Disclosed are various embodiments for data processing using decision tree data structures to implement artificial intelligence in an ingestion process. At least one computing device may be employed to access reference data from a data store accessible to the at least one computing device and parse the reference data using a natural language processor to identify relevant data for storage in at least one decision tree data structure. An ingestion process is applied to receive input data from at least one client device remotely over a transmission network. The at least one decision tree data structure is queried to identify a node in the at least one decision tree data structure that corresponds to a state of the ingestion process. A first metric for a first child node and a second metric for a second child node are generated using the input data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer-readable medium having a program executable in at least one computing device, the program comprising program code that, when executed by the at least one computing device, causes the at least one computing device to: access reference data from a data store accessible to the at least one computing device; parse the reference data using a natural language processor to identify relevant data; store the relevant data identified in at least one decision tree data structure; apply an ingestion process to receive input data from at least one client device, the input data being received by the at least one computing device from the client device remotely over a transmission network; query the at least one decision tree data structure to identify a node in the at least one decision tree data structure that corresponds to a state of the ingestion process; and generate a first metric for a first child node and a second metric for a second child node using the input data, the first child node and the second child node being children of the node identified in the at least one decision tree data structure. 7. The non-transitory computer-readable medium of claim 1, wherein the program further comprises program code that, when executed, causes the at least one computing device to: identify a binary response from the input data; and apply a receiver operating curve (ROC), wherein the first metric for the first child node and the second metric for the second child node are generated based on the receiver operating curve (ROC). 2. The non-transitory computer-readable medium of claim 1, wherein the program further comprises program code that, when executed, causes the at least one computing device to identify a subsequent state of the ingestion process based on the node identified in the at least one decision tree data structure. 3. The non-transitory computer-readable medium of claim 1, wherein the program further comprises program code that, when executed, causes the at least one computing device to: identify an unrecognizable term in the input data; and communicate with a service external to the at least one computing device to define the unrecognizable term through an application programming interface (API) of the service. 4. The non-transitory computer-readable medium of claim 1, wherein the at least one client device comprises a plurality of client devices; and wherein the program further comprises program code that, when executed, causes the at least one computing device to: access input data provided from a first one of the plurality of client devices obtained during the state of the ingestion process; access input data provided from a second one of the plurality of client devices obtained during the state of the ingestion process; and identify that a conflict exists between the input data provided from the first one of the plurality of client devices and the input data provided from the second one of the plurality of client devices. 5. The non-transitory computer-readable medium of claim 1, wherein the program further comprises program code that, when executed, causes the at least one computing device to determine a confidence metric from the input data, wherein the first metric and the second metric are generated using the confidence metric. 6. The non-transitory computer-readable medium of claim 5, wherein the program further comprises program code that, when executed, causes the at least one computing device to determine a credibility metric associated with the client device from which the input data is received, wherein the confidence metric is determined using the credibility metric. 8. A system, comprising: a server computing device in data communication with at least one client device over a network; program instructions executable by the at least one server computing device that, when executed by the at least one server computing device cause the at least one server computing device to: access reference data from a data store accessible to the at least one computing device; parse the reference data using a natural language processor to identify relevant data; store the relevant data identified in at least one decision tree data structure; apply an ingestion process to receive input data from the at least one client device, the input data being received by the at least one computing device from the client device remotely over a transmission network; query the at least one decision tree data structure to identify a node in the at least one decision tree data structure that corresponds to a state of the ingestion process; and generate a first metric for a first child node and a second metric for a second child node using the input data, the first child node and the second child node being children nodes of the node identified in the at least one decision tree data structure. 9. The system of claim 8, further comprising program instructions that, when executed, cause the at least one computing device to identify a subsequent state of the ingestion process based on the node identified in the at least one decision tree data structure. 10. The system of claim 8, further comprising program instructions that, when executed, cause the at least one computing device to: identify an unrecognizable term in the input data; and communicate with a service external to the at least one computing device to define the unrecognizable term through an application programming interface (API) of the service. 11. The system of claim 8, wherein the at least one client device comprises a plurality of client devices; and wherein the system further comprises program instructions that, when executed, cause the at least one computing device to: access input data provided from a first one of the plurality of client devices obtained during the state of the ingestion process; access input data provided from a second one of the plurality of client devices obtained during the state of the ingestion process; and identify that a conflict exists between the input data provided from the first one of the plurality of client devices and the input data provided from the second one of the plurality of client devices. 12. The system of claim 8, further comprising program instructions that, when executed, cause the at least one computing device to determine a confidence metric from the input data, wherein the first metric and the second metric are generated using the confidence metric. 13. The system of claim 12, further comprising program instructions that, when executed, cause the at least one computing device to determine a credibility metric associated with the client device from which the input data is received, wherein the confidence metric is determined using the credibility metric. 14. The system of claim 8, further comprising program instructions that, when executed, cause the at least one computing device to: identify a binary response from the input data; and apply a receiver operating curve (ROC), wherein the first metric for the first child node and the second metric for the second child node are generated based on the receiver operating curve (ROC). 15. A computer-implemented method, comprising: accessing, by at least one computing device comprising at least one hardware processor, reference data from a data store accessible to the at least one computing device; parsing, by the at least one computing device, the reference data using a natural language processor to identify relevant data; storing, by the at least one computing device, the relevant data identified in at least one decision tree data structure; applying, by the at least one computing device, an ingestion process to receive input data from at least one client device, the input data being received by the at least one computing device from the client device remotely over a transmission network; querying, by the at least one computing device, the at least one decision tree data structure to identify a node in the at least one decision tree data structure that corresponds to a state of the ingestion process; and generating, by the at least one computing device, a first metric for a first child node and a second metric for a second child node using the input data, the first child node and the second child node being children nodes of the node identified in the at least one decision tree data structure. 16. The computer-implemented method of claim 15, further comprising identifying, by the at least one computing device, a subsequent state of the ingestion process based on the node identified in the at least one decision tree data structure. 17. The computer-implemented method of claim 15, further comprising: identifying, by the at least one computing device, an unrecognizable term in the input data; and communicating, by the at least one computing device, with a service external to the at least one computing device to define the unrecognizable term through an application programming interface (API) of the service. 18. The computer-implemented method of claim 15, wherein the at least one client device comprises a plurality of client devices; and wherein the computer-implemented method further comprises: accessing, by the at least one computing device, input data provided from a first one of the plurality of client devices obtained during the state of the ingestion process; accessing, by the at least one computing device, input data provided from a second one of the plurality of client devices obtained during the state of the ingestion process; and identifying, by the at least one computing device, that a conflict exists between the input data provided from the first one of the plurality of client devices and the input data provided from the second one of the plurality of client devices. 19. The computer-implemented method of claim 15, further comprising determining, by the at least one computing device, a confidence metric from the input data, wherein the first metric and the second metric are generated using the confidence metric. 20. The computer-implemented method of claim 19, further comprising determining, by the at least one computing device, a credibility metric associated with the client device from which the input data is received, wherein the confidence metric is determined using the credibility metric.