Patent Application Titled “Natural Language Processing Parsimonious Question Generator” Published Online (USPTO 20220164549): Patent Application

2022 JUN 13 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors Bardhan, Ashok (El Cerrito, Ca, Us); Gupta, Avaneendra (Cupertino, Ca, Us), filed on September 17, 2021, was made available online on May 26, 2022.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: “It is common that an applicant is required to answer many redundant questions when filing for a service (e.g. life insurance, loan, medical services, etc.). For example, an application for life insurance may require the user to answer scores of questions, sometimes reaching up to a hundred. In addition to personal, physical descriptive data, the questions can involve detailed probing of medical history, hobbies and activities, general lifestyle related questions, as well as questions relating to the illness history of the family and parents and close kin. Usually any answer indicating that the applicant has suffered from some disease in the past usually leads them down entire paths of other auxiliary and supplemental questions, detailing visits to doctors, hospitals, treatment, drugs etc. The overall universe of questions, the order in which they are I asked, the sequencing and disposition is common for most operators in this domain and does not change from applicant to applicant, regardless of their age, gender, health condition or any other factor which could have a salient bearing on the overall health, prospects for longevity and future health trajectory. These questions can often be asked via a computer interface such as a web browser and/or chat bot agent. Accordingly, improvements to the order, types of questions and reduction of questions are desired in order to increase the engagement of the applicant and improve the overall application experience. The PQG is an operational tool in a user journey of the future, saving time and friction, increasing conversion and satisfaction through a customized set of life insurance application journey questions, based on the applicant age, gender and location.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “A computerized method for natural language generation and managing a parsimonious question generator includes the step of sorting all counties in the United States from a high to low mortality rate by each cause of death for each age group and each gender to generate a set of sorted tables. For a specified subject matter and with a natural language autonomous agent, the method generates a set of parsimonious questions. The set of parsimonious questions are based on a county, an age group, and a gender of an individual designated to answer the set of parsimonious questions. The method sorts the set of parsimonious questions in descending order of importance customized to the individual as described by age, gender, and location. With a natural language generating autonomous agent, the method asks the individual a subset of the set of the parsimonious questions in the optimized order. The method detects that a statistical threshold has been achieved. The method stops the natural language generating autonomous agent from asking further questions in a remainder of the set of parsimonious question when the statistical threshold has been crossed.

“The Figures described above are a representative set and are not an exhaustive with respect to embodying the invention.”

The claims supplied by the inventors are:

“1. A computerized method for natural language generation and managing a parsimonious question generator comprising: sorting all counties in the United States from a high to low mortality rate by each cause of death for each age group and each gender to generate a set of sorted tables; for a specified subject matter and with a natural language autonomous agent: generating a set of parsimonious questions, wherein the set of parsimonious questions are based on a county, an age group, and a gender of an individual designated to answer the set of parsimonious questions; sorting the set of parsimonious questions in descending order of importance customized to the individual as described by age, gender, and location; with a natural language generating autonomous agent, asking the individual a subset of the set of the parsimonious questions in the optimized order; detecting that a statistical threshold has been achieved; and stopping natural language generating autonomous agent from asking further questions in a remainder of the set of parsimonious question when the statistical threshold has been crossed.

“2. The computerized method of claim 1, wherein the set of parsimoniously generated questions are asked via a chat bot functionality in a descending order of a potential contribution to future cause of death.

“3. The computerized method of claim 1 further comprising: using a machine learning algorithm to optimize the sorting of the set of sorted tables.

“4. The computerized method of claim 3, wherein the machine learning algorithm comprises a sorting algorithm.

“5. The computerized method of claim 4, wherein the machine learning algorithm generates question sorting model with a training data set comprising a first historical set of county mortality data for each age group and each gender.

“6. The computerized method of claim 5, wherein the machine learning algorithm uses a second historical set of county mortality data for each age group and each gender to validate the question sorting model.

“7. The computerized method of claim 6 further comprising: using the question sorting mode to sort questions for based on a county, an age group, and a gender of an individual designated to answer the set of parsimonious questions to maximize a maximum percent of causes of death that are obtained in a fewest number of questions.

“8. The computerized method of claim 7, wherein the natural language autonomous agent varies a phraseology of the parsimonious set of questions to increase an engagement of the individual designated to answer the set of parsimonious questions.

“9. The computerized method of claim 7, wherein the natural language autonomous agent adjusts a vocabulary, and a syntax of the set of parsimonious questions based on a common vocabulary for an age of the individual and a county dialect of a county where the individual lives.

“10. The computerized method of 7, wherein the natural language autonomous agent obtains the mortality rate by each cause of death for each age group and each gender for each county in the United States from a plurality of publicly available databases.

“11. A computerized system comprising: a processor configured to execute instructions; a memory containing instructions when executed on the processor, causes the processor to perform operations that: sort all counties in the United States from a high to low mortality rate by each cause of death for each age group and each gender to generate a set of sorted tables; for a specified subject matter and with a natural language autonomous agent: generate a set of parsimonious questions, wherein the set of parsimonious questions are based on a county, an age group, and a gender of an individual designated to answer the set of parsimonious questions; sort the set of parsimonious questions in descending order of importance customized to the individual as described by age, gender, and location; with a natural language generating autonomous agent, ask the individual a subset of the set of the parsimonious questions in the optimized order; detect that a statistical threshold has been achieved; and stop natural language generating autonomous agent from asking further questions in a remainder of the set of parsimonious question when the statistical threshold has been crossed.

“12. The computerized system of claim 11, wherein the set of parsimoniously generated questions are asked via a chat bot functionality in a descending order of a potential contribution to future cause of death.

“13. The computerized system of claim 11, wherein the memory contains instructions that when executed on the processor, causes the processor to perform operations that: use a machine learning algorithm to optimize the sorting of the set of sorted tables.

“14. The computerized system of claim 13, wherein the machine learning algorithm comprises a sorting algorithm.

“15. The computerized system of claim 14, wherein the machine learning algorithm generates question sorting model with a training data set comprising a first historical set of county mortality data for each age group and each gender.

“16. The computerized system of claim 15, wherein the machine learning algorithm uses a second historical set of county mortality data for each age group and each gender to validate the question sorting model.

“17. The computerized system of claim 16, wherein the memory contains instructions that when executed on the processor, causes the processor to perform operations that: uses the question sorting mode to sort questions for based on a county, an age group, and a gender of an individual designated to answer the set of parsimonious questions to maximize a maximum percent of causes of death that are obtained in a fewest number of questions.

“18. The computerized system of claim 17, wherein the natural language autonomous agent varies a phraseology of the parsimonious set of questions to increase an engagement of the individual designated to answer the set of parsimonious questions.

“19. The computerized system of 18, wherein the natural language autonomous agent obtains the mortality rate by each cause of death for each age group and each gender for each county in the United States from a plurality of publicly available databases.

“20. The computer system of claim 19, wherein a statistical threshold is generated based on historic volatility, goodness of fit of models and variable nature of the top causes of death historically.”

For more information, see this patent application: Bardhan, Ashok; Gupta, Avaneendra. Natural Language Processing Parsimonious Question Generator. Filed September 17, 2021 and posted May 26, 2022. Patent URL: https://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220220164549%22.PGNR.&OS=DN/20220164549&RS=DN/20220164549

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