detects specific patterns. AI may use, for example, neural networks, deep learning, etc. The analysis unit performs analysis using, for example, an AI model that takes past price movement data as input and outputs specific patterns. The forecasting unit predicts future price movements using an AI model. AI models include, for example, regression models, time series forecasting models, etc. The forecasting unit performs predictions using, for example, an AI model that takes past price movement data as input and outputs future price movements. The generation unit generates the predicted future price movements as a chart. The generated charts include, for example, candlestick charts, line charts, and bar charts. The generation unit generates charts using a system that takes future price movement data as input and outputs charts. The alarm unit issues emergency alarms based on specific criteria, as determined by the prediction unit. These specific criteria include, for example, price fluctuations and a surge in trading volume. The alarm unit issues alarms using a system that takes future price movement data as input and outputs emergency alarms. As a result, the market prediction system according to this embodiment can protect assets and maximize profits by predicting future price movements based on past price movements and issuing emergency alarms.

[0030] The data collection unit can collect historical price movements using visual analysis tools. These visual analysis tools include, but are not limited to, charting tools and data visualization software. For example, the data collection unit can collect historical price movements using charting tools. Charting tools are tools for visually displaying historical price movements and detecting specific patterns. Alternatively, the data collection unit can collect historical price movements using data visualization software. Data visualization software is software for visually displaying historical price movements and detecting specific patterns. This allows for the efficient collection of historical price movements using visual analysis tools. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input data collected using visual analysis tools into an AI and have the AI ​​perform data analysis.

[0031] The analysis unit can use AI to analyze past price movements and detect specific patterns. AI may employ, but is not limited to, technologies such as neural networks and deep learning. For example, the analysis unit can analyze past price movements using a neural network. A neural network is a technology that uses multiple layers of artificial neurons to analyze data and detect specific patterns. Alternatively, the analysis unit can analyze past price movements using deep learning. Deep learning is a technology that uses deep neural networks to analyze data and detect specific patterns. For example, the analysis unit can perform analysis using an AI model that takes past price movement data as input and outputs specific patterns. This allows for the efficient detection of specific patterns from past price movements using AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input past price movement data into an AI and have the AI ​​perform the detection of specific patterns.

[0032] The prediction unit can predict future price movements using an AI model. The AI ​​model includes, but is not limited to, regression models and time series forecasting models. For example, the prediction unit can predict future price movements using a regression model. A regression model is a statistical method for predicting future price movements based on past data. The prediction unit can also predict future price movements using a time series forecasting model. A time series forecasting model is a method for predicting future price movements based on past time series data. For example, the prediction unit can perform predictions using an AI model that takes past price movement data as input and outputs future price movements. This allows for highly accurate prediction of future price movements by using an AI model. Some or all of the above-described processes in the prediction unit may be performed using AI, or not. For example, the prediction unit can input past price movement data into an AI and have the AI ​​predict future price movements.

[0033] The generation unit can generate candlestick charts that show predicted future price movements. Candlestick charts include, but are not limited to, daily charts and weekly charts. For example, the generation unit can generate a daily chart. A daily chart is a candlestick chart that shows price movements for one day. The generation unit can generate a weekly chart. A weekly chart is a candlestick chart that shows price movements for one week. The generation unit generates charts using, for example, a system that takes future price movement data as input and outputs candlestick charts. This allows for a visual understanding of predicted future price movements. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input future price movement data into AI and have the AI ​​generate candlestick charts.

[0034] The alarm unit can issue emergency alerts and notify users based on specific criteria. These criteria include, but are not limited to, price fluctuations and surges in trading volume. For example, the alarm unit may issue an emergency alert based on price fluctuations. Price fluctuations are an indicator of the magnitude of price fluctuations within a certain period. For example, the alarm unit may issue an emergency alert based on a surge in trading volume. A surge in trading volume is an indicator of a rapid increase in trading volume within a certain period. The alarm unit may issue an alert using a system that takes future price movement data as input and outputs an emergency alert. This allows for a quick response when large price movements are expected. Some or all of the above processing in the alarm unit may be performed using, for example, AI, or not using AI. For example, the alarm unit may input future price movement data into AI and have the AI ​​issue emergency alerts.

[0035] The data collection unit can filter historical price movement data by considering specific market events and news. For example, the data collection unit can collect data when important economic indicators are released and consider their impact. For example, the data collection unit can collect data when companies announce their earnings and consider their impact. For example, the data collection unit can collect data during political events or elections and consider their impact. This allows for more accurate data collection by considering market events and news. Consideration of market events and news is done using, for example, news feeds or event calendars. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input market event and news data into AI and have the AI ​​perform data filtering.

[0036] The data collection unit can simultaneously collect data from different timeframes when collecting historical price movement data. For example, the data collection unit can simultaneously collect 1-minute, 5-minute, and 15-minute data to analyze short-term price movements. For example, the data collection unit can simultaneously collect 1-hour, 4-hour, and daily data to analyze medium-term price movements. For example, the data collection unit can simultaneously collect weekly, monthly, and yearly data to analyze long-term price movements. This allows for the simultaneous collection of data from different timeframes, enabling analysis of price movements from short-term to long-term. Data collection from different timeframes is performed, for example, using a data collection system. Some or all of the above-described processes in the data collection unit may be performed, for example, using AI, or without AI. For example, the data collection unit can input data from different timeframes into AI and have the AI ​​perform the data collection.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting historical price movement data. For example, if the user is in Japan, the data collection unit will prioritize the collection of data for the Japanese market. For example, if the user is in the United States, the data collection unit will prioritize the collection of data for the US market. For example, if the user is in Europe, the data collection unit will prioritize the collection of data for the European market. This allows for the collection of highly relevant data by considering the user's geographical location. Consideration of geographical location is performed using, for example, GPS data or IP addresses. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform the data collection.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data when collecting historical price movement data. For example, the data collection unit can analyze the content of posts from accounts that a user follows on X (formerly Twitter) and collect relevant market data. For example, the data collection unit can analyze the content of posts from investment groups that a user participates in on Facebook (registered trademark) and collect relevant market data. For example, the data collection unit can analyze the content of posts from investors that a user interacts with on LinkedIn (registered trademark) and collect relevant market data. In this way, relevant market data can be collected by analyzing a user's social media activity. The analysis of social media activity is performed using methods such as analyzing post content and analyzing the number of followers. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into AI and have the AI ​​perform the data collection.

