US 11,836,173 B2
Apparatus and method for generating a schema
Saaransh Mahna, Northborough, MA (US); James Rollins, Fredericksburg, VA (US); Shannon Fee, San Francisco, CA (US); Randy Ulloa, Farmington, CT (US); Erika Granger, Oakland, CA (US); and Jimmy Lin, Washington, DC (US)
Assigned to Banjo Health Inc., Washington, DC (US)
Filed by Banjo Health Inc., Washington, DC (US)
Filed on May 26, 2022, as Appl. No. 17/825,006.
Claims priority of provisional application 63/193,267, filed on May 26, 2021.
Prior Publication US 2022/0382722 A1, Dec. 1, 2022
Int. Cl. G06F 40/00 (2020.01); G06V 30/41 (2022.01); G06V 10/82 (2022.01); G06F 40/30 (2020.01); G06F 16/33 (2019.01); G06F 16/2457 (2019.01); G06F 18/214 (2023.01)
CPC G06F 16/3346 (2019.01) [G06F 16/2457 (2019.01); G06F 18/214 (2023.01); G06F 40/00 (2020.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus for generating a schema, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
display, at a graphical control interface, a content field window, wherein the content field window comprises a decision tree and a structured field window comprising a plurality of structured models and guidelines for a user to input medical information;
receive, as a function of the content field window, a criterion element, wherein the criterion element comprises at least a treatment plan for a medical condition of a patient and a time period required to treat the medical condition, wherein the criterion element further comprises:
an ailment criterion associated with the medical condition of the patient
a clinical criterion, wherein the clinical criterion comprises an age restriction on the patient and proof of a confirmatory diagnosis of the patient by a laboratory report; and
a drug criterion, wherein the drug criterion comprises a chemical clearance threshold for the patient; and
generate a schema comprising at least a confidence interval between one or more courses of action as a function of the criterion element, wherein generating the schema further comprises:
identifying at least a significant term as a function of the criterion element;
receiving at least a training example;
training a machine-learning model as a function of the at least a training example;
generating the schema as a function of the criterion element and the machine-learning model; and
producing an updated schema as a function of a current criterion element;
wherein the machine-learning model is updated as a function of the current criterion element to produce the updated schema, wherein the updated schema comprises a decision tree including probabilistic outcomes and resource costs.