Directed trajectories through communication decision tree using iterative artificial intelligence

A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including:

• accessing a data structure representing a decision tree for assigning a job, wherein the decision tree includes a sequenced set of decision nodes and is configured to perform iterative decisions that correspond to progressive filtering of workers, and wherein, for each decision node of the sequenced set of decision nodes, each of one or more trajectory extensions to the decision node triggers an intermediate or final decision pertaining to configuring a worker request to perform the job;
• initiating a trajectory of the decision tree, the trajectory being associated with a set of workers for potential assignment of the job;
• detecting that the trajectory has reached a first decision node of the sequenced set of decision nodes in the decision tree;
• retrieving a first set of node-specific learned parameters generated by training a first machine-learning model with a first training data set, wherein the first set of node-specific learned parameters defines how unstructured data is to be transformed into structured feature data;
• accessing a plurality of worker-associated unstructured data sets, each of the plurality of worker-associated unstructured data sets corresponding to a worker of the set of workers and including unstructured data;
• and
• executing one or more first iterations of the first machine-learning model configured with the first set of node-specific learned parameters and using the plurality of worker-associated unstructured data sets, wherein the execution of the one or more first iterations of the first machine-learning model generates a first result that includes a plurality of worker feature data sets, each of the plurality of worker feature data sets corresponding to a worker of the set of workers and indicating an extent to which the worker-associated unstructured data set corresponding to the worker represents each of a set of features, wherein each of the plurality of worker feature data sets is structured data, and wherein the set of features is defined based on the first set of node-specific learned parameters;
• identifying, based on the plurality of worker feature data sets, a subset of the set of workers for continued evaluation for the job;
• detecting that the trajectory has reached a second decision node of the sequenced set of decision nodes in the decision tree;
• retrieving a second set of node-specific learned parameters generated by training a second machine-learning model with a second training data set, wherein the second set of node-specific learned parameters defines how structured data is to be transformed;
• executing one or more second iterations of the second machine-learning model configured with the second set of node-specific learned parameters and using a subset of the plurality of worker feature data sets, wherein each of the subset of the plurality of worker feature data sets corresponds to a worker in the subset of the set of workers, and wherein execution of the one or more second iterations of the second machine-learning model generates a second result that indicates, for each worker of at least one of the subset of the set of workers, an estimated absolute or relative degree of correspondence between the job and the worker;
• and
• triggering a communication action that represents the second result.

A system comprising:

• one or more data processors;
• and
• a non-transitory computer readable storage medium containing instructions which when executed on the one or more data processors, cause the one or more data processors to perform actions including:
• accessing a data structure representing a decision tree for assigning a job, wherein the decision tree includes a sequenced set of decision nodes and is configured to perform iterative decisions that correspond to progressive filtering of workers, and wherein, for each decision node of the sequenced set of decision nodes, each of one or more trajectory extensions to the decision node triggers an intermediate or final decision pertaining to configuring a worker request to perform the job;
• initiating a trajectory of the decision tree, the trajectory being associated with a set of workers for potential assignment of the job;
• detecting that the trajectory has reached a first decision node of the sequenced set of decision nodes in the decision tree;
• retrieving a first set of node-specific learned parameters generated by training a first machine-learning model with a first training data set, wherein the first set of node-specific learned parameters defines how unstructured data is to be transformed into structured feature data;
• accessing a plurality of worker-associated unstructured data sets, each of the plurality of worker-associated unstructured data sets corresponding to a worker of the set of workers and including unstructured data;
• and
• executing one or more first iterations of the first machine-learning model configured with the first set of node-specific learned parameters and using the plurality of worker-associated unstructured data sets, wherein the execution of the one or more first iterations of the first machine-learning model generates a first result that includes a plurality of worker feature data sets, each of the plurality of worker feature data sets corresponding to a worker of the set of workers and indicating an extent to which the worker-associated unstructured data set corresponding to the worker represents each of a set of features, wherein each of the plurality of worker feature data sets is structured data, and wherein the set of features is defined based on the first set of node-specific learned parameters;
• identifying, based on the plurality of worker feature data sets, a subset of the set of workers for continued evaluation for the job;
• detecting that the trajectory has reached a second decision node of the sequenced set of decision nodes in the decision tree;
• retrieving a second set of node-specific learned parameters generated by training a second machine-learning model with a second training data set, wherein the second set of node-specific learned parameters defines how structured data is to be transformed;
• executing one or more second iterations of the second machine-learning model configured with the second set of node-specific learned parameters and using a subset of the plurality of worker feature data sets, wherein each of the subset of the plurality of worker feature data sets corresponds to a worker in the subset of the set of workers, and wherein execution of the one or more second iterations of the second machine-learning model generates a second result that indicates, for each worker of at least one of the subset of the set of workers, an estimated absolute or relative degree of correspondence between the job and the worker;
• and
• triggering a communication action that represents the second result.