[0039] The analysis unit can improve accuracy by combining different analysis algorithms when analyzing historical price movement data. For example, the analysis unit can combine moving averages and Bollinger Bands. For example, the analysis unit can combine RSI and MACD. For example, the analysis unit can combine Fibonacci retracements and Elliott Wave Theory. This improves the accuracy of the analysis by combining different analysis algorithms. The combination of different analysis algorithms is performed using, for example, regression analysis and clustering. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input different analysis algorithms into AI and have the AI ​​perform the analysis.

[0040] The analysis department can focus its analysis on specific market segments or time periods when analyzing historical price movement data. For example, the analysis department can analyze data from the technology sector and analyze price movements during specific time periods. For example, the analysis department can analyze data from the energy sector and analyze price movements during specific time periods. For example, the analysis department can analyze data from the financial sector and analyze price movements during specific time periods. This allows for more detailed analysis by focusing on specific market segments or time periods. The focus on market segments and time periods can be done, for example, by using a specific industry or a specific trading time period. Some or all of the above processing in the analysis department may be performed using AI, or not. For example, the analysis department can input data from a specific market segment or time period into an AI and have the AI ​​perform the analysis.

[0041] The analysis unit can customize its analysis by taking into account the user's investment history when analyzing historical price movement data. For example, the analysis unit may prioritize the analysis of data on stocks the user has invested in in the past. For example, the analysis unit may adjust the analysis method according to the user's investment style (short-term, medium-term, long-term). For example, the analysis unit may customize the analysis results according to the user's risk tolerance. This allows for more personalized analysis by taking into account the user's investment history. Consideration of investment history is done using, for example, past trading data and portfolio history. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's investment history data into AI and have the AI ​​perform the customized analysis.

[0042] The analysis department can improve the accuracy of its analysis by referring to relevant market news and event information when analyzing historical price movement data. For example, the analysis department can analyze data when important economic indicators are released and consider their impact. For example, the analysis department can analyze data when companies announce their earnings and consider their impact. For example, the analysis department can analyze data during political events or elections and consider their impact. This improves the accuracy of the analysis by referring to market news and event information. This information can be accessed using, for example, news feeds or event calendars. Some or all of the above processes in the analysis department may be performed using, for example, AI, or not. For example, the analysis department can input market news and event information into AI and have the AI ​​perform the analysis to improve accuracy.

[0043] The prediction unit can improve accuracy by combining different prediction models when predicting future price movements. For example, the prediction unit can make predictions by combining the ARIMA model and the LSTM model. For example, the prediction unit can make predictions by combining the Random Forest and the Support Vector Machine. For example, the prediction unit can make predictions by combining the Neural Network and Bayesian estimation. This improves the accuracy of predictions by combining different prediction models. The combination of different prediction models is done using, for example, the ARIMA model and the LSTM model. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input different prediction models into the AI ​​and have the AI ​​perform the prediction.

[0044] The forecasting unit can focus on specific market segments or time periods when predicting future price movements. For example, the forecasting unit can predict future price movements in the technology sector. For example, the forecasting unit can predict future price movements in the energy sector. For example, the forecasting unit can predict future price movements in the financial sector. This allows for more detailed predictions by focusing on specific market segments or time periods. The focus on market segments and time periods can be done, for example, by using a specific industry or a specific trading time zone. Some or all of the above processing in the forecasting unit may be performed using AI, or not using AI. For example, the forecasting unit can input data for a specific market segment or time period into an AI and have the AI ​​perform the prediction.

[0045] The prediction unit can customize its predictions by taking into account the user's investment history when forecasting future price movements. For example, the prediction unit can predict the future price movements of stocks that the user has invested in in the past. For example, the prediction unit can adjust its prediction method according to the user's investment style (short-term, medium-term, long-term). For example, the prediction unit can customize the prediction results according to the user's risk tolerance. This allows for more personalized predictions by taking into account the user's investment history. Consideration of investment history is done using, for example, past trading data and portfolio history. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input the user's investment history data into AI and have the AI ​​perform the customization of the prediction.

[0046] The forecasting unit can improve the accuracy of its predictions by referring to relevant market news and event information when forecasting future price movements. For example, the forecasting unit forecasts data when important economic indicators are released and considers their impact. For example, the forecasting unit forecasts data when companies announce their earnings and considers their impact. For example, the forecasting unit forecasts data during political events or elections and considers their impact. This improves the accuracy of predictions by referring to market news and event information. Market news and event information is referenced using, for example, news feeds and event calendars. Some or all of the above processing in the forecasting unit may be performed using, for example, AI, or not using AI. For example, the forecasting unit can input market news and event information into AI and have the AI ​​perform the improvement of prediction accuracy.

[0047] The generation unit can combine and display different chart formats when generating a chart of predicted future price movements. For example, the generation unit can combine and display a line chart and a bar chart. For example, the generation unit can combine and display a candlestick chart and a histogram. For example, the generation unit can combine and display a scatter plot and an area chart. This allows for the provision of more multifaceted information by combining different chart formats. The combination of different chart formats is done using, for example, candlestick charts and line charts. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input data of different chart formats into AI and have the AI ​​perform the chart generation.

[0048] The generation unit can generate charts of predicted future price movements, focusing on specific market segments or time periods. For example, the generation unit can generate charts showing future price movements in the technology sector, the energy sector, or the financial sector. This allows for the provision of more detailed information by focusing on specific market segments or time periods. The focus on market segments and time periods can be achieved, for example, by using a specific industry or a specific trading time zone. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input data for a specific market segment or time period into an AI and have the AI ​​generate the charts.

[0049] The generation unit can customize the charts when generating predicted future price movements, taking into account the user's investment history. For example, the generation unit can generate charts showing the future price movements of stocks the user has invested in in the past. For example, the generation unit can customize the charts according to the user's investment style (short-term, medium-term, long-term). For example, the generation unit can customize the charts according to the user's risk tolerance. This makes it possible to provide more personalized information by taking into account the user's investment history. Consideration of investment history is done using, for example, past trading data, portfolio history, etc. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's investment history data into AI and have the AI ​​perform the chart customization.

