Researchers Submit Patent Application, “Simplistic Machine Learning Model Generation Tool For Predictive Data Analytics”, for Approval (USPTO 20240095599): Patent Application

2024 APR 05 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Business Daily -- From Washington, D.C., NewsRx journalists report that a patent application by the inventors Chandrappa, Mahesh (Bloomington, IL, US); Dickneite, Mark A. (Bloomington, IL, US); Fiala, Charles T. (Normal, IL, US); Gajendran, Suresh B. (Richardson, TX, US); Zaheer, Rashid (Bloomington, IL, US), filed on November 21, 2023, was made available online on March 21, 2024.

No assignee for this patent application has been made.

News editors obtained the following quote from the background information supplied by the inventors: “Service providers in various consumer industries maintain a massive amount of data related to the consumers. This data is typically dispersed across multiple “dimensions” that reflect various characteristics of the consumers. Such dimensions include, for example, the age of the consumer, the gender of the consumer, the race of the consumer, the occupation of the consumer, the annual income of the consumer, the marital status of the consumer, the type of services that are consumed over the time, etc. Particularly, for service providers in the auto insurance industry, such dimensions of consumer data may also include the type of vehicle-specific services that are consumed over the time, the type of claims that are filed over the time, the traffic violations associated with the consumer over the time, etc.

“Numerous efforts have been undertaken to discover correlations among various dimensions of consumer data. However, for a given product or service, identifying the key features that influence sales based on such correlations can be complex and time consuming, and may require specialized training related to dataset analysis. Traditionally, data scientists with in-depth knowledge in statistics coupled with insurance domain knowledge have been relied on to develop and provide such analysis. More recently, machine learning (ML) algorithms have been relied on to identify correlations between items in large datasets. In such efforts, a dataset may be divided into multiple parts. One or more parts of the dataset can then be used to train a ML model and the rest of the dataset can be used to test the trained ML model (also referred to herein as the “trained ML model”). Once the trained ML model has been tested to verify that it satisfies a desired level of prediction accuracy, the trained ML model can be implemented across multiple enterprise platforms (e.g., across auto insurance and claim operations platforms).

“However, with the limited availability of data scientists and the long cycle time required to develop ML models, deploying such ML models can, at least initially, cause significant reductions in the efficiency of business operations. Example embodiments of the present disclosure are directed toward addressing these difficulties.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “According to a first aspect, a method implemented by a computing device for predictive data analytics comprises generating a guided user interface (GUI) that guides one or more user operations on the user interface causing the computing device to construct a machine learning model, the one or more user operations on the user interface including: obtaining, from a database, a dataset including a plurality of data objects; determining one or more characteristics associated with a first data object of the plurality of data objects; identifying a subset of the dataset based at least in part on the one or more characteristics; selecting at least one machine learning algorithm; and training a machine learning (ML) model with respect to the first data object using the subset of the dataset and the at least one machine learning algorithm to generate a trained ML model with respect to the first data object; implementing the trained ML model with respect to the first data object in a cloud server to enable distributing the trained ML model to a plurality of client device via a network.

“According to a second aspect, a system for predictive data analytics comprises at least one processor, and memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform various actions. Such actions include generating a guided user interface (GUI) that guides one or more user operations on the user interface causing the computing device to construct a machine learning model, the one or more user operations on the user interface including: receiving a dataset including a plurality of data objects; determining one or more characteristics associated with a first data object of the plurality of data objects; identifying a subset of the dataset based at least in part on the one or more characteristics; selecting at least one machine learning algorithm; and training a machine learning (ML) model with respect to the first data object using the subset of the dataset and the at least one machine learning algorithm to generate a trained ML model with respect to the first data object; implementing the trained ML model with respect to the first data object in a cloud server to enable distributing the trained ML model to a plurality of client device via a network.

