# US 2025/0094841 A1 · Hybrid Machine Learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0094841 A1 - Document ID: US20250094841A1 - Status: Published application - Kind: A1 - Application number: 18/964,203 - Filed: 2024-11-29 - Published: 2025-03-20 - Earliest claimed: 2020-08-25 - Assignees: Alteryx, Inc. - Applicants: Alteryx, Inc. - Inventors: Dylan Blanchard; Tyler Heinl; Ronald Manfred Hochmuth - Web page: https://patentgrep.com/patent/US20250094841A1 ## CPC - G06N 5/04: Inference or reasoning models - G06F 16/90328: {using search space presentation or visualization, e.g. category or range presentation and selection} - G06F 18/2113: by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06F 18/2178: {based on feedback of a supervisor} - G06F 18/22: Matching criteria, e.g. proximity measures - G06N 3/096: Transfer learning - G06N 3/0985: Hyperparameter optimisation; Meta-learning; Learning-to-learn - G06N 3/10: Interfaces, programming languages or software development kits, e.g. for simulating neural networks - G06N 5/045: Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence - G06N 20/00: Machine learning - G06F 16/9035: Filtering based on additional data, e.g. user or group profiles - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A predictive computational model is generated through a hybrid process. In the hybrid process, a trained predictive computational model is automatically generated based on a dataset, where the predictive computational model is trained to generate an output based on new data. The automatic process uses a pipeline to train the model and makes decisions in the steps of the pipeline. After the model is automatically trained, a representation of the pipeline is presented to a user in a user interface. The user interface allows the user to modify at least some decision made in the automatic machine learning process. One or more modifications are received from the user through the user interface and are used to refine the trained model. The refined model is deployed to generate an output based on new data. ## Claims ### Claim 1 A method comprising: generating, automatically and independent of user input, a trained predictive computational model using a dataset and an automatic modeling application, the trained predictive computational model configured to generate an output based on new data; displaying, in a user interface of a computing device, a representation of a pipeline that depicts a sequence of decisions made by the automatic modeling application as part of generating the trained predictive computational model using the dataset; displaying, in the user interface, at least one prompt for feedback corresponding to at least one decision in the sequence of decisions made by the automatic modeling application as part of generating the trained predictive computational model; receiving, via the user interface, user input that modifies the at least one decision in response to the at least one prompt for feedback; and generating, using the automatic modeling application, a refined trained predictive computational model using a modified sequence of decisions that is defined by the user input provided in response to the at least one prompt for feedback. ### Claim 2 (depends on 1) The method of claim 1, wherein the sequence of decisions includes a data preparation decision and the modified sequence of decisions comprises a data type setting step in the data preparation decision that is different from the sequence of decisions used to generate the trained predictive computational model. ### Claim 3 (depends on 1) The method of claim 1, wherein the sequence of decisions includes a data preparation decision and the modified sequence of decisions comprises a data encoding step in the data preparation decision that is different from the sequence of decisions used to generate the trained predictive computational model. ### Claim 4 (depends on 1) The method of claim 1, wherein the sequence of decisions includes a data preparation decision and the modified sequence of decisions comprises a data imputation step in the data preparation decision that is different from the sequence of decisions used to generate the trained predictive computational model. ### Claim 5 (depends on 1) The method of claim 1, wherein the sequence of decisions includes a feature engineering decision and the modified sequence of decisions comprises a feature selection step in the feature engineering decision that is different from the sequence of decisions used to generate the trained predictive computational model. ### Claim 6 (depends on 1) The method of claim 1, wherein the sequence of decisions includes a feature engineering decision and the modified sequence of decisions comprises a feature ranking step in the feature engineering decision that is different from the sequence of decisions used to generate the trained predictive computational model. ### Claim 7 (depends on 1) The method of claim 1, wherein the sequence of decisions includes a model training decision and the modified sequence of decisions comprises a hyperparameter tuning step in the model training decision that is different from the sequence of decisions used to generate the trained predictive computational model. ### Claim 8 (depends on 1) The method of claim 1, wherein the sequence of decisions includes a model training decision and the modified sequence of decisions comprises an algorithm selection step in the model training decision that is different from the sequence of decisions used to generate the trained predictive computational model. ### Claim 9 (depends on 1) The method of claim 1, wherein the sequence of decisions includes at least one decision that is selected by the trained predictive computational model from a plurality of options and the representation of the pipeline includes a ranking score for each of the plurality of options, wherein the ranking score represents a performance measure of the trained predictive computational model resulting from selecting an option for the at least one decision. ### Claim 10 (depends on 1) The method of claim 1, wherein the representation of the pipeline includes a feature list comprising a plurality of features and a description, for each of the plurality of