[0050] The generation unit can incorporate relevant market news and event information into charts when generating predicted future price movements. For example, the generation unit can incorporate data into charts when important economic indicators are announced. For example, the generation unit can incorporate data into charts when companies announce their financial results. For example, the generation unit can incorporate data into charts during political events or elections. By incorporating market news and event information into charts, it becomes possible to provide more detailed information. The incorporation of market news and event information is done using, for example, news feeds or event calendars. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input market news and event information into AI and have the AI ​​perform the incorporation into charts.

[0051] The alarm unit can combine different alarm methods to notify when large price movements are expected. For example, the alarm unit can combine email and SMS to send an emergency alert. For example, the alarm unit can combine app notifications and email to send an emergency alert. For example, the alarm unit can combine SMS and app notifications to send an emergency alert. By combining different alarm methods, more reliable notifications become possible. The combination of different alarm methods is carried out using, for example, email notifications and SMS notifications. Some or all of the above processing in the alarm unit may be carried out using, for example, AI, or not using AI. For example, the alarm unit can input data for different alarm methods into AI and have the AI ​​execute the notifications.

[0052] The alarm unit can issue alerts focusing on specific market segments or time periods when significant price movements are expected. For example, the alarm unit can issue an alert when significant price movements are expected in the technology sector. For example, the alarm unit can issue an alert when significant price movements are expected in the energy sector. For example, the alarm unit can issue an alert when significant price movements are expected in the financial sector. This allows for more detailed alerts by focusing on specific market segments or time periods. The focus on market segments and time periods can be done, for example, by using a specific industry or a specific trading time zone. Some or all of the above processing in the alarm unit may be performed using AI, or not using AI. For example, the alarm unit can input data for a specific market segment or time period into an AI and have the AI ​​issue an alert.

[0053] The alarm unit can customize alarms when significant price movements are expected, taking into account the user's investment history. For example, the alarm unit may prioritize issuing alarms related to stocks the user has previously invested in. For example, the alarm unit may adjust the content of alarms according to the user's investment style (short-term, medium-term, long-term). For example, the alarm unit may customize the content of alarms according to the user's risk tolerance. This allows for more personalized alarm issuance by considering the user's investment history. Consideration of investment history is done using, for example, past trading data and portfolio history. Some or all of the above processing in the alarm unit may be performed using, for example, AI, or not using AI. For example, the alarm unit can input the user's investment history data into AI and have the AI ​​perform the alarm customization.

[0054] The alert unit can incorporate relevant market news and event information into its alerts when significant price movements are expected. For example, the alert unit can issue an alert when important economic indicators are released and consider their impact. For example, the alert unit can issue an alert when companies announce their financial results and consider their impact. For example, the alert unit can issue an alert during political events or elections and consider their impact. By incorporating market news and event information into the alerts, it becomes possible to provide more detailed information. The incorporation of market news and event information is done using, for example, news feeds or event calendars. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input market news and event information into AI and have the AI ​​perform the incorporation into the alerts.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The market forecasting system may also include a historical analysis unit that considers the user's trading history. This unit collects the user's past trading data and analyzes trading trends and patterns. For example, if a user frequently trades a particular stock, the system can prioritize providing information about that stock. It can also provide appropriate investment advice based on the user's trading style (short-term, medium-term, long-term). Furthermore, the historical analysis unit can consider the user's risk tolerance and issue warnings to avoid high-risk trades. This enables the provision of personalized information based on the user's trading history.

[0057] The data collection unit can collect regional market data, taking into account the user's geographical location. For example, if the user is in Asia, it will prioritize collecting data for the Asian market. If the user is in Europe, it will prioritize collecting data for the European market. This allows the system to provide highly relevant market data based on the user's geographical location. The data collection unit can also dynamically change the target region for data collection in response to the user's movement. This ensures that users always have access to the latest market information, no matter where they are.

[0058] The prediction unit can improve accuracy by combining different prediction models when forecasting future price movements. For example, it can combine the ARIMA model and the LSTM model for prediction, the Random Forest and Support Vector Machine for prediction, or the Neural Network and Bayesian estimation for prediction. This improves prediction accuracy by combining different prediction models. The combination of different prediction models is done using, for example, the ARIMA model and the LSTM model. The prediction unit can input different prediction models into the AI ​​and have the AI ​​perform the prediction.

[0059] The generation unit can combine and display different chart formats when generating charts of predicted future price movements. For example, it can combine line charts and bar charts, candlestick charts and histograms, or scatter plots and area charts. This allows for the provision of more multifaceted information by combining different chart formats. The combination of different chart formats is done using candlestick charts, line charts, etc. The generation unit can input data from different chart formats into the AI ​​and have the AI ​​generate the charts.

[0060] The data collection unit can filter historical price movement data by considering specific market events and news. For example, it can collect data when important economic indicators are released and consider their impact. It can also collect data when companies announce their earnings and consider its impact. Similarly, it can collect data during political events and elections and consider its impact. This allows for more accurate data collection by taking market events and news into account. Market events and news are considered using tools such as news feeds and event calendars.

[0061] The analysis unit can improve accuracy by combining different analysis algorithms when analyzing historical price movement data. For example, it can combine moving averages and Bollinger Bands, RSI and MACD, or Fibonacci retracements and Elliott Wave Theory. By combining different analysis algorithms, the accuracy of the analysis is improved. These combinations of different analysis algorithms are performed using methods such as regression analysis and clustering. The analysis unit can input different analysis algorithms into an AI and have the AI ​​perform the analysis.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects historical price movements. Historical price movements include, but are not limited to, data from the past year, data from the past five years, etc. The data collection unit collects historical price movements using, for example, visual analysis tools. Visual analysis tools include, for example, charting tools and data visualization software. Step 2: The analysis unit uses AI to analyze past price movements and detect specific patterns. AI technologies such as neural networks and deep learning are used. For example, the analysis unit uses an AI model that takes past price movement data as input and outputs specific patterns. Step 3: The prediction unit uses an AI model to predict future price movements. AI models include, for example, regression models and time series forecasting models. The prediction unit makes predictions using an AI model that takes past price movement data as input and outputs future price movements. Step 4: The generation unit generates a chart of the predicted future price movement. The generated charts may include, for example, candlestick charts, line charts, and bar charts. The generation unit generates the chart using, for example, a system that takes future price movement data as input and outputs a chart. Step 5: The alarm unit issues an emergency alert based on specific criteria determined by the prediction unit. These criteria may include, for example, price fluctuations or a surge in trading volume. The alarm unit issues an alert using a system that takes future price movement data as input and outputs an emergency alert.

