Patent Issued for Systems, devices, and methods for parallelized data structure processing (USPTO 11436281): Massachusetts Mutual Life Insurance Company

2022 SEP 22 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Merritt, Sears (Groton, MA, US), filed on March 16, 2020, was published online on September 6, 2022, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11436281 is assigned to Massachusetts Mutual Life Insurance Company (Springfield, Massachusetts, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “In this disclosure, where a document, an act, and/or an item of knowledge is referred to and/or discussed, then such reference and/or discussion is not an admission that the document, the act, and/or the item of knowledge and/or any combination thereof was at a priority date, publicly available, known to a public, part of common general knowledge, and/or otherwise constitutes any prior art under any applicable statutory provisions; and/or is known to be relevant to any attempt to solve any problem with which this disclosure may be concerned with. Further, nothing is disclaimed.

“A server may serve a network page to a client. The network page may include a set of fields programmed to receive a plurality of inputs from the client, such as a plurality of alphanumeric strings or a plurality of binary values. The network page may be further programmed to submit the inputs to the server, such as when the fields are populated or when triggered via the client. For example, a webserver may serve a webpage to a smartphone, where the webpage is programmed to receive a set of user inputs from the smartphone, such as personal information, address, health information, and others, and upload the user inputs to the webserver.

“Upon receiving the inputs from the client, the server may create a user profile based on the inputs and provide an output to the client based on the user profile. However, since some of the inputs may contain incorrect or imprecise information, some of the inputs may need to be verified or validated, such as independently. Therefore, until such verification or validation, the user profile may be classified as not reliable. Such classification may also taint the output in a similar light.

“When some of the inputs are determined to contain incorrect or imprecise information and when some of such inputs are amended in the user profile with correct or precise information, then this amendment updates the profile. Consequently, the output may be also be updated to account for the amendment. However, if the output has already been used in various data operations, then the update to the output may entail a repetition of such data operations with the output, as updated. This repetition wastes time and resources, such as computational cycles, memory space, and network bandwidth, especially cumulatively. If the output has not yet been used in various data operations, then a delay in verification or validation of some of the inputs is impractical. Accordingly, there is a desire for a computing technology to address at least one of such challenges.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “This disclosure at least partially addresses at least one of the above challenges. However, this disclosure can prove useful to other technical areas. Therefore, at least some claims should not be construed as necessarily limited to addressing any of the above challenges.

“In one embodiment, a computer-implemented method comprising: performing, by a server, a hyperparameter selection based on a parallelized grid search algorithm configured to determine a number of decision trees, a depth of decision trees, and an amount of variables used during tree node splitting; randomly selecting, by the server, a first subset from a data set, wherein the data set contains a set of records with a plurality of fields, wherein each of the fields includes a value; constructing, by the server, a learned mortality model for each of the number of decision trees, the depth of decision trees, and the number of variables based on the values of the first subset; identifying, by the server, a second subset from the data set, wherein the second subset is exclusive of the first subset; evaluating, by the server, the learned mortality model based on the second subset via at least one of a concordance or a time-varying area under curve statistic such that a first mortality score is determined; presenting, by the server, a user interface for display on a client computer based on the first mortality score satisfying a predetermined threshold, wherein the user interface is configured to receive a user input from the client computer; receiving, by the server, the input from the client computer, wherein the input includes a user profile with an attribute; submitting, by the server, the input into the learned mortality model such that the model outputs a second mortality score based on the user profile with the attribute; and updating, by the server, the user interface for display on the client computer with the second mortality score.

“In one embodiment, a memory storing a set of instructions for execution by a processor, wherein the set of instructions instructs the processor to: perform a hyperparameter selection based on a parallelized grid search algorithm configured to determine a number of decision trees, a depth of decision trees, and an amount of variables used during tree node splitting; randomly select a first subset from a data set, wherein the data set contains a set of records with a plurality of fields, wherein each of the fields includes a value; construct a learned mortality model for each of the number of decision trees, the depth of decision trees, and the number of variables based on the values of the first subset; identify a second subset from the data set, wherein the second subset is exclusive of the first subset; evaluate the learned mortality model based on the second subset via at least one of a concordance or a time-varying area under curve statistic such that a first mortality score is determined; present a user interface for display on a client computer based on the first mortality score satisfying a predetermined threshold, wherein the user interface is configured to receive a user input from the client computer; receive the input from the client computer, wherein the input includes a user profile with an attribute; submit the input into the learned mortality model such that the model outputs a second mortality score based on the user profile with the attribute; and update the user interface for display on the client computer with the second mortality score.

