Researchers Submit Patent Application, “Multi-Task Deep Learning Of Employer-Provided Benefit Plans”, for Approval (USPTO 20230376908): ADP Inc.

2023 DEC 08 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Washington, D.C., NewsRx journalists report that a patent application by the inventors Cardoso, Eduardo (Porto Alegre, BR); Ferreira, Alex (Porto Alegre, BR); Fontana, Mariele (Porto Alegre, BR); Lima, Edison (Porto Alegre, BR); Neukirchen, Fabio (Porto Alegre, BR); Santos, Leonardo (Porto Alegre, BR), filed on May 22, 2023, was made available online on November 23, 2023.

The patent’s assignee is ADP Inc. (Roseland, New Jersey, United States).

News editors obtained the following quote from the background information supplied by the inventors: “

“The present disclosure relates generally to an improved computer system and, in particular, to deep machine learning regarding changes in employer-provided benefit plans and predicting the types of changes employers will make to plan benefits as well as when they will make them.

“An employer provides employee benefits to its employees according to a benefits plan that includes different types of plans for the various employees. For example, an employer may provide health insurance to its employees based on an insurance plan that is offered at different rates to the various employees. As one specific example, a particular insurance plan may be offered at a first rate for an individual employee, a second rate for an employee and the employee’s spouse, a third rate for an employee and the employee’s children, and a fourth rate for an employee and the employee’s entire family including spouse and children.

“Insurance plans can be complex and many insurance providers provide a variety of insurance plans from which an employer can choose. For example, without limitation, insurance plans may vary based on whether the coverage is limited to a Health Maintenance Organization (HMO) or a Preferred Provider Organization (PPO). As another example, insurance plans may vary based on deductibles, the percentage of carrier coinsurance, the percentage of member coinsurance, and other features.

“Further, different employers choose to pay for different percentages of the insurance premiums required by insurance providers. These percentages may be different across different markets or different industries. Knowing these percentages can help an employer in determining what percentage of the overall insurance premium to pay in order to be competitive in the marketplace with respect to employee benefits.

“Thus, there may be many considerations for the employer to take into account when selecting a benefits plan for its employees. However, accessing the information needed to make a well-informed selection may be more tedious, difficult, and time-consuming than desired. In some cases, this information may not be readily available or easily acquirable.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “An illustrative embodiment provides a computer-implemented method for generating an employee benefit plan by using machine learning. The process collects employment data about employees of a plurality of business entities. The employment data comprises a number of dimensions of data collected from a number of sources. The process identifies a number of plan benefits for benefit plan for each of the business entities. The process determines metrics for the plan benefits during a given time interval. The process simultaneously models the plan benefits and the metrics for plan benefits to identify correlations among the dimensions of data and generalize rules for competitive benefit prediction. According to the modeling, the process predicts a number of competitive benefits for an employee benefit plan of a particular business entity based on the employment data of the particular business entity. The process generates the employee benefit plan for the particular business entity based on the number of competitive benefits.

“Another illustrative embodiment provides a system for generating an employee benefit plan. The system comprises a bus system, a storage device connected to the bus system, wherein the storage device stores program instructions, and a number of processors connected to the bus system, wherein the number of processors execute the program instructions to: collect employment data about employees of a plurality of business entities, wherein the employment data comprises a number of dimensions of data collected from a number of sources; identify a number of plan benefits for benefit plan for each of the business entities; determine metrics for the plan benefits during a given time interval; simultaneously model the plan benefits and the metrics for plan benefits to identify correlations among the dimensions of data and generalize rules for competitive benefit prediction; according to the modeling, predict a number of competitive benefits for an employee benefit plan of a particular business entity based on the employment data of the particular business entity; and generate the employee benefit plan for the particular business entity based on the number of competitive benefits.

“Another illustrative embodiment provides a computer program product for generating an employee benefit plan. The computer program product comprises a non-volatile computer readable storage medium having program instructions embodied therewith, the program instructions executable by a number of processors to cause the computer to perform the steps of: collecting employment data about employees of a plurality of business entities, wherein the employment data comprises a number of dimensions of data collected from a number of sources; identifying a number of plan benefits for benefit plan for each of the business entities; determining metrics for the plan benefits during a given time interval; simultaneously modeling the plan benefits and the metrics for plan benefits to identify correlations among the dimensions of data and generalize rules for competitive benefit prediction; according to the modeling, predicting a number of competitive benefits for an employee benefit plan of a particular business entity based on the employment data of the particular business entity; and generating the employee benefit plan for the particular business entity based on the number of competitive benefits.

“The features and functions can be achieved independently in various embodiments of the present disclosure or may be combined in yet other embodiments in which further details can be seen with reference to the following description and drawings.”

The claims supplied by the inventors are:

“1.-21. (canceled)

“22. A method, comprising: aggregating, by a data processing system coupled with memory, a first data set from a first source and a second data set from a second source; identifying, by the data processing system a first plan associated with the first source and a second plan associated with the second source; identifying, by the data processing system, first characteristics for the first plan at a first time, second characteristics for the second plan at a second time, third characteristics for the first plan at a time different from the first time and fourth characteristics for the second plan at a time different than the second time; determining, by the data processing system using the first data set, the first characteristics, and the third characteristics, a first metric associated with the first plan; determining, by the data processing system using the second data set, the second characteristics, and the fourth characteristics, a second metric associated with the second plan; identifying, by the data processing system, similarities between the first data set, the second data set, and a third data set from a third source; determining, by the data processing system, correlations between the first plan and the second plan responsive to identifying the similarities; generating target characteristics using a recurrent neural network having as inputs the determined correlations between the first plan and the second plan, the first metric, and the second metric; generating a third plan using a fully connected neural network having as inputs the correlations, the target characteristics, and the third data set; and transmitting, by the data processing system for display, the third plan.