[0064] (Example of form 2) The market forecasting system according to an embodiment of the present invention is a system that predicts market price movements using candlestick charts and generates future price movement predictions as a diagram. This market forecasting system visually analyzes past price movements and predicts future price movements. Furthermore, it issues an emergency alert and notifies the user when a large price movement is expected. This helps protect assets and maximize profits. First, it visually analyzes past price movements using a visual analysis tool. For example, it analyzes past candlestick charts and detects specific patterns. At this time, the AI ​​learns from past data and builds a model for predicting future price movements. This makes it possible to predict future price movements from past price movements. Next, it generates the predicted price movements as a chart. For example, it generates a candlestick chart showing future price movements and provides it to the user. This chart becomes an important tool for the user to understand future market trends. Furthermore, it issues an emergency alert when a large price movement is expected. For example, if the AI ​​predicts a large price movement, it issues an emergency alert and notifies the user. This allows the user to respond quickly and protect assets and maximize profits. This system allows users to predict future price movements based on past price movements and make appropriate investment decisions. Furthermore, emergency alerts enable quick responses to significant price fluctuations. For example, using it in markets such as FX, stocks, and cryptocurrencies can help protect assets and maximize profits. Thus, the market prediction system allows users to predict future price movements based on past price movements and make appropriate investment decisions. Furthermore, emergency alerts enable quick responses to significant price fluctuations.

[0065] The market forecasting system according to this embodiment comprises a data collection unit, an analysis unit, a forecasting unit, a generation unit, and an alarm unit. The data collection unit collects past price movements. Past price movements include, for example, data from the past year, data from the past five years, etc., but are not limited to such examples. The data collection unit collects past price movements using, for example, a visual analysis tool. Visual analysis tools include, for example, a charting tool, data visualization software, etc. The analysis unit analyzes past price movements using AI and detects specific patterns. AI may use, for example, neural networks, deep learning, etc. The analysis unit performs analysis using, for example, an AI model that takes past price movement data as input and outputs specific patterns. The forecasting unit predicts future price movements using an AI model. AI models include, for example, regression models, time series forecasting models, etc. The forecasting unit performs predictions using, for example, an AI model that takes past price movement data as input and outputs future price movements. The generation unit generates the predicted future price movements as a chart. The generated charts include, for example, candlestick charts, line charts, and bar charts. The generation unit generates charts using a system that takes future price movement data as input and outputs charts. The alarm unit issues emergency alarms based on specific criteria, as determined by the prediction unit. These specific criteria include, for example, price fluctuations and a surge in trading volume. The alarm unit issues alarms using a system that takes future price movement data as input and outputs emergency alarms. As a result, the market prediction system according to this embodiment can protect assets and maximize profits by predicting future price movements based on past price movements and issuing emergency alarms.

[0066] The data collection unit can collect historical price movements using visual analysis tools. These visual analysis tools include, but are not limited to, charting tools and data visualization software. For example, the data collection unit can collect historical price movements using charting tools. Charting tools are tools for visually displaying historical price movements and detecting specific patterns. Alternatively, the data collection unit can collect historical price movements using data visualization software. Data visualization software is software for visually displaying historical price movements and detecting specific patterns. This allows for the efficient collection of historical price movements using visual analysis tools. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input data collected using visual analysis tools into an AI and have the AI ​​perform data analysis.

[0067] The analysis unit can use AI to analyze past price movements and detect specific patterns. AI may employ, but is not limited to, technologies such as neural networks and deep learning. For example, the analysis unit can analyze past price movements using a neural network. A neural network is a technology that uses multiple layers of artificial neurons to analyze data and detect specific patterns. Alternatively, the analysis unit can analyze past price movements using deep learning. Deep learning is a technology that uses deep neural networks to analyze data and detect specific patterns. For example, the analysis unit can perform analysis using an AI model that takes past price movement data as input and outputs specific patterns. This allows for the efficient detection of specific patterns from past price movements using AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input past price movement data into an AI and have the AI ​​perform the detection of specific patterns.

[0068] The prediction unit can predict future price movements using an AI model. The AI ​​model includes, but is not limited to, regression models and time series forecasting models. For example, the prediction unit can predict future price movements using a regression model. A regression model is a statistical method for predicting future price movements based on past data. The prediction unit can also predict future price movements using a time series forecasting model. A time series forecasting model is a method for predicting future price movements based on past time series data. For example, the prediction unit can perform predictions using an AI model that takes past price movement data as input and outputs future price movements. This allows for highly accurate prediction of future price movements by using an AI model. Some or all of the above-described processes in the prediction unit may be performed using AI, or not. For example, the prediction unit can input past price movement data into an AI and have the AI ​​predict future price movements.

[0069] The generation unit can generate candlestick charts that show predicted future price movements. Candlestick charts include, but are not limited to, daily charts and weekly charts. For example, the generation unit can generate a daily chart. A daily chart is a candlestick chart that shows price movements for one day. The generation unit can generate a weekly chart. A weekly chart is a candlestick chart that shows price movements for one week. The generation unit generates charts using, for example, a system that takes future price movement data as input and outputs candlestick charts. This allows for a visual understanding of predicted future price movements. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input future price movement data into AI and have the AI ​​generate candlestick charts.

