Patent Issued for Entity prioritization and analysis systems (USPTO 11188985): Aon Risk Services Inc. of Maryland

2021 DEC 21 (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 Chmielewski, Nicholas Joseph (Chicago, IL, US), Crouse, Daniel (Seattle, WA, US), Lietz, Derek Charles (Boston, MA, US), Neagu, Iulia A. (Boston, MA, US), Thiagarajan, Vinayak (Chicago, IL, US), filed on August 16, 2019, was published online on November 30, 2021.

The assignee for this patent, patent number 11188985, is Aon Risk Services Inc. of Maryland (New York, New York, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “Litigation involving intellectual-property claims has been prevalent for some time. Defending against such claims may be costly and time consuming. Described herein are improvements in technology and solutions to technical problems that can be used to, among other things, assist in identifying entities likely to benefit from insurance covering intellectual-property claims and analyzing data associated with the entity.”

In addition to obtaining background information on this patent, NewsRx editors also obtained the inventors’ summary information for this patent: “Techniques and systems described herein are directed to evaluating degrees of exposure related to intellectual-property claims for one or more entities. Take, for example, an entity that sells goods and/or services in a market with one or more competitors. The competitors may have one or more intellectual property assets, such as patents, copyrights, trademarks, and/or trade secrets, that the competitors may attempt to assert against the entity. For example, one or more of the competitors may file a lawsuit claiming infringement and/or misappropriation of their intellectual-property assets. The entity may attempt to defend against such a lawsuit, but doing so is often expensive and time consuming. For many entities, the costs involved in defending against an intellectual-property claims are prohibitively high and would have a drastic negative impact on the entity and/or its business, regardless of and especially with a finding of liability. In these and other examples, an insurer may offer an insurance policy to insure the entity in the event that such an intellectual-property claim is levied against the entity.

“Typically, insurance policies are issued to an individual, group of individuals, or an organization, which may be referred to as the entity and/or the insured, by an additional organization, which may be referred to as the insurer. In some scenarios, the insurer may provide the insured with a monetary payout when the insured incurs a loss that is covered by the insurance policy. Such losses may include, for example, damage awards, litigation expenses, settlements, indemnification obligations, court costs, and/or other monetary amounts associated with defending legal claims and/or complying with court orders. Often, the insured pays an amount to the insurer on a regular basis in order to keep the policy active. This amount may be referred to as a premium. Additionally, the insurance policy may place a limit on the amount of payout by the insurer to the insured in case of damages incurred by the insured covered under the policy. The maximum payout by the insurer may be referred to as the policy limit. Further, in various situations, the insurer and the insured may share the expense of the loss. The sharing of the expense of the loss may be expressed as a percentage of a given loss for which the insurer is responsible and a percentage of the given loss for which the insured is responsible. The sharing of the loss may be referred to as co-insurance. Additionally, in examples, retention values may be determined with respect to an insurance policy, where the insured may retain exposure up to a certain dollar amount and the insurer may pay damages above that retention value limit. However, insurance policies covering intellectual-property claims are not frequently offered, the premiums associated with such insurance policies may be expensive, and the terms and conditions of such insurance policies may not provide adequate coverage for the entity.

“Described herein are systems and techniques that, among other things, prioritize entities that may be candidates for insurance policies that insure against intellectual-property claims. The systems and techniques may also analyze information associated with the entities to rate or otherwise evaluate a likelihood that a given entity will be involved in defending against an intellectual-property claim. For example, an insurer may maintain a database of information associated with entities that have acquired or applied for one or more insurance policies from the insurer. Those insurance policies may include, for example, general liability insurance, product liability insurance, etc., as well as IP infringement liability insurance. The information may include, for example, an identifier of a given entity, one or more industries associated with the entity, a revenue associated with the entity, a profit associated with the entity, an indication of intellectual-property litigation history, a number of employees associated with the entity, an indication of the insurance policies held by the entity, and/or a monetary amount paid by the entity to the insurer. In examples, at least a portion of this information may be publicly available and may be obtained from publicly-available databases. In further examples, at least a portion of this information may be stored with respect to a given entity, and the information may be obtained from systems and/or devices associated with the given entity upon receiving authorization to obtain such information from the entity.

