Abstract: A method for generating personalized investment responses that includes receiving, by a processor, input from a user, the input comprising at least one of a text prompt or an audio prompt; processing, by the processor, the input using a first artificial intelligence (AI) model to generate extracted information; generating, by the processor, a plurality of responses using generative AIs with the extracted information as input; connecting, by the processor, the plurality of responses to real-time market data and exclusive datasets to improve quality and relevance of the plurality of responses; and generating, by the processor, a personalized investment recommendation to the user based on the plurality of responses.

Abstract: A method for generating a personalized newsletter. The method may include receiving and analyzing news articles from various sources in real-time or at predetermined intervals; storing and managing user demographic information, financial portfolio data, interests, preferences, and financial education levels; processing the analyzed news articles and user information using a Generative Artificial Intelligence (AI) to create personalized content based on the user's demographic information, financial portfolio, interests, preferences, and financial education level; assembling the personalized content into a newsletter with varying lengths and details tailored to the user; and sending the personalized newsletter to the user via email or other communication methods, wherein the tone and style of the personalized content are adjusted based on the user's preferences.

Abstract: A method for performing investment portfolio balancing that includes receiving, by a processor, data associated with a user; receiving, by the processor, market condition information; generating, by the processor, a plurality of investment recommendations using a first artificial intelligence (AI) model with the data and the market condition information as input; ranking, by the processor, the plurality of investment recommendations based on coherence and accuracy using at least one second AI model; generating, by the processor, finalized investment recommendations based on ranked plurality of investment recommendations; and presenting, by the processor, the finalized investment recommendations to the user for selection, wherein selection of the finalized investment recommendations rebalances an investment portfolio of the user.

Abstract: A method for performing investment portfolio adjustment that includes receiving, by a processor, user portfolio associated with a user, receiving, by the processor, a plurality of news data from various real-time information providers; analyzing and weighing, by the processor, the plurality of news data based on the user portfolio using an artificial intelligence (AI) model; and adjusting, by the processor, the user portfolio based on a weighed plurality of news data.

Abstract: A method for generating personalized investment responses that includes receiving, by a processor, input from a user, the input comprising at least one of a text prompt or an audio prompt; processing, by the processor, the input using a first artificial intelligence (AI) model to generate extracted information; generating, by the processor, a plurality of responses using generative AIs with the extracted information as input; connecting, by the processor, the plurality of responses to real-time market data and exclusive datasets to improve quality and relevance of the plurality of responses; and generating, by the processor, a personalized investment recommendation to the user based on the plurality of responses.

Abstract: A method for generating personalized investment responses comprising receiving, by a processor, input from a user, the input comprising at least one of a text prompt or an audio prompt processing, by the processor, the input using a first artificial intelligence (AI) model to generate extracted information generating, by the processor, a plurality of responses using generative AIs with the extracted information as input; connecting, by the processor, the plurality of responses to real-time market data and exclusive datasets to improve quality and relevance of the plurality of responses; and generating, by the processor, a personalized investment recommendation to the user based on the plurality of responses.

Abstract: A computer-implemented method comprising receiving user preference information, based on the received user preference information, determining one or more user settings, processing data to determine a data condition, wherein, to determine the data condition, a plurality of alternate data inputs of different types are processed and normalized, and applied to a series of operations to generate a forecast having a degree of confidence, and the data condition is compared with third party information; providing an electronic notification indicative of the data condition to the user device, wherein the electronic notification includes the data condition compared with the third party information, a confidence indicator associated with the data condition, and a user prompt; in response to a single user input, the user device generating an instruction to execute the user request; and based on the instruction, executing the user request based on the determined one or more user settings.

Abstract: A method for generating personalized investment responses that includes receiving, by a processor, input from a user, the input comprising at least one of a text or audio prompt; processing, by the processor, the input using an artificial intelligence (AI) model to generate extracted information; generating, by the processor, a plurality of responses using generative AIs with the extracted information as input; and generating, by the processor, a recommendation based on the plurality of responses.

Abstract: A computer-implemented method includes a method comprising using at least one hardware processor to: receive a plurality of data from a plurality of data sources; standardize the plurality of data; tag the standardized plurality of data with one or more companies; train a prediction model to predict a metric for each of the one or more companies based on the standardized plurality of data tagged with that company and historical measurements for that company; and apply the prediction model to new data to predict the metric for at least one of the one or more companies.

