Patent Issued for Systems And Methods For Automated Body Mass Index Calculation (USPTO 10,748,217)

2020 AUG 28 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Massachusetts Mutual Life Insurance Company (Springfield, Massachusetts, United States) has been issued patent number 10,748,217, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Ross, Gareth (Amherst, MA); Ben-Zvi, Yaron (Hastings on Hudson, NY); Merritt, Sears (Groton, MA).

This patent was filed on April 20, 2016 and was published online on August 31, 2020.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Body mass index (BMI) is a wide known indicator employed in health insurance. Health insurance companies look at body mass index for insurance rating purposes for the reason that BMI is a significant factor correlated with health risk conditions, such as obesity and heart disease. Individuals applying for an insurance plan may be classified in different categories depending on their respective BMI, which may affect life insurance premiums set to the individuals.

“Currently, many insurance companies require paramedical examiners to visit potential clients for the purpose of administering one or more tests, including BMI samples. BMI samples could include acquiring bodily fluids (e.g., blood, urine) from the potential clients. These tests imply potential delays in gathering the information required, and may be subject to human error. Although a BMI calculation requires only a few pieces of information from potential clients, such as height and weight, and potential clients are able to provide this information through any communication technology available, there is still a need to validate the information provided by the potential clients.

“Thus, there is a need for providing systems methods to address these and other concerns.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “Systems and methods for an automated body mass index calculation are disclosed. In one embodiment, a system architecture may include components, such as one or more computing devices connected to one or more servers via a network connection. In this embodiment, the one or more servers include an analytical engine that coordinates multiple algorithms for data fetching, image processing tasks, and predictive analytics. The aforementioned algorithms may be executed by the server processor and/or the computing device processor. In one or more embodiments, the one or more servers are in communication with a database so that the analytical engine has access to relevant multimedia data associated with a potential client.

“In another embodiment, a method for an automated body mass index calculation may include a computing device that allows an agent to request a body mass index calculation of a specific potential client. In this embodiment, the request is processed by a server. Further to this embodiment, the server is in communication with a database containing relevant multimedia information associated with a customer, and includes an analytical engine coordinating multiple algorithms. In one or more embodiments, the analytical engine includes a data extraction module and a data processing module. In these embodiments, the data extraction module fetches relevant multimedia information regarding a potential client, and makes the relevant multimedia information available to the data processing module. In one embodiment, the data processing module performs feature detection over the multimedia information, computes one or more feature values or feature vectors, normalizes the feature values, and uses the normalized feature values along with one or more regression algorithms for calculating the body mass index associated with a potential client.

“One embodiment of a computer-implemented method may include receiving height and weight data of a potential customer. Upon receipt of the height and weight data, a request may be made for an image of the potential customer to be captured from a remote computing device, where the requested image includes a standard sized object positioned in the image according to at least one reference point. An image of the potential customer may be received, where the image is captured and transmitted from the remote computing device, where the image includes a standard sized object positioned in the image according to the at least one reference point. Upon receipt of the image, at least one anatomical region of the potential customer may be detected, a calculation of a feature value of the detected at least one anatomical region of the potential customer in comparison to the standard sized object positioned in the image according to the at least one reference point may be made, where the calculation includes utilizing at least one image processing technique on the detected at least one anatomical region of the potential customer and on the standard sized object positioned in the image according to the at least one reference point feature value, the feature value may be normalized, a body mass index (BMI) of the potential customer may be predicted based on the normalized feature value, and the BMI may be caused to be transmitted to a computing device. In transmitting the BMI, the BMI may be caused to be displayed on a graphical user interface.

“One embodiment of a system and computerized-method may include receiving height and weight data of a potential customer. Upon receipt of the height and weight data, a request may be made for a first electronic image of the potential customer to be captured from an image capture device of the potential customer, where the requested image includes a standard sized object positioned in the image according to at least one reference point for the first image. An first electronic image of the potential customer may be received via a communications network from the image capture device of the potential customer at a first time, where the first image is captured and transmitted from the image capture device, where the first image includes a standard sized object positioned in the image according to the at least one reference point for the first image. A baseline body mass index (BMI) of the potential customer may be computed as a function of the height and weight data and the first image inclusive of the standard sized object positioned in the image according to the least one reference point for the first image. A determination of a baseline underwriting value (e.g., for an insurance policy) may be made as a function of the computed BMI for the potential customer (e.g., to be an insured under an insurance policy).