[0070] The alarm unit can issue emergency alerts and notify users based on specific criteria. These criteria include, but are not limited to, price fluctuations and surges in trading volume. For example, the alarm unit may issue an emergency alert based on price fluctuations. Price fluctuations are an indicator of the magnitude of price fluctuations within a certain period. For example, the alarm unit may issue an emergency alert based on a surge in trading volume. A surge in trading volume is an indicator of a rapid increase in trading volume within a certain period. The alarm unit may issue an alert using a system that takes future price movement data as input and outputs an emergency alert. This allows for a quick response when large price movements are expected. Some or all of the above processing in the alarm unit may be performed using, for example, AI, or not using AI. For example, the alarm unit may input future price movement data into AI and have the AI ​​issue emergency alerts.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the frequency of data collection and collect data in real time. For example, if the user is relaxed, the data collection unit can decrease the frequency of data collection and collect data periodically. For example, if the user is in a hurry, the data collection unit can shorten the frequency of data collection and collect data quickly. By adjusting the timing of data collection according to the user's emotions, more appropriate data collection becomes possible. User emotions are estimated using, for example, facial recognition or survey results. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the data collection timing.

[0072] The data collection unit can filter historical price movement data by considering specific market events and news. For example, the data collection unit can collect data when important economic indicators are released and consider their impact. For example, the data collection unit can collect data when companies announce their earnings and consider their impact. For example, the data collection unit can collect data during political events or elections and consider their impact. This allows for more accurate data collection by considering market events and news. Consideration of market events and news is done using, for example, news feeds or event calendars. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input market event and news data into AI and have the AI ​​perform data filtering.

[0073] The data collection unit can simultaneously collect data from different timeframes when collecting historical price movement data. For example, the data collection unit can simultaneously collect 1-minute, 5-minute, and 15-minute data to analyze short-term price movements. For example, the data collection unit can simultaneously collect 1-hour, 4-hour, and daily data to analyze medium-term price movements. For example, the data collection unit can simultaneously collect weekly, monthly, and yearly data to analyze long-term price movements. This allows for the simultaneous collection of data from different timeframes, enabling analysis of price movements from short-term to long-term. Data collection from different timeframes is performed, for example, using a data collection system. Some or all of the above-described processes in the data collection unit may be performed, for example, using AI, or without AI. For example, the data collection unit can input data from different timeframes into AI and have the AI ​​perform the data collection.

[0074] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting data from high-risk markets. For example, if the user is feeling at ease, the data collection unit will prioritize collecting data from stable markets. For example, if the user is excited, the data collection unit will prioritize collecting data from highly volatile markets. This allows for more appropriate data collection by prioritizing data according to the user's emotions. User emotions are estimated using methods such as facial recognition and survey results. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​determine the data prioritization.

[0075] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting historical price movement data. For example, if the user is in Japan, the data collection unit will prioritize the collection of data for the Japanese market. For example, if the user is in the United States, the data collection unit will prioritize the collection of data for the US market. For example, if the user is in Europe, the data collection unit will prioritize the collection of data for the European market. This allows for the collection of highly relevant data by considering the user's geographical location. Consideration of geographical location is performed using, for example, GPS data or IP addresses. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform the data collection.

[0076] The data collection unit can analyze the user's social media activity and collect relevant data when collecting historical price movement data. For example, the data collection unit can analyze the content of posts from accounts that the user follows on X (formerly Twitter) and collect relevant market data. For example, the data collection unit can analyze the content of posts from investment groups that the user participates in on Facebook and collect relevant market data. For example, the data collection unit can analyze the content of posts from investors that the user interacts with on LinkedIn and collect relevant market data. In this way, relevant market data can be collected by analyzing the user's social media activity. The analysis of social media activity is performed using methods such as analyzing the content of posts and analyzing the number of followers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into AI and have the AI ​​perform the data collection.

[0077] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize risk-avoidance analysis methods. For example, if the user is feeling secure, the analysis unit will prioritize risk-taking analysis methods. For example, if the user is excited, the analysis unit will prioritize analysis methods for highly volatile markets. By adjusting the analysis method according to the user's emotions, more appropriate analysis becomes possible. User emotions are estimated using methods such as facial recognition and survey results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the analysis method.

[0078] The analysis unit can improve accuracy by combining different analysis algorithms when analyzing historical price movement data. For example, the analysis unit can combine moving averages and Bollinger Bands. For example, the analysis unit can combine RSI and MACD. For example, the analysis unit can combine Fibonacci retracements and Elliott Wave Theory. This improves the accuracy of the analysis by combining different analysis algorithms. The combination of different analysis algorithms is performed using, for example, regression analysis and clustering. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input different analysis algorithms into AI and have the AI ​​perform the analysis.

[0079] The analysis department can focus its analysis on specific market segments or time periods when analyzing historical price movement data. For example, the analysis department can analyze data from the technology sector and analyze price movements during specific time periods. For example, the analysis department can analyze data from the energy sector and analyze price movements during specific time periods. For example, the analysis department can analyze data from the financial sector and analyze price movements during specific time periods. This allows for more detailed analysis by focusing on specific market segments or time periods. The focus on market segments and time periods can be done, for example, by using a specific industry or a specific trading time period. Some or all of the above processing in the analysis department may be performed using AI, or not. For example, the analysis department can input data from a specific market segment or time period into an AI and have the AI ​​perform the analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate information. The estimation of the user's emotions is performed using, for example, facial recognition, survey results, etc. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0081] The analysis unit can customize its analysis by taking into account the user's investment history when analyzing historical price movement data. For example, the analysis unit may prioritize the analysis of data on stocks the user has invested in in the past. For example, the analysis unit may adjust the analysis method according to the user's investment style (short-term, medium-term, long-term). For example, the analysis unit may customize the analysis results according to the user's risk tolerance. This allows for more personalized analysis by taking into account the user's investment history. Consideration of investment history is done using, for example, past trading data and portfolio history. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's investment history data into AI and have the AI ​​perform the customized analysis.

