Computational naturally-occurring-action interpreter for machine learning

1 . A method comprising:

receiving by a processor-based device action data representative of a sequence of actions resulting in an outcome, the sequence of actions, the resulting outcome, and respective properties associated with the sequence of actions and the resulting outcome defining a first observable process;

transforming by the processing-based device the action data into a normalized representation recognizable by a machine learning analysis engine, wherein the machine learning analysis engine is configured to access and process one or more databases maintaining multiple records of previously transformed normalized representations of other different processes with corresponding sequences of action, resulting outcomes, and properties; and

processing the normalized representation by the machine learning analysis engine to generate, based on correlations determined from the normalized representation of the first observable process and the multiple records of the previously transformed normalized representations of the other processes, an alternative process, for the first observable process, comprising one or more of: alternative sequence of actions determined from the correlations, or an alternative outcome determined from the correlations.

2 . The method of claim 1 , wherein the action data comprises a sequence of chemical actions resulting in a product, the sequence of actions and the product being described, at least in part, through human-language-based observations, and wherein the alternative process comprises one or more of: a simplified sequence of chemical actions to produce the product, or alternative one or more product syntheses through adjustments to the sequence of chemical actions.

3 . The method of claim 1 , wherein at least some of the action data includes property data representing measurable properties for one or more of: at least one of the sequence actions, or the resulting outcome.

4 . The method of claim 1 , wherein transforming the action data into the normalized representation comprises:

transforming the sequence of actions and the resulting outcome into a directed acyclic graph (DAG) representation comprising nodes joined by edges.

5 . The method of claim 4 , further comprising converting the DAG representation into an element representation storable in a data storage device.

6 . The method of claim 4 , wherein transforming the sequence of actions and the resulting outcome into the DAG representation comprises:

applying natural language processing to the sequence of actions and the resulting outcome.

7 . The method of claim 1 , wherein transforming the action data into the normalized representation comprises:

transforming the sequence of actions and resulting outcome into coded normalized expressions.

8 . The method of claim 7 , wherein transforming the sequence of actions and resulting outcome into the coded normalized expressions comprises transforming the sequence of actions and resulting outcome into algebraic expressions.

9 . A computing system comprising:

an input stage to receive one or more input data records; and

a processor-based controller, implementing one or more learning engines, in communication with a memory device to store programmable instructions, to:

receive action data representative of a sequence of actions resulting in an outcome, the sequence of actions, the resulting outcome, and respective properties associated with the sequence of actions and the resulting outcome defining a first observable process;

transform the action data into a normalized representation recognizable by a machine learning analysis engine implemented on the processor-based controller, wherein the machine learning analysis engine is configured to access and process one or more databases maintaining multiple records of previously transformed normalized representations of other different processes with corresponding sequences of action, resulting outcomes, and properties; and

process the normalized representation by the machine learning analysis engine to generate, based on correlations determined from the normalized representation of the first observable process and the multiple records of the previously transformed normalized representations of the other processes, an alternative process, for the first observable process, comprising one or more of: alternative sequence of actions determined from the correlations, or an alternative outcome determined from the correlations.

10 . The system of claim 9 , wherein the action data comprises a sequence of chemical actions resulting in a product, the sequence of actions and the product being described, at least in part, through human-language-based observations, and wherein the alternative process comprises one or more of: a simplified sequence of chemical actions to produce the product, or alternative one or more product syntheses through adjustments to the sequence of chemical actions.

11 . The system of claim 9 , wherein at least some of the action data includes property data representing measurable properties for one or more of: at least one of the sequence actions, or the resulting outcome.

12 . The system of claim 9 , wherein the processor-based controller configured to transform the action data into the normalized representation is configured to:

transform the sequence of actions and the resulting outcome into a directed acyclic graph (DAG) representation comprising nodes joined by edges.

13 . The system of claim 12 , wherein the process-based controller is further configured to convert the DAG representation into an element representation storable in a data storage device.

14 . The system of claim 9 , wherein the processor-based controller configured to transform the action data into the normalized representation is configured to:

transform the sequence of actions and resulting outcome into coded normalized expressions.

15 . A non-transitory computer readable media storing a set of instructions, executable on at least one programmable device, to:

receive action data representative of a sequence of actions resulting in an outcome, the sequence of actions, the resulting outcome, and respective properties associated with the sequence of actions and the resulting outcome defining a first observable process;

transform the action data into a normalized representation recognizable by a machine learning analysis engine, wherein the machine learning analysis engine is configured to access and process one or more databases maintaining multiple records of previously transformed normalized representations of other different processes with corresponding sequences of action, resulting outcomes, and properties; and

process the normalized representation by the machine learning analysis engine to generate, based on correlations determined from the normalized representation of the first observable process and the multiple records of the previously transformed normalized representations of the other processes, an alternative process, for the first observable process, comprising one or more of: alternative sequence of actions determined from the correlations, or an alternative outcome determined from the correlations.

16 . The computer readable media of claim 15 , wherein the action data comprises a sequence of chemical actions resulting in a product, the sequence of actions and the product being described, at least in part, through human-language-based observations, and wherein the alternative process comprises one or more of: a simplified sequence of chemical actions to produce the product, or alternative one or more product syntheses through adjustments to the sequence of chemical actions.

17 . The computer readable media of claim 15 , wherein at least some of the action data includes property data representing measurable properties for one or more of: at least one of the sequence actions, or the resulting outcome.