“Updated height and weight data of the insured may be received at a second time. Upon receipt of the updated height and weight data, a request may be made for a second electronic image of the potential customer to be captured from the image capture device of the potential customer, where the requested image includes a standard sized object positioned in the image according to at least one reference point for the second image. A second electronic image of the potential customer may be received via the communications network from the image capture device of the potential customer at a second time, where the second image is captured and transmitted from the image capture device, where the second image includes a standard sized object positioned in the image according to the at least one reference point for the second image. The standard sized objects in the first and second images may be the same or different standard sized objects. An updated BMI of the insured may be computed as a function of the updated height and weight data and the second image inclusive of the standard sized object positioned in the image according to the least one reference point for the second image. A determination of an updated value (e.g., underwriting value) may be made (e.g., for an insurance policy) as a function of the computed updated BMI.

“Numerous other aspects, features and benefits of the present disclosure may be made apparent from the following detailed description taken together with the drawing figures.”

The claims supplied by the inventors are:

“What is claimed is:

“1. A computer-implemented method comprising: upon receiving, by a processing unit, height and weight data of a potential customer, requesting, by the processing unit, an image of the potential customer to be captured from a remote computing device, the requested image being inclusive of a standard sized object positioned in the image according to at least one reference point; receiving, by the processing unit, an image of the potential customer, wherein the image is captured and transmitted from the remote computing device, the image being inclusive of the standard sized object positioned in the image according to the least one reference point; and upon receiving the image; detecting, by the processing unit, at least one anatomical region of the potential customer from the image, calculating, by the processing unit, a feature value of the detected at least one anatomical region of the potential customer within the image in comparison to the standard sized object positioned in the image according to the at least one reference point, wherein the calculating comprises utilizing at least one image processing technique on the detected at least one anatomical region of the potential customer within the image and on the standard sized object positioned in the image according to the at least one reference point, normalizing, by the processing unit, the feature value, executing, by the processing unit, a predictive model to predict body mass index (BMI) of the potential customer based on the normalized feature value of the image, the predictive model comprising a machine learning algorithm trained in accordance with historical data corresponding to previous customer feature values and their respective BMI; and causing, by the processing unit, the BMI to be transmitted to a computing device.

“2. The method of claim 1, wherein receiving the image being inclusive of a standard sized object positioned in the image according to the least one reference point comprises receiving an image being inclusive of a credit card positioned in the image according to the least one reference point.

“3. The method of claim 1, wherein receiving the image comprises receiving the image inclusive of at least a partially unclothed, upper torso of the potential customer.

“4. The method of claim 1, further comprising: calculating, by the processing unit, the BMI using only the height and weight data of the potential customer; calculating, by the processing unit, a delta value between (i) the BMI calculated using only the height and weight data and (ii) the BMI predicted using the normalized feature value; and causing, by the processing unit, the delta value to be presented.

“5. The method of claim 1, wherein predicting the BMI comprises computing a regression algorithm.

“6. The method of claim 5, further comprising training the regression algorithm by using a set of faces associated with individuals with respective known BMIs.

“7. The method of claim 1, further comprising: calculating BMI of the potential customer using only the height and weight data of the potential customer; and determining, by the processing unit, a category of life insurance of which the potential customer qualifies based on the BMI calculated using only the height and weight data.

“8. The method of claim 1, wherein receiving the image comprises receiving an image captured by a computing device with an integrated camera.

“9. The method of claim 8, wherein receiving the image comprises receiving a video image during a real-time video call.

“10. The method of claim 1, wherein detecting the at least one anatomical region comprises detecting the at least one anatomical region utilizing at least one edge detector on the image.

“11. The method of claim 1, further comprising determining a dimension of the at least one anatomical region of the potential customer.