“Additional features and advantages of various embodiments are set forth in a detailed description which follows. Various objectives and other advantages of this disclosure are realized and attained by various structures particularly pointed out in various illustrative embodiments in the detailed description and claims hereof as well as in a set of appended drawings. Note that the detailed description is illustrative and explanatory and is intended to provide further explanation of this disclosure as claimed.”

The claims supplied by the inventors are:

“1. A computer-implemented method comprising: receiving, by a server from an electronic device, a set of attributes associated with a user and a request to predict a mortality score associated with the user; executing, by the server, a learned mortality model to determine the mortality score, the learned mortality model having a set of decision trees, each decision tree having a respective depth and a number of variables, the learned mortality model configured to: execute a hyper-parameter attribute selection protocol configured to determine a number of decision trees, a depth of decision trees, and an amount of variables used during tree node splitting; select a first subset of attributes from the received set of attributes and a second subset of attributes from the set of attributes, the first subset of attribute corresponding to hyper-parameter attributes selected by the learned mortality model, wherein the second subset is exclusive of the first subset; execute the learned mortality model using the second set of attributes to determine the mortality score for the user, wherein the set of decision trees of the learned model are configured based upon the first set of attributes; and transmitting, by the server, the mortality score to the electronic device.

“2. The method of claim 1, wherein the first subset of attributes are randomly selected.

“3. The method of claim 1, wherein at least one of the first subset of attributes and the second subset of attributes are received from the electronic device.

“4. The method of claim 1, wherein the server displays the mortality score onto a graphical user interface of the electronic device.

“5. The method of claim 1, wherein the hyper-parameter attribute selection protocol uses a parallelized grid search algorithm.

“6. The method of claim 1, wherein the learned mortality model determines the mortality score using at least one of a concordance and a time-varying area under curve statistic algorithm.

“7. The method of claim 1, wherein the server identifies the first subset of attributes based on or more predetermined thresholds.

“8. The method of claim 1, wherein a number of attributes within the first or the second subset of attributes is predetermined.

“9. The method of claim 1, wherein the server executes one or more of the steps in parallel.

“10. The method of claim 1 wherein the set of decision trees is arranged based on historical mortality data corresponding to historical user attributes and their respective mortality data.

“11. A system comprising: a database configured to store a learned mortality model; an electronic device configured to receive request and transmit the request to a server; and the server in communication with the database and the electronic device, the server configured to: receive, from the electronic device, a set of attributes associated with a user and a request to predict a mortality score associated with the user; execute a learned mortality model to determine a mortality score for the user, the learned mortality model having a set of decision trees, each decision tree having a respective depth and a number of variables, the learned mortality model configured to: execute a hyper-parameter attribute selection protocol configured to determine a number of decision trees, a depth of decision trees, and an amount of variables used during tree node splitting; select a first subset of attributes from the received set of attributes and a second subset of attributes from the set of attributes, wherein the second subset is exclusive of the first subset, the first subset of attribute corresponding to hyper-parameter attributes selected by the learned mortality model; execute the model using the second set of attributes to determine the mortality score for the user, wherein the set of decision trees of the learned model are configured based upon the first set of attributes; and transmit the mortality score to the electronic device.

“12. The system of claim 11, wherein the first attributes are randomly selected.

“13. The system of claim 11, wherein at least one of the first subset of attributes and the second subset of attributes are received from the electronic device.

“14. The system of claim 11, wherein the server displays the mortality score onto a graphical user interface of the electronic device.

“15. The system of claim 11, wherein the hyper-parameter attribute selection protocol uses a parallelized grid search algorithm.

“16. The system of claim 11, wherein the learned mortality model determines the mortality score using at least one of a concordance and a time-varying area under curve statistic algorithm.

“17. The system of claim 11, wherein the server identifies the first subset of attributes based on or more predetermined thresholds.

“18. The system of claim 11, wherein a number of attributes within the first or the second subset of attributes is predetermined.

“19. The system of claim 11, wherein the server executes one or more of the steps in parallel.

“20. The system of claim 11, wherein the set of decision trees is arranged based on historical mortality data corresponding to historical user attributes and their respective mortality data.”

URL and more information on this patent, see: Merritt, Sears. Systems, devices, and methods for parallelized data structure processing. U.S. Patent Number 11436281, filed March 16, 2020, and published online on September 6, 2022. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11436281.PN.&OS=PN/11436281RS=PN/11436281

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