Patent Issued for Systems and methods for processing air particulate datasets (USPTO 11796524): Massachusetts Mutual Life Insurance Company

2023 NOV 15 (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 11796524, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Merritt, Sears (Groton, MA, US), Ross, Gareth (Amherst, MA, US).

This patent was filed on January 31, 2019 and was published online on October 24, 2023.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Many industrial activities produce pollution of one or more types. Examples of the pollution include carbon dioxide emissions, greenhouse gases, noise pollution, light pollution, consumer waste pollution, and any combinations thereof. Several organizations collect pollutant data in various cities around the world in order to quantify and analyze the pollutant data, which result from human and industrial activity, due to the increasing concern of the negative impact of the pollution on the human health and environment.

“In China, several organizations have collected pollutant data for approximately last 40 years. The organizations made use of approximately 42,000 pollutant data collectors all across China over a period of 40 years because of which the monitored and collected air particulate datasets are now available for almost all Chinese provinces and municipalities, with some provinces containing multiple data points from a few cities and air monitoring stations. The thousands of pollutant data collectors report big air particulate datasets having concentrations in (mg/m3) for major pollutants such as sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM10, particles with a diameter of 10 microns or less) in real-time, hourly basis.

“Various problems occur in connection with management and processing of the big pollutant datasets, using conventional technologies, since the pollutant datasets are so large and complex that the pollutant datasets become difficult to work with using on-hand management and analytical tools. In particular, the processing of the larger pollutant datasets for any management application may cause problems involving access times, processing costs, and load balancing requirements among multiple pollutant data collector centers that are geographically dispersed, and subject to varying levels of access demands depending on particular locations and/or particular times.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “What is therefore needed is a method and a system that efficiently and effectively manages and processes distributed big pollutant datasets generated across multiple sites at real-time for an application. The processed big pollutant datasets may be used as an input to generate an artificial intelligence model, such as a mortality model, which may be executed to determine eligibility of a user to avail a health-related service. When facing a high number of big pollutant datasets from multiple sites, a computer-specific set of rules may also be applied to the mortality model to produce efficient and accurate results for each user.

“In one embodiment, a server-implemented method may include receiving, by a server programmed according to an event-driven asynchronous architecture, from a client computing device operated by a user, a request from an input field of a network page served to the client computing device, the request comprising one or more attributes of the user, a list of locations, and a time period during which the user has been associated with each location; in response to receiving the request, querying, by the server, an application server to receive a set of air particulate datasets corresponding to the user, wherein each set of air particulate datasets comprises air particulate data associated with at least one location within the list of locations and a corresponding time period; generating, by the server, a multi-dimensional feature vector where each dimension corresponds to a location from the list of locations and the corresponding time period at which the user is associated with the location; retrieving, by the server, one or more artificial intelligence models in accordance with the multi-dimensional feature vector, each artificial intelligence model having a neural network that incorporates air particulate data; executing, by the server, the one or more artificial intelligence models based on the multi-dimensional feature vector and the set of air particulate datasets; generating, by the server executing the one or more artificial intelligence models, an indicator corresponding to an output of the one or more artificial intelligence models; and upon the indicator satisfying a threshold, executing, by the server, a pricing model module.

“In another embodiment, a system may include a network, a client computing device operated by a client, and a server programmed according to an event-driven asynchronous architecture. The server is configured to receive from the client computing device a request from an input field of a network page served to the client computing device, the request comprising one or more attributes of the user, a list of locations, and a time period during which the user has been associated with each location; query an application server to receive a set of air particulate datasets corresponding to the user, wherein each set of air particulate datasets comprises air particulate data associated with at least one location within the list of locations and a corresponding time period; generate a multi-dimensional feature vector where each dimension corresponds to a location from the list of locations and the corresponding time period at which the user is associated with the location; retrieve one or more artificial intelligence models in accordance with the multi-dimensional feature vector, each artificial intelligence model having a neural network that incorporates air particulate data; execute the one or more artificial intelligence models based on the multi-dimensional feature vector and the set of air particulate datasets; generate an indicator corresponding to an output of the one or more artificial intelligence models; and upon the indicator satisfying a threshold, execute a pricing model module.

“It is to be understood that both the foregoing general description and the following detailed description are explanatory and are intended to provide further explanation of the subject matter as claimed.”

The claims supplied by the inventors are:

“1. A server-implemented method comprising: receiving, by a server programmed according to an event-driven asynchronous architecture, from a client computing device operated by a user, a request from an input field of a network page served to the client computing device, the request comprising one or more attributes of the user, a list of locations, and a time period during which the user has been associated with each location; in response to receiving the request, querying, by the server, an application server to receive a set of air particulate datasets corresponding to the user, wherein the set of air particulate datasets comprises air particulate data associated with at least one location within the list of locations and a corresponding time period; generating, by the server, a multi-dimensional feature vector where each dimension corresponds to a location from the list of locations and the corresponding time period at which the user is associated with the location; selecting, by the server, based on the list of locations, from a plurality of artificial intelligence models, one or more artificial intelligence models suitable for the multi-dimensional feature vector and respectively corresponding to each location in the list of locations, each artificial intelligence model having a neural network that is trained on multi-dimensional feature vectors corresponding to users, information associated with user profiles of the users, and air particulate data corresponding to each location in the list of locations, each of the one or more artificial intelligence models trained using a respective dataset corresponding to a respective location of the list of locations, the one or more artificial intelligence models associated with a set of rules corresponding to a plurality of locations; executing, by the server, the one or more artificial intelligence models based on the multi-dimensional feature vector and the set of air particulate datasets; generating, by the server executing the one or more artificial intelligence models, a score by applying the set of rules to an output of each of the one or more artificial intelligence models; and executing by the server, a pricing module based on the user profile of the user and the score, that results in providing a health-related service recommendation for the user and a price range for the health-related service.