“12. The method of claim 1, wherein receiving the image being inclusive of the standard sized object positioned in the image according to the least one reference point comprises: receiving, by the remote computing device, a request to capture an image of the potential customer; displaying, by the remote computing device, the at least one reference point; conveying, by the remote computing device, a message requesting the potential customer to hold the standard sized object up against an anatomical part of the potential customer; conveying, by the remote computing device, a message requesting the potential customer to align the standard sized object with the at least one reference point; and capturing, by a camera coupled to the remote computing device, an image of the potential customer holding the standard sized object when it is detected, by the remote computing device, that the standard sized object is aligned with the at least one reference point.

“13. A system for determining body mass index (BMI) of a potential customer, the system comprising: a non-transitory memory configured to store data; an input/output unit configured to bi-directionally communicate data over a communications network; and a processing unit in communication with the non-transitory memory and input/output unit, and configured to receive height and weight data of a potential customer, request, upon receiving the height and weight of the potential customer, an image of the potential customer to be captured from a remote computing device, the requested image being inclusive of a standard sized object positioned in the image according to at least one reference point, receive an image of the potential customer, wherein the image is captured and transmitted from the remote computing device, the image being inclusive of the standard sized object positioned in the image according to the least one reference point, upon receiving the image; detect at least one anatomical region of the potential customer from the image, calculate a feature value of the detected at least one anatomical region of the potential customer within the image in comparison to the standard sized object positioned in the image according to the at least one reference point, wherein the processing unit is further configured to calculate the feature value by utilizing at least one image processing technique on the detected at least one anatomical region of the potential customer and on the standard sized object positioned in the image according to the at least one reference point, normalize the feature value, execute a predictive model to predict body mass index (BMI) of the potential customer based on the normalized feature value of the image, the predictive model comprising a machine learning algorithm trained in accordance with historical data corresponding to previous customer feature values and their respective BMI, and cause the BMI to be transmitted to a computing device.

“14. The system of claim 13, wherein the standard sized object is a credit card.

“15. The system of claim 13, wherein the image comprises an image of at least a partially unclothed, upper torso of the potential customer.

“16. The system of claim 13, wherein said processing unit is further configured to: calculate the BMI using only the height and weight data of the potential customer; calculate a delta value between (i) the BMI calculated using only the height and weight data and (ii) BMI predicting using the normalized feature value; and cause the delta value to be presented.

“17. The system according to claim 13, wherein said processing unit is configured to predict the BMI by computing a regression algorithm.

“18. The system according to claim 17, wherein said processing unit is further configured to train the regression algorithm by using a set of faces associated with individuals with respective known BMIs.

“19. The system according to claim 13, wherein said processing unit is further configured to calculate BMI of the potential customer using only the height and weight data of the potential customer, and determine a category of life insurance of which the potential customer qualifies based on the BMI calculated using only the height and weight data.

“20. The system according to claim 13, wherein the image is captured by a computing device with an integrated camera.

“21. The system according to claim 20, wherein said processing unit is configured to receive the image as a video image during a real-time video call.

“22. The system according to claim 13, wherein said processing unit, in detecting the at least one anatomical region, is configured to detect the at least one anatomical region utilizing at least one edge detector on the image.

“23. The system according to claim 13, wherein said processing unit is further configured to determine a dimension of the at least one anatomical region of the potential customer.

“24. The system of claim 13, wherein the remote computing device is configured to receive a request to capture an image of the potential customer, display the at least one reference point to the potential customer, convey a message requesting the potential customer to hold the standard sized object up against an anatomical part of the potential customer, convey a message requesting the potential customer to align the standard sized object with the at least one reference point, and capture, by a camera coupled to the remote computing device, an image of the potential customer holding the standard sized object when it is detected, by the remote computing device, that the standard sized object is aligned with the at least one reference point.”

For the URL and additional information on this patent, see: Ross, Gareth; Ben-Zvi, Yaron; Merritt, Sears. Systems And Methods For Automated Body Mass Index Calculation. U.S. Patent Number 10,748,217, filed April 20, 2016, and published online on August 31, 2020. 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=10,748,217.PN.&OS=PN/10,748,217RS=PN/10,748,217

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