Stateful, Real-Time, Interactive, and Predictive Knowledge Pattern Machine

Abstract

This disclosure describes a knowledge pattern machine that is distinct from and goes beyond a traditional artificially intelligent predictive knowledge system employing simple domain-specific numerical regression models. Rather than generating purely quantitative projections within a static set of parameters and data, the disclosed pattern machine uses various layers of artificial intelligence to recognize and derive dynamically evolving predictive patterns and correlations among quantitative and/or qualitative information pertaining to one or multiple domains. The pattern machine extracts knowledge items, including various signals, events, properties, and correlations therebetween, and predicts future trends and evolvements of relating knowledge items to automatically and intelligently answer user queries. The generated predictive answers are rendered as reports updated in real-time without user interference as the underlying data sources evolve over time and are sharable among different users at various levels. The various knowledge items are timestamped and used to further yield a stateful pattern machine.

What is claimed is: 
     
         1 . A knowledge pattern system, comprising:
 a real-time-updated signal database comprising quantifiable and/or qualifiable metrics and signal properties thereof;   a real-time-updated event database comprising singular or repeated occurrences and event properties thereof;   a real-time-updated knowledge graph comprising information entities including a set of signals, a set of events, properties thereof, and relationships between the set of signals, the set of events, and the properties;   a model library comprising a set of trained artificial intelligence models; and   a circuitry configured to:
 automatically update the set of artificial intelligence models; 
 automatically update the signals, signal properties, events, and event properties in the knowledge graph and time-stamp the update; 
 compute and timestamp in real-time future-time values of the signals in the real-time-updated knowledge graph by automatically selecting, fetching, and running artificially intelligent models from the model library; 
 automatically store the future-time values of the signals in the signal database; 
 execute a user query process in combination with the real-time updated knowledge graph, the signal database, and the event database to process a free-form user query to extract at least one signal with corresponding future-time values and/or at least one event; 
 automatically generate a predictive answer to the free-form user query via a simulation process based on the future-time values corresponding to the extracted at least one signal, the predictive answer comprising a future occurrence probability of the at least one event; and 
 automatically convert the predictive answer into a report by selecting a reporting format according to the predictive answer. 
   
     
     
         2 . The system of  claim 1 , wherein the circuitry is configured to automatically perform data crawling from at least one data source to obtain crawled data items to generate, populate, update, and/or time-stamp the real-time-updated signal database and the real-time updated event database. 
     
     
         3 . The system of  claim 2 , wherein the circuitry is configured to automatically update the signals, events, properties thereof, and relationships therebetween in the real-time updated knowledge graph from the crawled data items and/or the real-time updated signal database and/or the real-time updated event database. 
     
     
         4 . The system of  claim 3 , wherein the real-time updated knowledge graph associates the information entities to time-stamps or timelines to operate as a time series and to identify evolving patterns of the information items over time. 
     
     
         5 . The system of  claim 4  wherein the real-time updated knowledge graph is capable of self-expansion and the self-expansion is performed by an auto machine learning layer within the real-time-updated knowledge graph. 
     
     
         6 . The system of  claim 5 , wherein the real-time-updated knowledge graph is configured to self-expand to discover previously unknown information items including qualitative or quantitative signals, events, or relationships therebetween. 
     
     
         7 . The system of  claim 1 , wherein to compute in real-time the future-time values of the signals comprises:
 to identify at least one signal;   to identify a corresponding property associated with the at least one signal;   to select at least one artificial intelligence model from the model library;   to generate test datasets based on the at least one signal and the corresponding property; and   to generate the future-time values of the signalsof the at least one signal associated with the corresponding property using the at least one artificial intelligence model and the test datasets.   
     
     
         8 . The system of  claim 1 , wherein the model library contains at least one pre-computed artificial intelligence model that is real-time updated when underlying training data of the at least one pre-computed artificial intelligence model changes. 
     
     
         9 . The system of  claim 1 , wherein to execute the user query process comprises:
 to receive the free-form user query by a user from a graphical user interface; and   to automatically process the free-form user query to extract the at least one signal and/or the at least one event and corresponding signal properties and/or event properties based on the free-form query, the real-time-updated signal database, the real-time updated event database, and the real-time-updated knowledge graph.   
     
     
         10 . The system of  claim 9 , wherein to automatically generate the predictive answer to the free-form user query via the simulation process based on signal predictions and/or event predictions comprises:
 to automatically identify correlations between the future-time values of the at least one signal and the future occurrence probability of the at least one event; and   to automatically generate a simulation of the correlation as the predictive answer to the free-form user query.   
     