“A third aspect of the present disclosure includes a computer-readable storage medium storing computer-readable instructions executable by one or more processors. When executed by the one or more processors, the instructions cause the one or more processors to perform actions comprising: generating a guided user interface (GUI) that guides one or more user operations on the user interface including: obtaining, from a database, a dataset including a plurality of data objects; determining one or more characteristics associated with a first data object of the plurality of data objects; identifying a subset of the dataset based at least in part on the one or more characteristics; selecting at least one machine learning algorithm; and training a machine learning (ML) model with respect to the first data object using the subset of the dataset and the at least one machine learning algorithm to generate a trained ML model with respect to the first data object; implementing the trained ML model with respect to the first data object in a cloud server to enable distributing the trained ML model to a plurality of client device via a network.”

The claims supplied by the inventors are:

“1. A method implemented by a computing device, the method comprising: generating a guided user interface (GUI) configured to enable construction of machine learning (ML) model generation tools; receiving inputs, via the GUI, indicative of requested operations to be performed by the computing device, the requested operations including: obtaining, from a database, a dataset associated with a plurality of data objects, generating, on the GUI, a visualization indicating a correlation between at least two objects of the plurality of data objects, determining, based on the visualization, a subset of the dataset correlated with a first data object, selecting a ML algorithm, and generating, based on the subset of the dataset and using the ML algorithm, an ML model generation tool corresponding to the first data object; and implementing the ML model generation tool in a cloud server.

“2. The method of claim 1, wherein the requested operations further comprise: generating, using the ML model generation tool, an ML model corresponding to the first data object; and implementing the ML model, using the first data object, in the cloud server.

“3. The method of claim 1, wherein the requested operations further comprise: generating, on the GUI, a first visualization of linear dependencies between the first data object and other data objects of the plurality of data objects; determining, based on the first visualization, one or more second data objects having the linear dependencies higher than a threshold; and performing, based at least in part on the one or more second data objects, a dimension reduction on the dataset to obtain the subset of the dataset.

“4. The method of claim 3, wherein the dimension reduction is performed using at least one of a random forest algorithm, a single variable logistic regression algorithm, or a variable clustering algorithm.

“5. The method of claim 1, wherein the requested operations further comprise: causing the dataset to be transmitted from a database to a local storage; presenting, on the GUI, statistic information associated with the dataset; and performing at least one of a null value treatment or an outlier value treatment on the dataset.

“6. The method of claim 2, further comprising: receiving a request to predict a target value associated with a target object, the request including anew dataset; determining whether an ML model with respect to the target object exists in the cloud server; and in response to the determination that the ML model with respect to the target object exists in the cloud server, downloading the ML model with respect to the target object from the cloud server to a local storage, and computing, using the ML model with respect to the target object, the target value.

“7. The method of claim 6, further comprising: in response to the determination that the ML model with respect to the target object does not exist in the cloud server, downloading the ML model generation tool from the cloud server to the local storage; executing the ML model generation tool to generate, based at least in part on the new dataset, the ML model with respect to the target object; and compute, using the ML model with respect to the target object, the target value.

“8. A system comprising: a processor; and memory storing instructions that, when executed by processor, cause the processor to perform actions comprising: generating a guided user interface (GUI) configured to enable construction of machine learning (ML) model generation tools; receiving inputs, via the GUI, indicative of requested operations to be performed by the computing device, the operations including: obtaining, from a database, a dataset associated with a plurality of data objects, generating, on the GUI, a visualization indicating a correlation between at least two objects of the plurality of data objects, determining, based on the visualization, a subset of the dataset correlated with a first data object, selecting a ML algorithm, and generating, based on the subset of the dataset and using the ML algorithm, an ML model generation tool corresponding to the first data object; and implementing the ML model generation tool in a cloud server.

“9. The system of claim 8, wherein the requested operations further comprise: generating, using the ML model generation tool, an ML model corresponding to the first data object; and implementing the ML model, using the first data object, in the cloud server.

