Patent Issued for Scheduling Machine Learning Tasks, And Applications (USPTO 10,990,900)
2021 MAY 10 (NewsRx) -- By a
The patent’s inventor is Lindner,
This patent was filed on
From the background information supplied by the inventors, news correspondents obtained the following quote: “Field
“This field is generally related to processing personal data.
“Background
“As technology advances, an ever increasing amount of personal data is becoming digitized, and as a result, more and more personal data is becoming lawfully accessible. The increased accessibility of personal data has spawned new industries focused on lawfully mining personal data.
“A personal data record may include a number of properties. A data record representing an individual may include properties such as the name of the individual, his or her city, state, and ZIP code. In addition to demographic information, data records can include information about a person’s behavior. Data records from different sources may comprise different properties. Systems exist for collecting information describing characteristics or behavior of separate individuals. Collecting such personal information has many applications, including in national security, law enforcement, marketing, healthcare and insurance.
“In healthcare for example, a healthcare provider may have inconsistent personal information, such as address information, from a variety of data sources, including the national provider identifier registration,
“As records receive more updates from different sources, they also have a greater risk of inconsistency and errors associated with data entry. In these ways, data records all describing the same individual can be incongruous, inconsistent, and erroneous in their content. From these various sources, a single healthcare provider can have many addresses, perhaps as many as 200 addresses. The sources may disagree about what the right address is. Some healthcare providers have multiple correct addresses. For this reason, the fact that a provider may have a more recent address does not mean that older addresses are incorrect.
“Some health and dental insurance companies have staff tasked with manually calling healthcare providers in an effort to determine their correct address. However, this manual updating is expensive because a healthcare provider’s address information may change frequently. In addition to address information, similar issues are present with other demographic information relating to a healthcare provider, such as its phone number.
“In addition, fraudulent claims are enormous problems in healthcare. By some estimates, fraudulent claims may steal in excess of
“Data-directed algorithms, known as machine learning algorithms, are available to make predictions and conduct certain data analysis. Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Within the field of data analytics, machine learning is a method used to devise complex models and algorithms can be used for prediction and estimation.
“To develop such models, they first must be trained. Generally, the training involves inputting a set of parameters, called features, and known correct or incorrect values for the input features. After the model is trained, it may be applied to new features for which the appropriate solution is unknown. By applying the model in this way, the model predicts, or estimates, the solution for other cases that are unknown. These models may uncover hidden insights through learning from historical relationships and trends in the database. The quality of these machine learning models may depend on the quality and quantity of the underlying training data.
“Systems and methods are needed to improve identification and forecasting of the correct personal information, such as a healthcare provider’s demographic information and propensity for fraud, or a data source.”
Supplementing the background information on this patent, NewsRx reporters also obtained the inventor’s summary information for this patent: “In an embodiment, a system schedules data ingestion and machine learning. The system includes a computing device, a database, a queue stored on the computing device, and a scheduler implemented on the computing device. The scheduler is configured to place a request to complete a job on the queue. The request includes instructions to complete at least one of a data ingestion task, a training task and a solving task. The system also includes three processes, each implemented on the computing device and monitoring the queue: a data ingestion process, a trainer process, and a solver process. When the queue includes a request to complete the data ingestion task, the data ingestion task retrieves data relating to a person from a data source and to store the retrieved data in the database. When the queue includes a request to complete the training task, the trainer task trains a model using the retrieved data in the database and an indication that a value for the particular property in the person’s data was verified as accurate or inaccurate. The model is trained such that it can predict whether another person’s value for the particular property is accurate. Finally, when the queue includes a request to complete the solving task, the solver process applies the model to predict whether the other person’s value in the plurality of properties is accurate.
“Method and computer program product embodiments are also disclosed.
“Further embodiments, features, and advantages of the invention, as well as the structure and operation of the various embodiments, are described in detail below with reference to accompanying drawings.”
