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.

Abstract: The disclosure below describes a knowledge pattern machine that goes beyond and is distinct from a traditional search engine as simple information aggregator. Rather than acting as a search engine of the data itself, the knowledge pattern machine use variously layers of artificial intelligence to discover correlations within the queries and historical data, and to derive and recognize data patterns based on user queries for predictively generating new knowledge items or reports that are of interest to the user. Previous patterns and knowledge items or reports are accumulated and incorporated in identification of new data patterns and new predictive knowledge items or reports in response to future user queries, thus providing a stateful machine. The predictive knowledge items are updated in real-time without user interference as the underlying data sources evolve overtime. The data patterns and knowledge items are organized hierarchically and may be shared among different users at various levels.

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.

Abstract: The disclosure below describes a knowledge pattern machine that goes beyond and is distinct from a traditional search engine as simple information aggregator. Rather than acting as a search engine of the data itself, the knowledge pattern machine use variously layers of artificial intelligence to discover correlations within the queries and historical data, and to derive and recognize data patterns based on user queries for predictively generating new knowledge items or reports that are of interest to the user. Previous patterns and knowledge items or reports are accumulated and incorporated in identification of new data patterns and new predictive knowledge items or reports in response to future user queries, thus providing a stateful machine. The predictive knowledge items are updated in real-time without user interference as the underlying data sources evolve overtime. The data patterns and knowledge items are organized hierarchically and may be shared among different users at various levels.

Abstract: The disclosure below describes a knowledge pattern machine that goes beyond and is distinct from a traditional search engine as simple information aggregator. Rather than acting as a search engine of the data itself, the knowledge pattern machine use variously layers of artificial intelligence to discover correlations within the queries and historical data, and to derive and recognize data patterns based on user queries for predictively generating new knowledge items or reports that are of interest to the user. Previous patterns and knowledge items or reports are accumulated and incorporated in identification of new data patterns and new predictive knowledge items or reports in response to future user queries, thus providing a stateful machine. The predictive knowledge items are updated in real-time without user interference as the underlying data sources evolve overtime.

Abstract: The disclosure below describes a knowledge pattern machine that goes beyond and is distinct from a traditional search engine as simple information aggregator. Rather than acting as a search engine of the data itself, the knowledge pattern machine use variously layers of artificial intelligence to discover correlations within the queries and historical data, and to derive and recognize data patterns based on user queries for predictively generating new knowledge items or reports that are of interest to the user. Previous patterns and knowledge items or reports are accumulated and incorporated in identification of new data patterns and new predictive knowledge items or reports in response to future user queries, thus providing a stateful machine. The predictive knowledge items are updated in real-time without user interference as the underlying data sources evolve overtime. The data patterns and knowledge items are organized hierarchically and may be shared among different users at various levels.