[0082] The analysis department can improve the accuracy of its analysis by referring to relevant market news and event information when analyzing historical price movement data. For example, the analysis department can analyze data when important economic indicators are released and consider their impact. For example, the analysis department can analyze data when companies announce their earnings and consider their impact. For example, the analysis department can analyze data during political events or elections and consider their impact. This improves the accuracy of the analysis by referring to market news and event information. This information can be accessed using, for example, news feeds or event calendars. Some or all of the above processes in the analysis department may be performed using, for example, AI, or not. For example, the analysis department can input market news and event information into AI and have the AI ​​perform the analysis to improve accuracy.

[0083] The prediction unit can estimate the user's emotions and adjust its prediction method based on the estimated emotions. For example, if the user is feeling anxious, the prediction unit prioritizes a risk-avoidance prediction method. If the user is feeling reassured, the prediction unit prioritizes a risk-taking prediction method. If the user is excited, the prediction unit prioritizes a prediction method for highly volatile markets. By adjusting the prediction method according to the user's emotions, more accurate predictions become possible. User emotions are estimated using methods such as facial recognition and survey results. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into AI and have the AI ​​adjust the prediction method.

[0084] The prediction unit can improve accuracy by combining different prediction models when predicting future price movements. For example, the prediction unit can make predictions by combining the ARIMA model and the LSTM model. For example, the prediction unit can make predictions by combining the Random Forest and the Support Vector Machine. For example, the prediction unit can make predictions by combining the Neural Network and Bayesian estimation. This improves the accuracy of predictions by combining different prediction models. The combination of different prediction models is done using, for example, the ARIMA model and the LSTM model. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input different prediction models into the AI ​​and have the AI ​​perform the prediction.

[0085] The forecasting unit can focus on specific market segments or time periods when predicting future price movements. For example, the forecasting unit can predict future price movements in the technology sector. For example, the forecasting unit can predict future price movements in the energy sector. For example, the forecasting unit can predict future price movements in the financial sector. This allows for more detailed predictions by focusing on specific market segments or time periods. The focus on market segments and time periods can be done, for example, by using a specific industry or a specific trading time zone. Some or all of the above processing in the forecasting unit may be performed using AI, or not using AI. For example, the forecasting unit can input data for a specific market segment or time period into an AI and have the AI ​​perform the prediction.

[0086] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit provides a simple and highly visible display method. For example, if the user is relaxed, the prediction unit provides a display method that includes detailed information. For example, if the user is in a hurry, the prediction unit provides a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate information. The estimation of the user's emotions is performed using, for example, facial recognition or survey results. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0087] The prediction unit can customize its predictions by taking into account the user's investment history when forecasting future price movements. For example, the prediction unit can predict the future price movements of stocks that the user has invested in in the past. For example, the prediction unit can adjust its prediction method according to the user's investment style (short-term, medium-term, long-term). For example, the prediction unit can customize the prediction results according to the user's risk tolerance. This allows for more personalized predictions by taking into account the user's investment history. Consideration of investment history is done using, for example, past trading data and portfolio history. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input the user's investment history data into AI and have the AI ​​perform the customization of the prediction.

[0088] The forecasting unit can improve the accuracy of its predictions by referring to relevant market news and event information when forecasting future price movements. For example, the forecasting unit forecasts data when important economic indicators are released and considers their impact. For example, the forecasting unit forecasts data when companies announce their earnings and considers their impact. For example, the forecasting unit forecasts data during political events or elections and considers their impact. This improves the accuracy of predictions by referring to market news and event information. Market news and event information is referenced using, for example, news feeds and event calendars. Some or all of the above processing in the forecasting unit may be performed using, for example, AI, or not using AI. For example, the forecasting unit can input market news and event information into AI and have the AI ​​perform the improvement of prediction accuracy.

[0089] The generation unit can estimate the user's emotions and adjust the format of the generated chart based on the estimated emotions. For example, if the user is tense, the generation unit generates a simple and highly visible chart. If the user is relaxed, the generation unit generates a chart containing detailed information. If the user is in a hurry, the generation unit generates a chart that gets straight to the point. By adjusting the chart format according to the user's emotions, it becomes possible to provide more appropriate information. The estimation of the user's emotions is performed using, for example, facial recognition or survey results. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user emotion data into AI and have the AI ​​perform the adjustment of the chart format.

[0090] The generation unit can combine and display different chart formats when generating a chart of predicted future price movements. For example, the generation unit can combine and display a line chart and a bar chart. For example, the generation unit can combine and display a candlestick chart and a histogram. For example, the generation unit can combine and display a scatter plot and an area chart. This allows for the provision of more multifaceted information by combining different chart formats. The combination of different chart formats is done using, for example, candlestick charts and line charts. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input data of different chart formats into AI and have the AI ​​perform the chart generation.

[0091] The generation unit can generate charts of predicted future price movements, focusing on specific market segments or time periods. For example, the generation unit can generate charts showing future price movements in the technology sector, the energy sector, or the financial sector. This allows for the provision of more detailed information by focusing on specific market segments or time periods. The focus on market segments and time periods can be achieved, for example, by using a specific industry or a specific trading time zone. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input data for a specific market segment or time period into an AI and have the AI ​​generate the charts.

[0092] The generation unit can estimate the user's emotions and adjust the color and design of the generated chart based on the estimated emotions. For example, if the user is tense, the generation unit will generate a chart with calm colors. For example, if the user is relaxed, the generation unit will generate a chart with bright colors. For example, if the user is in a hurry, the generation unit will generate a chart with highly visible colors. By adjusting the color and design of the chart according to the user's emotions, it becomes possible to provide information with higher visibility. The estimation of the user's emotions is performed using, for example, facial recognition or survey results. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user emotion data into AI and have the AI ​​perform the adjustment of the chart's color and design.

[0093] The generation unit can customize the charts when generating predicted future price movements, taking into account the user's investment history. For example, the generation unit can generate charts showing the future price movements of stocks the user has invested in in the past. For example, the generation unit can customize the charts according to the user's investment style (short-term, medium-term, long-term). For example, the generation unit can customize the charts according to the user's risk tolerance. This makes it possible to provide more personalized information by taking into account the user's investment history. Consideration of investment history is done using, for example, past trading data, portfolio history, etc. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's investment history data into AI and have the AI ​​perform the chart customization.