The claims supplied by the inventors are:
“What is claimed is:
“1. A system for scheduling data ingestion and machine learning, comprising: a computing device including a processor; a database; a queue stored on the computing device; a scheduler implemented on the computing device and configured to place a request to complete a job on the queue, the request including instructions to complete at least one of a data ingestion task, a training task and a solving task; a data ingestion process implemented on the computing device and configured to: (i) monitor the queue and, (ii) when the queue includes a request to complete the data ingestion task, retrieve data relating to a person from a data source and to store the retrieved data in the database; a trainer process implemented on the computing device and configured to: (i) monitor the queue and, (ii) when the queue includes a request to complete the training task, train a model using the retrieved data in the database and an indication that a value for a particular property in the retrieved data was verified as accurate or inaccurate such that the model can predict whether another person’s value for the particular property is accurate; a solver process implemented on the computing device and configured to: (i) monitor the queue and, (ii) when the queue includes a request to complete the solving task, apply the model to predict whether the other person’s value is accurate; and an API monitor implemented on the computing device and configured to, on receipt of an API request, place a request to complete another job specified on the API request on the queue, the API request including instructions to complete at least one of: the data ingestion task, the training task, the solving task, or a scheduling task.
“2. The system of claim 1, further comprising a plurality of queues, each queue dedicated to one of the data ingestion task, the training task and the solving task, wherein the data ingestion process monitors a queue dedicated to the data ingestion task from the plurality of queues, wherein the trainer process monitors a queue dedicated to the training task from the plurality of queues, and wherein the solver process monitors a queue dedicated to the solver task from the plurality of queues.
“3. The system of claim 1, wherein the scheduler places the request to complete the job on the queue at periodic intervals.
“4. The system of claim 1, wherein the data ingestion process is configured to: (i) monitor the data source to determine whether data relating to the person has updated; and (ii) when data for the person has been updated, storing the updated data in the database.
“5. The system of claim 1, wherein the scheduler monitors the queue and, when the queue includes a request to complete the scheduling task, schedules a task as specified in the API request.
“6. The system of claim 1, wherein the API request includes: (i) an indication that a value for the particular property in the retrieved data was verified as accurate or inaccurate at a particular time, and (ii) an instruction to complete the training task.
“7. The system of claim 1, wherein the data ingestion process is configured to monitor the data source to determine whether data relating to the person has updated and, when data for the person has been updated, place another request to complete the training task on the queue.
“8. A computer-implemented method for scheduling data ingestion and machine learning, comprising: (a) placing a request to complete a job on a queue, the request including instructions to complete at least one of a data ingestion task, a training task and a solving task; (b) monitoring the queue to determine whether the queue includes the request and what task is next on the queue; © when the queue includes the request to complete the data ingestion task, retrieving data relating to a person from a data source to store the retrieved data in a database; (d) when the queue includes the request to complete the training task, training a model using the retrieved data in the database and an indication that a value for a particular property in the retrieved data was verified as accurate or inaccurate such that the model can predict whether another person’s value for the particular property is accurate; (e) when the queue includes the request to complete the solving task, applying the model to predict whether the other person’s value is accurate; (f) receiving an API request; and (g) on receipt of the API request, placing another request to complete another job specified on the API request on the queue, the API request including instructions to complete at least one of: the data ingestion task, the training task, the solving task, or a scheduling task.
“9. The method of claim 8, wherein the monitoring (b) comprises monitoring a plurality of queues, each dedicated to one of the data ingestion task, the training task and the solving task.
“10. The method of claim 8, wherein the placing (a) occurs at periodic intervals.
“11. The method of claim 8, further comprising: (f) monitoring the data source to determine whether data relating to the person has updated; and (g) when data for the person has been updated, storing the updated data in the database.
“12. The method of claim 8, further comprising: (h) when the queue includes the other request to complete the scheduling task, scheduling a task as specified in the API request.
“13. The method of claim 8, wherein the API request includes (i) an indication that a value for the particular property in the retrieved data was verified as accurate or inaccurate at a particular time, and (ii) an instruction to complete the training task.
“14. The method of claim 8, further comprising: (f) monitoring the data source to determine whether data relating to the person has updated; and (g) when data for the person has been updated, placing another request to complete the training task on the queue.”
For the URL and additional information on this patent, see: Lindner,
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