Abstract: A computer-implemented method includes a method comprising using at least one hardware processor to: receive a plurality of data from a plurality of data sources; standardize the plurality of data; tag the standardized plurality of data with one or more companies; train a prediction model to predict a metric for each of the one or more companies based on the standardized plurality of data tagged with that company and historical measurements for that company; and apply the prediction model to new data to predict the metric for at least one of the one or more companies.

Abstract: A method for determining present or future trends, and providing a recommendation based on those trends is provided. The method includes receiving raw data, where the raw data is data which has not been cleaned or normalized, cleaning and normalizing the raw data, creating historic data via machine learning, comparing the cleaned and normalized data with the historic data, generating a model based on the compared cleaned and normalized data and the historic data, wherein the model generates one or more determinations, and providing the one or more determinations for use by a recommendation engine or a user. Additionally, a method of collecting specific data is provided. The method includes receiving a survey and additional information provided by a panelist on a mobile application, filtering and organizing the panelists, storing the collected information, providing the stored data to a server for cleaning and normalizing, and providing the panelist with rewards.

Abstract: A system for processing data to generate an output includes an automated tagging system that receives data from a plurality of alternate data providers, each of the plurality of data providers having different types of data; a company financial data unit that provides published information comprising annual reports, press releases, information from the social media of spokespersons or executives, and published pricing information; a modelling system that receives the standardized data of the automated tagging system and the company financial data, the modelling system including one or more handle generators and forecast builders that are applied to the artificial intelligence-based system that employs the neural networks to generate, as an output, a forecast; and a revenue prediction unit that receives the output as the forecast, comprising one or more revenue predictions, to generate a final output as a revenue prediction.

Abstract: A computer-implemented method includes a method comprising using at least one hardware processor to: receive a plurality of data from a plurality of data sources; standardize the plurality of data; tag the standardized plurality of data with one or more companies; train a prediction model to predict a metric for each of the one or more companies based on the standardized plurality of data tagged with that company and historical measurements for that company; and apply the prediction model to new data to predict the metric for at least one of the one or more companies.

Abstract: A computer-implemented method comprising at least one hardware processor to: at each of one or more times, receive, from a user, a company identifier and one or more conditions, generate an alert for the company identifier and the one or more conditions, and associate the alert with the user; receive a plurality of data from a plurality of data sources in real time; update a plurality of signals based on the plurality of data, wherein each of the plurality of signals represents one of a plurality of critical indicators for one of a plurality of companies; and for each alert associated with the user, apply the one or more conditions of the alert to at least one of the plurality of signals to determine whether or not the alert is triggered.

Abstract: A computer-implemented method comprising receiving user preference information, based on the received user preference information, determining one or more user settings, processing data to determine a data condition, wherein, to determine the data condition, a plurality of alternate data inputs of different types are processed and normalized, and applied to a series of operations to generate a forecast having a degree of confidence, and the data condition is compared with third party information; providing an electronic notification indicative of the data condition to the user device, wherein the electronic notification includes the data condition compared with the third party information, a confidence indicator associated with the data condition, and a user prompt; in response to a single user input, the user device generating an instruction to execute the user request; and based on the instruction, executing the user request based on the determined one or more user settings.

Abstract: A computer-implemented method comprising receiving user preference information, based on the received user preference information, determining one or more user settings, processing data to determine a data condition, wherein, to determine the data condition, a plurality of alternate data inputs of different types are processed and normalized, and applied to a series of operations to generate a forecast having a degree of confidence, and the data condition is compared with third party information; providing an electronic notification indicative of the data condition to the user device, wherein the electronic notification includes the data condition compared with the third party information, a confidence indicator associated with the data condition, and a user prompt; in response to a single user input, the user device generating an instruction to execute the user request; and based on the instruction, executing the user request based on the determined one or more user settings.

Abstract: A system for processing data to generate an output includes an automated tagging system that receives data from a plurality of alternate data providers, each of the plurality of data providers having different types of data; a company financial data unit that provides published information comprising annual reports, press releases, information from the social media of spokespersons or executives, and published pricing information; a modelling system that receives the standardized data of the automated tagging system and the company financial data, the modelling system including one or more handle generators and forecast builders that are applied to the artificial intelligence-based system that employs the neural networks to generate, as an output, a forecast; and a revenue prediction unit that receives the output as the forecast, comprising one or more revenue predictions, to generate a final output as a revenue prediction.