features, indicating an importance of the feature relative to the output generated by the trained predictive computational model, wherein the plurality of features include features used by the automatic modeling application to generate the trained predictive computational model. ### Claim 11 A system comprising: one or more processors; and a computer-readable storage medium storing instructions that are executable by the one or more processors to: generate, automatically and independent of user input, a trained predictive computational model using a dataset and an automatic modeling application, the trained predictive computational model configured to generate an output based on new data; display, in a user interface of a computing device, a representation of a pipeline that depicts a sequence of decisions made by the automatic modeling application as part of generating the trained predictive computational model using the dataset; display, in the user interface, at least one prompt for feedback corresponding to at least one decision in the sequence of decisions made by the automatic modeling application as part of generating the trained predictive computational model; receive, via the user interface, user input that modifies the at least one decision in response to the at least one prompt for feedback; and generate, using the automatic modeling application, a refined trained predictive computational model using a modified sequence of decisions that is defined by the user input provided in response to the at least one prompt for feedback. ### Claim 12 (depends on 11) The system of claim 11, wherein the sequence of decisions includes a data preparation decision and the modified sequence of decisions comprises at least one of a data type setting step, a data encoding step, or a data imputation step in the data preparation decision that is different from the sequence of decisions used to generate the trained predictive computational model. ### Claim 13 (depends on 11) The system of claim 11, wherein the sequence of decisions includes a feature engineering decision and the modified sequence of decisions comprises at least one of a feature selection step or a feature ranking step in the feature engineering decision that is different from the sequence of decisions used to generate the trained predictive computational model. ### Claim 14 (depends on 11) The system of claim 11, wherein the sequence of decisions includes a model training decision and the modified sequence of decisions comprises at least one of a hyperparameter tuning step or an algorithm selection step in the model training decision that is different from the sequence of decisions used to generate the trained predictive computational model. ### Claim 15 (depends on 11) The system of claim 11, wherein the sequence of decisions includes at least one decision that is selected by the trained predictive computational model from a plurality of options and the representation of the pipeline includes a ranking score for each of the plurality of options, wherein the ranking score represents a performance measure of the trained predictive computational model resulting from selecting an option for the at least one decision. ### Claim 16 (depends on 11) The system of claim 11, wherein the representation of the pipeline includes a feature list comprising a plurality of features and a description, for each of the plurality of features, indicating an importance of the feature relative to the output generated by the trained predictive computational model, wherein the plurality of features include features used by the automatic modeling application to generate the trained predictive computational model. ### Claim 17 A non-transitory computer-readable storage medium storing instructions that are executable by at least one processing device to perform operations comprising: generating, automatically and independent of user input, a trained predictive computational model using a dataset and an automatic modeling application, the trained predictive computational model configured to generate an output based on new data; displaying, in a user interface of a computing device, a representation of a pipeline that depicts a sequence of decisions made by the automatic modeling application as part of generating the trained predictive computational model using the dataset; displaying, in the user interface, at least one prompt for feedback corresponding to at least one decision in the sequence of decisions made by the automatic modeling application as part of generating the trained predictive computational model; receiving, via the user interface, user input that modifies the at least one decision in response to the at least one prompt for feedback; and causing the automatic modeling application to generate a refined trained predictive computational model using a modified sequence of decisions that is defined by the user input provided in response to the at least one prompt for feedback. ### Claim 18 (depends on 17) The non-transitory computer-readable storage medium of claim 17, wherein the sequence of decisions includes a data preparation decision and the modified sequence of decisions comprises at least one of a data type setting step, a data encoding step, or a data imputation step in the data preparation decision that is different from the sequence of decisions used to generate the trained predictive computational model. ### Claim 19 (depends on 17) The non-transitory computer-readable storage medium of claim 17, wherein the sequence of decisions includes a feature engineering decision and the modified sequence of decisions comprises at least one of a feature selection step or a feature ranking step in the feature engineering decision that is different from the sequence of decisions used to generate the trained predictive computational model. ### Claim 20 (depends on 17) The non-transitory computer-readable storage medium of claim 17, wherein the sequence of decisions includes a model training decision and the modified sequence of decisions comprises at least one of a hyperparameter tuning step or an algorithm selection step in the model training decision that is different from the sequence of decisions used to generate the trained predictive computational model.