Patent Issued for Methods And Systems For Ranking Leads Based On Given Characteristics (USPTO 10,394,834)

2019 SEP 10 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Ross, Gareth (Amherst, MA); Walker, Tricia (East Hampton, MA), filed on October 22, 2018, was published online on September 9, 2019.

The assignee for this patent, patent number 10,394,834, is Massachusetts Mutual Life Insurance Company (Springfield, Massachusetts, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “Leads generation or acquisition is one of the most important tools for boosting sales in a company. The use of leads allows buyers to request information from businesses that offer a product or service they are interested in acquiring while enabling companies to offer their products or services to individuals with a higher propensity to close a transaction.

“Although sales leads offer an opportunity to create value for a company, there are some challenges for its efficient utilization by a business. For example, when companies generate leads from a variety of sources, often the sources providing the leads do not provide enough information about the quality of the leads generated. This can result in an agent to wasting time, effort, and financial resources by establishing contact with a lead that may not be financially ready or interested in purchasing a service and/or product.

“For the aforementioned reasons, there is a need for a method and system that provides a company with insight about the quality of leads so that the company can focus its efforts and resources on the most promising leads.”

In addition to obtaining background information on this patent, NewsRx editors also obtained the inventors’ summary information for this patent: “A method for ranking and appraising leads according to their quality is disclosed. In one embodiment, a system architecture allowing the method to operate is disclosed. The system architecture includes one or more client computing devices, an analytical engine, an external data sources database, an internal database and a network connection. In this embodiment, the analytical engine is a software module implemented in one or more computing devices. Further to this embodiment, the analytical engine includes a ranking module and a price modeling module. In one embodiment, the ranking module retrieves information related to a set of leads in order to rank them based on their quality. Afterwards, the price modeling module determines a floor price based on said ranking and based on internal and external valuation information. In some embodiments, the analytical engine stores the solutions derived from the aforementioned software modules in the internal database for it to be available to other software modules operating within a systems’ architecture configured to rank and auction leads. Additionally, the results stored at the internal database are employed for future analysis using a client computer device.

“In one embodiment, a computer-implemented method for ranking leads, comprises collecting, by a computer, lead information related to one or more leads; classifying, by the computer, lead information into categories of lead information; receiving, by the computer, from a computer of an agent a selection of a first category of lead information and an attribute of the first category; filtering, by the computer, the lead information to obtain a set of filtered lead information comprising only the lead with the selected attribute; determining, by the computer, a set of attributes associated with the lead from the filtered lead information; assigning, by the computer, a score to each attribute associated with the lead from the filtered lead information based on a measure of how each attribute satisfies a predetermined set of criteria; calculating, by the computer, a quality score by computing the mean score for each lead based on the each scored attribute; ranking, by the computer, each lead based on the quality score; and associating, by the computer, the lead information with the ranked leads and the quality score.

“In another embodiment, a system for ranking leads comprises a computer processor; a memory containing a program that, when executed by the computer processor, is configured to perform an operation comprising collecting, by the computer processor, lead information related to one or more leads; classifying, by the computer processor, lead information into categories of lead information; receiving, by the computer processor, from a computer of an agent a selection of a first category of lead information and an attribute of the first category; filtering, by the computer processor, the lead information to obtain a set of filtered lead information comprising only the lead with the selected attribute; determining, by the computer processor, a set of attributes associated with the lead from the filtered lead information; assigning, by the computer processor, a score to each attribute associated with the lead from the filtered lead information based on a measure of how each attribute satisfies a predetermined set of criteria; calculating, by the computer processor, a quality score by computing the mean score for each lead based on the each scored attribute; ranking, by the computer processor, each lead based on the quality score; and associating, by the computer processor, the lead information with the ranked leads and the quality score.

“In one embodiment, a computer-implemented method for leads appraisal, comprises collecting, by a computer, lead information related to one or more leads; filtering, by the computer, the lead information to obtain a set of filtered lead information comprising a ranking associated to the one or more leads, internal valuation information, and external valuation information; determining, by the computer, one or more categories defined by the ranking associated to the one or more leads; defining, by the computer, a floor price for the set of leads associated to each rank category based on internal and external valuation information.

“In another embodiment, a system for leads appraisal comprises a computer processor; a memory containing a program that, when executed by the computer processor, is configured to perform an operation comprising collecting, by the computer processor, lead information related to one or more leads; filtering, by the computer processor, the lead information to obtain a set of filtered lead information comprising a ranking associated to the one or more leads, internal valuation information, and external valuation information; determining, by the computer processor, one or more categories defined by the ranking associated to the one or more leads; defining, by the computer processor, a floor price for the set of leads associated to each rank category.

