Methods, computer-readable media and systems are disclosed for building, deploying, operating, and maintaining an intelligent dynamic form in which a trained machine learning (ML) model is embedded. A universe of questions is associated with a plurality of output classifiers, which could represent eligibilities for respective benefits. The questions are partitioned into blocks. Each block can be associated with one or more of the classifiers, and each classifier can have a dependency on one or more blocks. An ML model is trained to make inferences from varied combinations of responses to questions and pre-existing data, and determine probabilities or predictions of values of the output classifiers. Based on outputs of the trained model, blocks of questions can be selectively rendered. The trained model is packaged with the question blocks and other components suitably for offline deployment. Uploading collected responses and maintenance of the dynamic form are also disclosed.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for providing a dynamic form, the method comprising: training a machine learning model using training data which associates input data values of the training data with corresponding output classifications of form blocks, each of the form blocks including one or more input data fields; building a predictive module comprising the trained machine learning model, wherein the predictive module is configured to map input data values at runtime to parameters associated with at least some of the output classifications; linking the input data fields of at least one of the form blocks with the predictive module so that the input data values at runtime, when entered into the one or more input data fields of the at least one form block, are provided to the predictive module; configuring a decision module to receive at least one of the parameters associated with the at least some of the output classifications from the predictive module, and to determine whether a given one of the form blocks is to be presented; building the dynamic form by packaging the form blocks, the predictive module, and the decision module. 2. The method of claim 1, further comprising deploying the dynamic form on a mobile device for offline use. 3. The method of claim 1, further comprising linking pre-existing user data as additional input to the predictive module. 4. The method of claim 1, wherein at least one of the parameters is a probability of a corresponding value of one of the output classifications. 5. The method of claim 1, wherein at least one of the parameters is a predicted value of one of the output classifications. 6. The method of claim 1, wherein at least one of the parameters is an indication whether all output classifications associated with a particular form block among the form blocks are determined. 7. The method of claim 1, wherein the machine learning model uses one or more of: a Bayes classifier, a decision tree, logistic regression, a model ensemble, multinomial logistic regression, a neural network, a random forest, or a support vector machine. 8. One or more computer-readable media comprising instructions, which when performed by one or more processors, cause performance of operations comprising: training a machine learning model using training data which associates values of one or more input data fields with corresponding values of at least one output classification; building a predictive module comprising the trained machine learning model, wherein the predictive module is configured to receive at least one input data value at runtime and output one or more parameters associated with respective output classifications; linking input fields of one or more of a plurality of form blocks with the predictive module so that values entered into the input fields of the one or more of the plurality of form blocks are provided to the predictive module; configuring a decision module to receive at least one of the parameters associated with respective output classifications from the predictive module, and to determine whether a next form block among the plurality of form blocks is to be presented; building the intelligent dynamic form by packaging the plurality of form blocks, the predictive module, and the decision module. 9. The one or more computer-readable media of claim 8, wherein the operations further comprise receiving the form blocks. 10. The one or more computer-readable media of claim 8, wherein the operations further comprise receiving the training data, the training data comprising a plurality of records, each of the plurality of records having at least one input data value and one or more of the output classifications. 11. The one or more computer-readable media of claim 8, wherein the machine learning model uses one or more of a Bayes classifier, a decision tree, logistic regression, a model ensemble, multinomial logistic regression, a neural network, a random forest, or a support vector machine. 12. The one or more computer-readable media of claim 8, wherein the operations further comprise: obtaining additional training data; updating the machine learning model and the predictive module using at least the additional training data as input to the machine learning model; updating the dynamic form with the updated predictive module. 13. The one or more computer-readable media of claim 12, wherein the additional training data includes input data values for at least one additional form block and/or at least one additional output classification. 14. The one or more computer-readable media of claim 8, wherein the trained machine learning model is represented as a set of rules in the predictive module. 15. A computer-implemented method for managing how a form is rendered, comprising: rendering a first block of questions in a graphical user interface; invoking a predictive module based at least partly on responses to the first block of questions, to obtain a first parameter associated with an output classification associated with a second block of questions; and based on the first parameter, determining whether to render the second block of questions in the graphical user interface; wherein the predictive module comprises a trained machine learning model having been trained using one or more of: a Bayes classifier, a decision tree, logistic regression, a model ensemble, multinomial logistic regression, a neural network, a random forest, or a support vector machine. 16. The method of claim 15, wherein the first parameter is a predicted value of the output classification associated with the second block of questions. 17. The method of claim 15, wherein the determination is a first determination, the method further comprising: in response to the first determination, rendering the second block of questions in the graphical user interface; invoking the predictive module based at least partly on responses to the second block of questions, to obtain a second parameter associated with an output classification associated with a third block of questions; based on the second parameter, making a second determination whether to render the third block of questions in the graphical user interface; and in response to the second determination, not rendering the third block of questions. 18. The method of claim 15, wherein the method is performed offline on a mobile device. 19. The method of claim 15, wherein the predictive module is implemented in one or more of: JavaScript, Dart, or WebAssembly. 20. The method of claim 15, wherein the rendering is performed on a client device in response to accessing a webpage from a server, and the invoking and the determining are performed on a server.