“23. The method of claim 22, comprising determining, by the data processing system, the first metric associated with the first plan and the second metric associated with the second plan by identifying differences between the first characteristics, the second characteristics, the third characteristics, and the fourth characteristics.

“24. The method of claim 22, wherein the recurrent neural network comprises three layers.

“25. The method of claim 22, comprising predicting, by the data processing system, the target characteristics using a recurrent neural network for each of the target characteristics.

“26. The method of claim 22, comprising determining, by the data processing system using the recurrent neural network, probability distributions associated with the target characteristics using the first metric, the second metric, the first data set, and the second data set.

“27. The method of claim 22, comprising generating, by the data processing system using the fully connected neural network, the third plan according to probability distributions associated with the target characteristics.

“28. The method of claim 22, wherein the first data set, the second data set, and the third data set comprise at least one of: payroll services beginning date, a payroll services ending date, an industry, a geographic region, a number of employees, a collection of job codes, a range of salary amount, a range of part-time to full-time employees, hiring data, characteristics administration data, payroll data, performance review data, or team data.

“29. The method of claim 22, wherein the first characteristics are different than the third characteristics and the second characteristics are different than the fourth characteristics.

“30. The method of claim 22, wherein the third plan comprises a subset of the target characteristics.

“31. A system, comprising a data processing system comprising a processor coupled with memory, the data processing system to: aggregate a first data set from a first source and a second data set from a second source; identify a first plan associated with the first source and a second plan associated with the second source; identify first characteristics for the first plan at a first time, second characteristics for the second plan at a second time, third characteristics for the first plan at a time different from the first time and fourth characteristics for the second plan at a time different than the second time; determine using the first data set, the first characteristics, and the third characteristics, a first metric associated with the first plan; determine using the second data set, the second characteristics, and the fourth characteristics, a second metric associated with the second plan; identify similarities between the first data set, the second data set, and a third data set from a third source; determine correlations between the first plan and the second plan responsive to identifying the similarities; generate target characteristics using a recurrent neural network having as inputs the determined correlations between the first plan and the second plan, the first metric, and the second metric; generate a third plan using a fully connected neural network having as inputs the correlations, the target characteristics, and the third data set; and transmit for display the third plan. predict using a recurrent neural network, target characteristics based on the correlations, the first metric, and the second metric; generate using a fully connected neural network, a third plan, using the correlations, the target characteristics, and the third data set; and transmit the third plan to the third source for presentation on a display associated with the third source.

“32. The system of claim 31, comprising the data processing system to determine the first metric associated with the first plan and the second metric associated with the second plan by identifying differences between the first characteristics, the second characteristics, the third characteristics, and the fourth characteristics.

“33. The system of claim 31, wherein the recurrent neural network comprises three layers.

“34. The system of claim 31, comprising the data processing system to predict the target characteristics using a recurrent neural network for each of the target characteristics.

“35. The system of claim 31, comprising the data processing system to determine, using the recurrent neural network, probability distributions associated with the target characteristics using the first metric, the second metric, the first data set, and the second data set.

“36. The system of claim 31, comprising the data processing system to generate, using the fully connected neural network, the third plan according to probability distributions associated with the target characteristics.

“37. The system of claim 31, wherein the first data set, the second data set, and the third data set comprise at least one of: payroll services beginning date, a payroll services ending date, an industry, a geographic region, a number of employees, a collection of job codes, a range of salary amount, a range of part-time to full-time employees, hiring data, characteristics administration data, payroll data, performance review data, or team data.

“38. The system of claim 31, wherein the first characteristics are different than the third characteristics and the second characteristics are different than the fourth characteristics.

“39. A non-transitory computer-readable medium, comprising instructions embodied thereon, the instructions to cause a processor to: aggregate a first data set from a first source and a second data set from a second source; identify a first plan associated with the first source and a second plan associated with the second source; identify first characteristics for the first plan at a first time, second characteristics for the second plan at a second time, third characteristics for the first plan at a time different from the first time and fourth characteristics for the second plan at a time different than the second time; determine using the first data set, the first characteristics, and the third characteristics, a first metric associated with the first plan; determine using the second data set, the second characteristics, and the fourth characteristics, a second metric associated with the second plan; identify similarities between the first data set, the second data set, and a third data set from a third source; determine correlations between the first plan and the second plan responsive to identifying the similarities; generate target characteristics using a recurrent neural network having as inputs the determined correlations between the first plan and the second plan, the first metric, and the second metric; generate a third plan using a fully connected neural network having as inputs the correlations, the target characteristics, and the third data set; and transmit for display the third plan.

“40. The non-transitory computer-readable medium of claim 39, comprising the instructions to cause the processor to determine the first metric associated with the first plan and the second metric associated with the second plan by identifying differences between the first characteristics, the second characteristics, the third characteristics, and the fourth characteristics.

“41. The non-transitory computer-readable medium of claim 39, comprising the instructions to cause the processor to generate, using the fully connected neural network, the third plan according to probability distributions associated with the target characteristics.”

For additional information on this patent application, see: Cardoso, Eduardo; Ferreira, Alex; Fontana, Mariele; Lima, Edison; Neukirchen, Fabio; Santos, Leonardo. Multi-Task Deep Learning Of Employer-Provided Benefit Plans. U.S. Patent Application Number 20230376908, filed May 22, 2023 and posted November 23, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230376908)&db=US-PGPUB&type=ids

(Our reports deliver fact-based news of research and discoveries from around the world.)