“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: periodically scanning, by a first processor of a computer comprising at least two processors, one or more social networking web documents to extract one or more characteristic values associated with a set of leads; upon receiving from a computer of an agent, a selection of a lead attribute from a set of lead attributes, filtering, by the first processor, lead information from the one or more social networking web documents to obtain a set of filtered lead information comprising only a first subset of leads containing the lead attribute; executing, by the first processor, a machine-learning model to calculate a score for each lead within the first subset of leads where the machine-learning model is configured to calculate the score for each lead based on a learning dataset; while the first processor is executing the machine-learning model, iteratively updating, by a second processor of the computer, the learning dataset of the machine-learning model based on modified data associated with each lead, wherein the second processor is configured to periodically query one or more databases to receive inputs on the modified data associated with each lead and, in an event that the second processor determines that data associated with the lead attributes is changed, the second processor adjusts the learning dataset; generating, by the first processor, a second subset of leads that imply a highest potential value of each lead based on the score; and updating, by the first processor, a graphical user interface of the computer of the agent with information comprising the second subset of leads.

“2. The computer-implemented method according to claim 1, wherein the lead information comprises at least one of: identity, age, ethnicity, place of residence, number of dependent persons, identity of dependent persons, expenditure, savings, approximate market value of assets and their composition, education, professional situation, contact information, shopping preferences, travel preferences, hobbies, social activities, active lifestyle, online behavioral patterns, medical and health records.

“3. The computer-implemented method according to claim 1, wherein the lead information comprises one or more categories selected from a group consisting at least one of: geography, demographics, asset value, and credit score.

“4. The computer-implemented method according to claim 1, wherein the set of lead attributes associated with the lead comprises at least one of: economic stability, credit card usage, liquid assets, education level, discretionary spending, and retirement assets.

“5. The computer-implemented method of claim 1, further comprising ranking, by the first processor, each of the first subset of leads based on their corresponding score.

“6. The computer-implemented method of claim 5, further comprising triaging, by the first processor, each ranked lead that imply the highest potential value of each lead based on the score.

“7. The computer-implemented method of claim 1, wherein the machine-learning model utilizes a naive bayes and a logistic regression technique.

“8. The computer-implemented method of claim 1, wherein the lead is a potential new customer.

“9. The computer-implemented method of claim 1, wherein the lead is a group of potential new customers.

“10. The computer-implemented method of claim 1, wherein the score is calculated based on a support vector machine and a random forest technique.

“11. A system for ranking leads, the system comprising: a computer having a first and a second processor; a memory containing a program that, when executed by the first and the second processor, is configured to perform an operation comprising: periodically scanning, by a first processor of a computer comprising at least two processors, one or more social networking web documents to extract one or more characteristic values associated with a set of leads; upon receiving from a computer of an agent, a selection of a lead attribute from a set of lead attributes, filtering, by the first processor, lead information from the one or more social networking web documents to obtain a set of filtered lead information comprising only a first subset of leads containing the lead attribute; executing, by the first processor, a machine-learning model to calculate a score for each lead within the first subset of leads where the machine-learning model is configured to calculate the score for each lead based on a learning dataset; while the first processor is executing the machine-learning model, iteratively updating, by a second processor of the computer, the learning dataset of the machine-learning model based on modified data associated with each lead, wherein the second processor is configured to periodically query one or more databases to receive inputs on the modified data associated with each lead and, in an event that the second processor determines that data associated with the lead attributes is changed, the second processor adjusts the learning dataset; generating, by the first processor, a second subset of leads that imply a highest potential value of each lead based on the score; and updating, by the first processor, a graphical user interface of the computer of the agent with information comprising the second subset of leads.

“12. The system according to claim 11, wherein the lead information comprises at least one of: identity, age, ethnicity, place of residence, number of dependent persons, identity of dependent persons, expenditure, savings, approximate market value of assets and their composition, education, professional situation, contact information, shopping preferences, travel preferences, hobbies, social activities, active lifestyle, online behavioral patterns, medical and health records.

“13. The system according to claim 11, wherein the lead information comprises one or more categories selected from a group consisting at least one of: geography, demographics, asset value, and credit score.

“14. The system according to claim 11, wherein the set of lead attributes associated with the lead comprises at least one of: economic stability, credit card usage, liquid assets, education level, discretionary spending, and retirement assets.

“15. The system of claim 11, wherein the lead is a potential new customer.

“16. The system of claim 11, wherein the lead is a group of potential new customers.

“17. The system of claim 11, wherein the score is calculated for each lead based on a support vector machine and a random forest technique.

“18. The system of claim 11, wherein the machine-learning model utilizes a naive bayes and a logistic regression technique.

“19. The system of claim 1, wherein the first processor is configured to rank each of the first subset of leads based on their corresponding score.

“20. The system of claim 19, wherein the first processor is configured to triage each ranked lead that imply the highest potential value of each lead based on the score.”

For more information, see this patent: Ross, Gareth; Walker, Tricia. Methods And Systems For Ranking Leads Based On Given Characteristics. U.S. Patent Number 10,394,834, filed October 22, 2018, and published online on September 9, 2019. 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=10,394,834.PN.&OS=PN/10,394,834RS=PN/10,394,834

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