Architecture for data processing and user experience to provide decision support

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.

1 . A system for processing data to generate an output, the system comprising:
 memory that stores one or more software modules; and   at least one hardware processor that executes the one or more software modules to receive data from a plurality of data providers, wherein the data comprise a plurality of consumer transactions,   standardize the data received from the plurality of data providers into a common format,   for each of the plurality of consumer transactions in the data, automatically classify that consumer transaction into a brand using first artificial intelligence that employs a neural network trained using back propagation, and tag that consumer transaction with the brand into which that consumer transaction was classified,   receive published information for at least one company associated with one or more brands with which the plurality of consumer transactions have been tagged, and   generate a forecast of performance of the at least one company based on the published information and the plurality of consumer transactions that have been tagged with the one or more brands associated with the at least one company using second artificial intelligence that employs a neural network, and   generate a final output based on the forecast of performance, wherein the final output comprises a recommendation to buy, hold, or sell a stock of the at least one company.   
     
     
         2 .- 3 . (canceled) 
     
     
         4 . (canceled) 
     
     
         5 . The system of  claim 1 , wherein a rule-based or other deterministic approach is used to extract features used by each neural network. 
     
     
         6 . The system of  claim 1 , wherein the at least one hardware processor executes the one or more software modules to, for each of the plurality of data providers, apply one of a plurality of adapters to the data received from that data provider to normalize, deduplicate, and classify the data received from that data provider. 
     
     
         7 .- 11 . (canceled) 
     
     
         12 . The system of  claim 1 , wherein the plurality of consumer transactions comprise purchase transactions. 
     
     
         13 . The system of  claim 12 , wherein the purchase transactions comprise a time series of one or both of debit card transactions and credit card transactions. 
     
     
         14 . The system of  claim 1 , wherein the plurality of consumer transactions comprises consumer engagements with an application. 
     
     
         15 . The system of  claim 1 , wherein the final output comprises an order to buy or sell a stock of the at least one company. 
     
     
         16 . The system of  claim 1 , wherein the final output comprises a prediction of a future stock price of the at least one company. 
     
     
         17 . The system of  claim 1 , wherein the final output comprises a prediction of a value of at least one metric of the at least one company. 
     
     
         18 . The system of  claim 17 , wherein the at least one metric comprises revenue. 
     
     
         19 . The system of  claim 17 , wherein the at least one metric comprises a number of users. 
     
     
         20 . The system of  claim 17 , wherein the at least one metric comprises a time spent with an application. 
     
     
         21 . The system of  claim 17 , wherein the at least one metric comprises a number of store visits. 
     
     
         22 . The system of  claim 1 , wherein the final output comprises an indication of whether the at least one company is overperforming, underperforming, or neutral. 
     
     
         23 . The system of  claim 1 , wherein the final output comprises a signal strength vector derived based on a comparison of the forecast of performance to a benchmark. 
     
     
         24 . The system of  claim 1 , wherein generating the forecast of performance comprises:
 generating at least one panel based on a subset of the plurality of consumer transactions that satisfies one or more criteria; and   applying the neural network of the second artificial intelligence to the at least one panel.   
     
     
         25 . The system of  claim 1 , wherein generating the forecast of performance comprises:
 generating a plurality of panels, wherein each of the plurality of panels is based on a subset of the plurality of consumer transactions that satisfies one or more criteria;   using the second artificial intelligence to generate a forecast of an indicator of performance for each of the plurality of panels; and   assembling the forecasts of the indicators of performance for the plurality of panels into the forecast of performance based on weightings associated with the indicators of performance.   
     
     
         26 . A method comprising using at least one hardware processor to:
 receive data from a plurality of data providers, wherein the data comprise a plurality of consumer transactions;   standardize the data received from the plurality of data providers into a common format;   for each of the plurality of consumer transactions in the data, automatically classify that consumer transaction into a brand using first artificial intelligence that employs a neural network trained using back propagation, and tag that consumer transaction with the brand into which that consumer transaction was classified;   receive published information for at least one company associated with one or more brands with which the plurality of consumer transactions have been tagged; and   generate a forecast of performance of the at least one company based on the published information and the plurality of consumer transactions that have been tagged with the one or more brands associated with the at least one company using second artificial intelligence that employs a neural network; and   generate a final output based on the forecast of performance, wherein the final output comprises a recommendation to buy, hold, or sell a stock of the at least one company.   
     
     
         27 . A non-transitory computer-readable medium having instructions stored thereon, wherein the instructions, when executed by a processor, cause the processor to:
 receive data from a plurality of data providers, wherein the data comprise a plurality of consumer transactions;   standardize the data received from the plurality of data providers into a common format;   for each of the plurality of consumer transactions in the data, automatically classify that consumer transaction into a brand using first artificial intelligence that employs a neural network trained using back propagation, and tag that consumer transaction with the brand into which that consumer transaction was classified;   receive published information for at least one company associated with one or more brands with which the plurality of consumer transactions have been tagged; and   generate a forecast of performance of the at least one company based on the published information and the plurality of consumer transactions that have been tagged with the one or more brands associated with the at least one company using second artificial intelligence that employs a neural network; and   generate a final output based on the forecast of performance, wherein the final output comprises a recommendation to buy, hold, or sell a stock of the at least one company.