[0094] The generation unit can incorporate relevant market news and event information into charts when generating predicted future price movements. For example, the generation unit can incorporate data into charts when important economic indicators are announced. For example, the generation unit can incorporate data into charts when companies announce their financial results. For example, the generation unit can incorporate data into charts during political events or elections. By incorporating market news and event information into charts, it becomes possible to provide more detailed information. The incorporation of market news and event information is done using, for example, news feeds or event calendars. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input market news and event information into AI and have the AI ​​perform the incorporation into charts.

[0095] The alarm unit can estimate the user's emotions and adjust the timing of emergency alarms based on the estimated emotions. For example, if the user is feeling anxious, the alarm unit will issue an emergency alarm earlier. If the user is feeling at ease, the alarm unit will issue an emergency alarm at the normal time. If the user is excited, the alarm unit will issue an emergency alarm immediately. By adjusting the timing of emergency alarms according to the user's emotions, more appropriate alarms can be issued. The user's emotions can be estimated using, for example, facial recognition or survey results. Some or all of the above processing in the alarm unit may be performed using, for example, AI, or not. For example, the alarm unit can input user emotion data into AI and have the AI ​​adjust the timing of emergency alarms.

[0096] The alarm unit can combine different alarm methods to notify when large price movements are expected. For example, the alarm unit can combine email and SMS to send an emergency alert. For example, the alarm unit can combine app notifications and email to send an emergency alert. For example, the alarm unit can combine SMS and app notifications to send an emergency alert. By combining different alarm methods, more reliable notifications become possible. The combination of different alarm methods is carried out using, for example, email notifications and SMS notifications. Some or all of the above processing in the alarm unit may be carried out using, for example, AI, or not using AI. For example, the alarm unit can input data for different alarm methods into AI and have the AI ​​execute the notifications.

[0097] The alarm unit can issue alerts focusing on specific market segments or time periods when significant price movements are expected. For example, the alarm unit can issue an alert when significant price movements are expected in the technology sector. For example, the alarm unit can issue an alert when significant price movements are expected in the energy sector. For example, the alarm unit can issue an alert when significant price movements are expected in the financial sector. This allows for more detailed alerts by focusing on specific market segments or time periods. The focus on market segments and time periods can be done, for example, by using a specific industry or a specific trading time zone. Some or all of the above processing in the alarm unit may be performed using AI, or not using AI. For example, the alarm unit can input data for a specific market segment or time period into an AI and have the AI ​​issue an alert.

[0098] The alarm unit can estimate the user's emotions and adjust the content and format of the emergency alert based on the estimated emotions. For example, if the user is feeling anxious, the alarm unit will issue an emergency alert containing detailed information. For example, if the user is feeling at ease, the alarm unit will issue an emergency alert containing concise information. For example, if the user is excited, the alarm unit will issue an emergency alert in a visually stimulating format. By adjusting the content and format of the emergency alert according to the user's emotions, it becomes possible to provide more appropriate information. The estimation of the user's emotions is performed using, for example, facial recognition or survey results. Some or all of the above processing in the alarm unit may be performed using, for example, AI, or not using AI. For example, the alarm unit can input user emotion data into AI and have the AI ​​adjust the content and format of the emergency alert.

[0099] The alarm unit can customize alarms when significant price movements are expected, taking into account the user's investment history. For example, the alarm unit may prioritize issuing alarms related to stocks the user has previously invested in. For example, the alarm unit may adjust the content of alarms according to the user's investment style (short-term, medium-term, long-term). For example, the alarm unit may customize the content of alarms according to the user's risk tolerance. This allows for more personalized alarm issuance by considering the user's investment history. Consideration of investment history is done using, for example, past trading data and portfolio history. Some or all of the above processing in the alarm unit may be performed using, for example, AI, or not using AI. For example, the alarm unit can input the user's investment history data into AI and have the AI ​​perform the alarm customization.

[0100] The alert unit can incorporate relevant market news and event information into its alerts when significant price movements are expected. For example, the alert unit can issue an alert when important economic indicators are released and consider their impact. For example, the alert unit can issue an alert when companies announce their financial results and consider their impact. For example, the alert unit can issue an alert during political events or elections and consider their impact. By incorporating market news and event information into the alerts, it becomes possible to provide more detailed information. The incorporation of market news and event information is done using, for example, news feeds or event calendars. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input market news and event information into AI and have the AI ​​perform the incorporation into the alerts. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, generation unit, and alarm unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects past price movements by the control unit 46A of the smart device 14. The analysis unit analyzes past price movements using AI by the specific processing unit 290 of the data processing unit 12. The prediction unit predicts future price movements by the specific processing unit 290 of the data processing unit 12. The generation unit generates the predicted future price movements as a chart by the control unit 46A of the smart device 14. The alarm unit issues an emergency alarm by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, generation unit, and alarm unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects past price movements by the control unit 46A of the smart glasses 214. The analysis unit analyzes past price movements using AI by the identification processing unit 290 of the data processing unit 12. The prediction unit predicts future price movements by the identification processing unit 290 of the data processing unit 12. The generation unit generates the predicted future price movements as a chart by the control unit 46A of the smart glasses 214. The alarm unit issues an emergency alarm by the identification processing unit 290 of the data processing unit 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, generation unit, and alarm unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects past price movements using the control unit 46A of the headset terminal 314. The analysis unit analyzes past price movements using AI, for example, the specific processing unit 290 of the data processing unit 12. The prediction unit predicts future price movements, for example, the specific processing unit 290 of the data processing unit 12. The generation unit generates the predicted future price movements as a chart, for example, using the control unit 46A of the headset terminal 314. The alarm unit issues an emergency alarm, for example, the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, generation unit, and alarm unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects past price movements by the control unit 46A of the robot 414. The analysis unit analyzes past price movements using AI by the specific processing unit 290 of the data processing unit 12. The prediction unit predicts future price movements by the specific processing unit 290 of the data processing unit 12. The generation unit generates the predicted future price movements as a chart by the control unit 46A of the robot 414. The alarm unit issues an emergency alarm by the specific processing unit 290 of the data processing unit 12.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The market forecasting system may also include a historical analysis unit that considers the user's trading history. This unit collects the user's past trading data and analyzes trading trends and patterns. For example, if a user frequently trades a particular stock, the system can prioritize providing information about that stock. It can also provide appropriate investment advice based on the user's trading style (short-term, medium-term, long-term). Furthermore, the historical analysis unit can consider the user's risk tolerance and issue warnings to avoid high-risk trades. This enables the provision of personalized information based on the user's trading history.

