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 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 comprises using at least one hardware processor to: receive a plurality of data related to one or more entities from a plurality of data sources; apply a predictive model to the plurality of data to predict a value of a critical indicator of performance for each of the one or more entities; generate a graphical user interface comprising a watchlist, wherein the watchlist comprises, for each of the one or more entities, an identifier of the entity, an identifier of the critical indicator predicted for the entity, a value associated with the critical indicator predicted for the entity, and a recommendation based on the value associated with the critical indicator predicted for the entity; and provide the graphical user interface to at least one user.

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 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: Techniques to provide content based on detected ambient media are disclosed. In various embodiments, a set of audio features extracted from audio data associated with an audio environment with which a client device is associated are received from the client device. A stored media signature data generated based on media content data from a source other than the client device is used to detect, based at least in part on the set of audio features, a media channel and program that is being played in the audio environment. A secondary content that is selected based at least in part on a semantic content data associated with the detected media channel and program is provided.

Abstract: Techniques to coordinate ambient media detection are disclosed. A request to become active in performing media detection processing with respect to the audio environment is received from a first ambient media detection agent associated with an audio environment. A response denying the request is sent to the first ambient media detection agent if it is determined that a second ambient media detection agent associated with the audio environment currently is active in performing media detection processing with respect to the audio environment.

Abstract: Techniques to provide content based on detected ambient media are disclosed. In various embodiments, a set of audio features extracted from audio data associated with an audio environment with which a client device is associated are received from the client device. A stored media signature data generated based on media content data from a source other than the client device is used to detect, based at least in part on the set of audio features, a media channel and program that is being played in the audio environment. A secondary content that is selected based at least in part on a semantic content data associated with the detected media channel and program is provided.

Abstract: Techniques to coordinate ambient media detection are disclosed. A request to become active in performing media detection processing with respect to the audio environment is received from a first ambient media detection agent associated with an audio environment. A response denying the request is sent to the first ambient media detection agent if it is determined that a second ambient media detection agent associated with the audio environment currently is active in performing media detection processing with respect to the audio environment.

Abstract: Techniques to provide content based on detected ambient media are disclosed. In various embodiments, a set of audio features extracted from audio data associated with an audio environment with which a client device is associated are received from the client device. A stored media signature data generated based on media content data from a source other than the client device is used to detect, based at least in part on the set of audio features, a media channel and program that is being played in the audio environment. A secondary content that is selected based at least in part on a semantic content data associated with the detected media channel and program is provided.