“2. The server-implemented method according to claim 1, wherein each dataset of the set of air particulate datasets contains a location identifier from one or more pollutant data collectors configured to monitor and detect air particulate data for one or more locations.

“3. The server-implemented method according to claim 2, wherein each of the one or more pollutant data collectors comprises one or more sensor devices configured to generate the set of air particulate datasets containing information corresponding to air particulate data detected by the one or more sensor devices, wherein the one or more pollutant data collectors store the set of air particulate datasets in the one or more databases.

“4. The server-implemented method according to claim 3, wherein the one or more sensor devices transmits one or more control signals containing information regarding the air particulate data to the one or more pollutant data collectors, and wherein the one or more control signals are RF signals.

“5. The server-implemented method according to claim 4, wherein the one or more control signals comprises a unique identification code identifying the one or more sensor devices and a location identifier identifying the location of the one or more sensor devices.

“6. The server-implemented method according to claim 2, wherein the one or more pollutant data collectors are mobile collectors and located in an automobile.

“7. The server-implemented method according to claim 2, wherein the one or more pollutant data collectors are static collectors and located in an industrial facility.

“8. The server-implemented method according to claim 1, wherein the one or more artificial intelligence models are further configured to generate the score for the set of air particulate datasets based at least in accordance with one or more characteristics associated with each location corresponding to the set of air particulate datasets, wherein the one or more characteristics comprises at least an altitude of each location and a temperature value at each location.

“9. The server-implemented method according to claim 1, wherein a dynamically updated network page to the client computing device based on the score displays a status of eligibility for availing one or more health-related services, and wherein the status of eligibility comprises an acceptance message or a non-acceptance message regarding availability of the one or health-related services.

“10. The method of claim 1, further comprising updating, by the server, the one or more artificial intelligence models based on the score.

“11. A system comprising: a network; a client computing device operated by a user; and a server programmed according to an event-driven asynchronous architecture, wherein the server is configured to: receive from the client computing device a request from an input field of a network page served to the client computing device, the request comprising one or more attributes of the user, a list of locations, and a time period during which the user has been associated with each location; query an application server to receive a set of air particulate datasets corresponding to the user, wherein the set of air particulate datasets comprises air particulate data associated with at least one location within the list of locations and a corresponding time period; generate a multi-dimensional feature vector where each dimension corresponds to a location from the list of locations and the corresponding time period at which the user is associated with the location; select, based on the list of locations, from a plurality of artificial intelligence models, one or more artificial intelligence models suitable for the multi-dimensional feature vector and respectively corresponding to each location of the list of locations, each artificial intelligence model having a neural network that is trained on multi-dimensional feature vectors corresponding to users, information associated with user profiles of the users, and air particulate data corresponding to each location in the list of locations, each of the one or more artificial intelligence models trained using a respective dataset corresponding to a respective location of the list of locations, the one or more artificial intelligence models associated with a set of rules corresponding to a plurality of locations; execute the one or more artificial intelligence models based on the multi-dimensional feature vector and the set of air particulate datasets; generate a score by applying the set of rules to an output of each of the one or more artificial intelligence models; and execute a pricing module based on the user profile of the user and the score, that results in providing a health-related service recommendation for the user and a price range for the health-related service.

“12. The system according to claim 11, wherein each dataset of the set of air particulate datasets contains a location identifier from one or more pollutant data collectors configured to monitor and detect air particulate data for one or more locations.

“13. The system according to claim 12, wherein each of the one or more pollutant data collectors comprises one or more sensor devices configured to generate the set of air particulate datasets containing information corresponding to air particulate data detected by the one or more sensor devices, wherein the one or more pollutant data collectors store the set of air particulate datasets in the one or more databases.

“14. The system according to claim 13, wherein the one or more sensor devices transmits one or more control signals containing information regarding the air particulate data to the one or more pollutant data collectors, and wherein the one or more control signals are RF signals.

“15. The system according to claim 14, wherein the one or more control signals comprises a unique identification code identifying the one or more sensor devices and a location identifier identifying the location of the one or more sensor devices.

“16. The system according to claim 12, wherein the one or more pollutant data collectors are mobile collectors and located in an automobile.

“17. The system according to claim 12, wherein the one or more pollutant data collectors are static collectors and located in an industrial facility.

“18. The system according to claim 11, wherein the one or more artificial intelligence models are further configured to generate the score for the set of air particulate datasets based at least in accordance with one or more characteristics associated with each location corresponding to the set of air particulate datasets, wherein the one or more characteristics comprises at least an altitude of each location and a temperature value at each location.

“19. The system according to claim 11, wherein a dynamically updated network page served to the client computing device displays a status of eligibility for availing one or more health-related services, and wherein the status of eligibility comprises an acceptance message or a non-acceptance message regarding availability of the one or health-related services.

“20. The system of claim 11, wherein the server is further configured to update the one or more artificial intelligence models based on the score.”

For the URL and additional information on this patent, see: Merritt, Sears. Systems and methods for processing air particulate datasets. U.S. Patent Number 11796524, filed January 31, 2019, and published online on October 24, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11796524)&db=USPAT&type=ids

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