[0103] The data collection unit can collect regional market data, taking into account the user's geographical location. For example, if the user is in Asia, it will prioritize collecting data for the Asian market. If the user is in Europe, it will prioritize collecting data for the European market. This allows the system to provide highly relevant market data based on the user's geographical location. The data collection unit can also dynamically change the target region for data collection in response to the user's movement. This ensures that users always have access to the latest market information, no matter where they are.

[0104] The analysis department can estimate user emotions and adjust the analysis method based on those estimated emotions. For example, if a user is feeling anxious, the analysis method prioritizes risk avoidance. If a user is feeling secure, the analysis method prioritizes risk-taking. If a user is excited, the analysis method prioritizes high-volatility markets. By adjusting the analysis method according to the user's emotions, more appropriate analysis becomes possible. User emotions are estimated using methods such as facial recognition and survey results.

[0105] The prediction unit can improve accuracy by combining different prediction models when forecasting future price movements. For example, it can combine the ARIMA model and the LSTM model for prediction, the Random Forest and Support Vector Machine for prediction, or the Neural Network and Bayesian estimation for prediction. This improves prediction accuracy by combining different prediction models. The combination of different prediction models is done using, for example, the ARIMA model and the LSTM model. The prediction unit can input different prediction models into the AI ​​and have the AI ​​perform the prediction.

[0106] The generation unit can combine and display different chart formats when generating charts of predicted future price movements. For example, it can combine line charts and bar charts, candlestick charts and histograms, or scatter plots and area charts. This allows for the provision of more multifaceted information by combining different chart formats. The combination of different chart formats is done using candlestick charts, line charts, etc. The generation unit can input data from different chart formats into the AI ​​and have the AI ​​generate the charts.

[0107] The alarm unit can estimate the user's emotions and adjust the timing of emergency alarms based on those emotions. For example, if the user is feeling anxious, an emergency alarm will be issued earlier. If the user is feeling at ease, an emergency alarm will be issued at the normal time. If the user is excited, an emergency alarm will be issued immediately. By adjusting the timing of emergency alarms according to the user's emotions, more appropriate alarms can be issued. User emotions are estimated using methods such as facial recognition and survey results.

[0108] The data collection unit can filter historical price movement data by considering specific market events and news. For example, it can collect data when important economic indicators are released and consider their impact. It can also collect data when companies announce their earnings and consider its impact. Similarly, it can collect data during political events and elections and consider its impact. This allows for more accurate data collection by taking market events and news into account. Market events and news are considered using tools such as news feeds and event calendars.

[0109] The analysis unit can improve accuracy by combining different analysis algorithms when analyzing historical price movement data. For example, it can combine moving averages and Bollinger Bands, RSI and MACD, or Fibonacci retracements and Elliott Wave Theory. By combining different analysis algorithms, the accuracy of the analysis is improved. These combinations of different analysis algorithms are performed using methods such as regression analysis and clustering. The analysis unit can input different analysis algorithms into an AI and have the AI ​​perform the analysis.

[0110] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is nervous, it provides a simple and highly visible display method. If the user is relaxed, it provides a display method that includes detailed information. If the user is in a hurry, it provides a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate information. The estimation of the user's emotions is performed using methods such as facial recognition and survey results.

[0111] The generation unit can estimate the user's emotions and adjust the color and design of the generated chart based on the estimated emotions. For example, if the user is nervous, it generates a chart with calm colors. If the user is relaxed, it generates a chart with bright colors. If the user is in a hurry, it generates a chart with highly visible colors. By adjusting the color and design of the chart according to the user's emotions, it becomes possible to provide information with higher visibility. The estimation of the user's emotions is performed using methods such as facial recognition and survey results.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The data collection unit collects historical price movements. Historical price movements include, but are not limited to, data from the past year, data from the past five years, etc. The data collection unit collects historical price movements using, for example, visual analysis tools. Visual analysis tools include, for example, charting tools and data visualization software. Step 2: The analysis unit uses AI to analyze past price movements and detect specific patterns. AI technologies such as neural networks and deep learning are used. For example, the analysis unit uses an AI model that takes past price movement data as input and outputs specific patterns. Step 3: The prediction unit uses an AI model to predict future price movements. AI models include, for example, regression models and time series forecasting models. The prediction unit makes predictions using an AI model that takes past price movement data as input and outputs future price movements. Step 4: The generation unit generates a chart of the predicted future price movement. The generated charts may include, for example, candlestick charts, line charts, and bar charts. The generation unit generates the chart using, for example, a system that takes future price movement data as input and outputs a chart. Step 5: The alarm unit issues an emergency alert based on specific criteria determined by the prediction unit. These criteria may include, for example, price fluctuations or a surge in trading volume. The alarm unit issues an alert using a system that takes future price movement data as input and outputs an emergency alert.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection unit collects past price movements, An analysis unit analyzes past price movements collected by the aforementioned collection unit, A prediction unit that predicts future price movements based on the analysis results obtained by the aforementioned analysis unit, A generation unit generates a chart of the future price movements predicted by the prediction unit, The aforementioned prediction unit issues an emergency alert based on specific criteria, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect historical price movements using visual analysis tools. The system according to feature 1.

3. The aforementioned analysis unit is Using AI to analyze past price movements and detect specific patterns. The system according to feature 1.

4. The prediction unit, Predicting future price movements using AI models The system according to feature 1.

5. The generating unit is Generates a candlestick chart showing predicted future price movements. The system according to feature 1.

6. The alarm unit is, Issue emergency alerts and notify users based on specific criteria. The system according to feature 1.

7. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of data collection for historical price movements based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is When collecting historical price movement data, filter the data to take into account specific market events and news. The system according to feature 1.

9. The aforementioned collection unit is When collecting historical price movement data, collect data from different timeframes simultaneously. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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