     
         11 . The system of  claim 9 , wherein to automatically process the free-form user query further comprises:
 to preprocess the free-form user query to determine at least one domain for the free-form user query and to perform at least one of text normalization, stemming/lemmatization, tokenization, and text cleansing; and   at least one of:
 determine a type of the free-form user query; 
 refine the free-form user query; and 
 deduplicate the free-form user query based on a historical query database; and 
   wherein to automatically process the free-form user query, the circuitry is configured to extract one or more entities based on pre-trained semantic or syntactic modes and to generate one or more embedding vectors.   
     
     
         12 . The system, of  claim 11 , wherein the circuitry is configured to refine the free-form user query by:
 automatically determining a completeness of the free-form user query based on the preprocessed free-form query and the type of free-form query; and   in response to determining that the free-form user query is incomplete, automatically generating an interactive prompt through the graphical user interface to obtain supplemental information from the user to complete the free-form user query.   
     
     
         13 . The system of  claim 11 , wherein to determine the type of the free-form user query comprises configuring the circuitry to detect the free-form user query as one of normal, out of scope, and biased types. 
     
     
         14 . The system of  claim 11 , wherein the circuitry is configured to extract the at least one signal and/or the at least one event by performing an automatic signal matching and an automatic event matching based on the one or more entities, the one or more embedding vectors, and the real-time-updated signal database and the real-time-updated event database. 
     
     
         15 . The system of  claim 11 , wherein the circuitry is further configured to automatically generate and display at least one recommended follow-up query based on the free-form user query, the predictive answer, and the historical query database. 
     
     
         16 . The system of  claim 1 , wherein the circuitry is configured to generate the report based on a predefined report template automatically selected based on at least one domain of the free-form user query, the at least one signal, and/or the at least one event. 
     
     
         17 . The system of  claim 1 , wherein the report comprises an automatically generated textual information item summarizing the predictive answer to the free-form user query and/or visual presentation comprising graphs, tables, charts, spreadsheets, images, animations, videos, 3D models, virtual reality images/videos, and/or augmented/virtual reality images/videos of the future-time values associated with the at least one signal associated with the at least one event. 
     
     
         18 . The system of  claim 1 , wherein the knowledge pattern system further comprises a visual entity database and the circuitry is further configured to:
 generate a set of visual entities from a set of visual data items; link the visual entities to the set of signals and events via the real-time-update knowledge graph;   execute an intelligent visual information processing pipeline to generate predicted visual entities;   extract a subset of visual entities and predicted visual entities based on the at least one signal, the at least one event, or the predictive answer; and   generate predictive visual content in at least one form of virtual reality, augmented reality, interactive graphics, images, or videos, based on the subset of visual entities to supplement the predictive answer.   
     
     
         19 . The system of  claim 1 , wherein the model library comprises a set of pre-trained semantic or syntactic models to generate one or more embedding vectors for preprocessing the free-form user query, for computing the future-time values, for generating the predictive answer, and/or for converting the predictive answer into the report. 
     
     
         20 . A method for generating and processing knowledge patterns performed by a system comprising a real-time-updated signal database comprising quantifiable and/or qualifiable metrics and signal properties thereof, a real-time-updated event database comprising singular or repeated occurrences and event properties thereof, a real-time-updated knowledge graph comprising information entities including a set of signals, a set of events, properties thereof, and relationships between the set of signals, the set of events, and the properties, a model library comprising a set of trained artificial intelligence models, a memory for storing computer instructions, and at least one processor, the method comprising:
 automatically updating the set of artificial intelligence models;   automatically updating the signals, signal properties, events, and event properties in the knowledge graph and time-stamp the update;   computing and timestamping in real-time future-time values of the signals in the real-time-updated knowledge graph by automatically selecting, fetching, and running artificially intelligent models from the model library;   automatically storing the future-time values of the signals in the signal database;   executing a user query process in combination with the real-time updated knowledge graph, the signal database, and the event database to process a free-form user query to extract at least one signal with corresponding future-time values and/or at least one event;   automatically generating a predictive answer to the free-form user query via a simulation process based on the future-time values corresponding to the extracted at least one signal, the predictive answer comprising a future occurrence probability of the at least one event; and   automatically converting the predictive answer into a report by selecting a reporting format according to the predictive answer.