“10. The system of claim 8, wherein the requested operations further comprise: generating, on the GUI, a first visualization of linear dependencies between the first data object and other data objects of the plurality of data objects; determining, based on the first visualization, one or more second data objects having the linear dependencies higher than a threshold; and performing, based at least in part on the one or more second data objects, a dimension reduction on the dataset to obtain the subset of the dataset.

“11. The system of claim 10, wherein the dimension reduction is performed using at least one of a random forest algorithm, a single variable logistic regression algorithm, or a variable clustering algorithm.

“12. The system of claim 8, wherein the requested operations further comprise: causing the dataset to be transmitted from a database to a local storage; presenting, on the GUI, statistic information associated with the dataset; and performing at least one of a null value treatment or an outlier value treatment on the dataset.

“13. The system of claim 9, wherein the processor is caused to further perform actions including: receiving a request to predict a target value associated with a target object, the request including anew dataset; determining whether an ML model with respect to the target object exists in the cloud server; and in response to the determination that the ML model with respect to the target object exists in the cloud server, downloading the ML model with respect to the target object from the cloud server to a local storage, and computing, using the ML model with respect to the target object, the target value.

“14. The system of claim 13, wherein the processor is caused to further perform actions including: in response to the determination that the ML model with respect to the target object does not exist in the cloud server, downloading the ML model generation tool from the cloud server to the local storage; executing the ML model generation tool to generate, based at least in part on the new dataset, the ML model with respect to the target object; and compute, using the ML model with respect to the target object, the target value.

“15. A computer-readable storage medium storing computer-readable instructions executable by a processor, that when executed by the processor, cause the processor to perform actions comprising: generating a guided user interface (GUI) configured to enable construction of machine learning (ML) model generation tools; receiving inputs, via the GUI, indicative of requested operations to be performed by the computing device, the operations including: obtaining, from a database, a dataset associated with a plurality of data objects, generating, on the GUI, a visualization indicating a correlation between at least two objects of the plurality of data objects, determining, based on the visualization, a subset of the dataset correlated with a first data object, selecting a ML algorithm, and generating, based on the subset of the dataset and using the ML algorithm, an ML model generation tool corresponding to the first data object; and implementing the ML model generation tool in a cloud server.

“17. The computer-readable storage medium of claim 15, wherein the requested operations further comprise: generating, on the GUI, a first visualization of linear dependencies between the first data object and other data objects of the plurality of data objects; determining, based on the first visualization, one or more second data objects having the linear dependencies higher than a threshold; and performing, based at least in part on the one or more second data objects, a dimension reduction on the dataset to obtain the subset of the dataset.

“18. The computer-readable storage medium of claim 17, wherein the requested operations further comprise: causing the dataset to be transmitted from a database to a local storage; presenting, on the GUI, statistic information associated with the dataset; and performing at least one of a null value treatment or an outlier value treatment on the dataset.

“19. The computer-readable storage medium of claim 16, wherein the processor is caused to further perform actions including: receiving a request to predict a target value associated with a target object, the request including anew dataset; determining whether an ML model with respect to the target object exists in the cloud server; in response to the determination that the ML model with respect to the target object exists in the cloud server, downloading the ML model with respect to the target object from the cloud server to a local storage, and computing, using the ML model with respect to the target object, the target value; and in response to the determination that the ML model with respect to the target object does not exist in the cloud server, downloading the ML model generation tool from the cloud server to the local storage; executing the ML model generation tool to generate, based at least in part on the new dataset, the ML model with respect to the target object; and compute, using the ML model with respect to the target object, the target value.”

There are additional claims. Please visit full patent to read further.

For additional information on this patent application, see: Chandrappa, Mahesh; Dickneite, Mark A.; Fiala, Charles T.; Gajendran, Suresh B.; Zaheer, Rashid. Simplistic Machine Learning Model Generation Tool For Predictive Data Analytics. U.S. Patent Application Number 20240095599, filed November 21, 2023 and posted March 21, 2024. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20240095599)&db=US-PGPUB&type=ids

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