“The information described above may be received at a broker system and may be analyzed to determine a joint probability value indicating a likelihood and/or expected monetary amount of loss and/or associated information associated with a given entity. Additionally, or alternatively, the information described above may be identified, determined, and/or generated by the broker system. For example, the broker system may generate and maintain a predictive model configured to accept features and/or feature vectors corresponding to the entity information. For example, the entity information, when received by the broker system, may be formatted into a feature and/or feature vector for input into the predictive model. The predictive model may utilize the features and/or feature vectors to generate output data associated with the entity. For example, the output data may include a frequency value indicating an anticipated frequency of the entity being involved in defending against an intellectual-property claim. The output data may also include a severity value indicating a monetary amount and/or a range of monetary amounts of anticipated damage to the entity associated with the intellectual-property claim. The output data may also include an indication of a most-probable type of intellectual-property claim that may be levied against the entity based at least in part on the industry in which the entity operates and/or the products and/or services offered by the entity. The output data may also include a probability distribution of loss, which may represent a graph showing probabilities that a given loss amount will occur for the entity. The output data may also include one or more confidence values associated with other portions of the output data. By so doing, the broker system may generate data that indicates how likely a given entity is to be involved in defending against an intellectual-property claim and what the potential loss to the entity would be in the event that such as claim was levied against the entity.

“The information described above may also be analyzed to determine a ranking of entities. For example, the broker system may score some or all of the entity information, otherwise described herein as characteristics, based at least in part on one or more machine learning models and/or utilizing heuristics. By way of example, each score may be on a given scale, such as from -5 to +5, with +5 representing a highest possible score for a given characteristic. For example, the industry associated with the entity may be scored, the revenue associated with the entity may be scored, the number of intellectual-property lawsuits that the entity has been involved in may be scored, the types of insurance policies held by the entity may be scored, and/or the monetary amount paid to the insurer may be scored. Some or all of the scores may be weighted based at least in part on, for example, the industry associated with the entity and/or prior rankings performed by the broker system. The scores and/or weighted scores may be aggregated to generate a prioritization value for a given entity. The prioritization value may be analyzed in association with one or more other prioritization values associated with other entities to generate a ranking of the entities. By so doing, the broker system may determine which entities are most likely to acquire and/or benefit from an insurance policy insuring against intellectual-property claims.”

The claims supplied by the inventors are:

“1. A system comprising: one or more processors; and non-transitory computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving, from at least one of an insurer system or an entity device, first entity data indicating first characteristics associated with a first entity; generating, utilizing a predictive model and based at least in part on the first entity data, a joint probability value indicating a likelihood and expected monetary amount of loss associated with the first entity being involved in defending a claim of intellectual-property infringement; determining, based at least in part on the first entity data, a loss-probability distribution associated with the claim of intellectual-property infringement, the loss-probability distribution indicating a severity of the claim of intellectual-property infringement and a frequency associated with the claim of intellectual-property infringement, wherein the joint probability value is based at least in part on the loss-probability distribution; generating, based at least in part on the first entity data and utilizing a predictive model trained utilizing feedback data indicating acceptance of insurance policies by other entities, a first prioritization value associated with the first entity, the first prioritization value indicating a likelihood that the first entity will acquire an insurance policy insuring against the claim of intellectual-property infringement; receiving, from the insurer system, second entity data indicating second characteristics associated with a second entity; generating, based at least in part on the second entity data and utilizing the predictive model, a second prioritization value associated with the second entity; generating, based at least in part on the first prioritization value and the second prioritization value, a ranking indicating that the first entity is more prioritized than the second entity; and generating a recommendation including the joint probability value and the ranking.

“2. The system of claim 1, wherein the first entity data includes an industry identifier associated with the first entity, a revenue associated with the first entity, an indication of intellectual-property litigation history, and a number of employees of the first entity.

“3. The system of claim 1, wherein the first characteristics include an industry identifier associated with the first entity, a number of patent-infringement lawsuits the first entity has been involved in, a revenue associated with the first entity, an indication of one or more other insurance policies held by the first entity, and a monetary amount provided by the first entity to an insurer associated with the insurer system, and the operations further comprise: determining, based at least in part on the first entity data, a score associated with individual ones of the first characteristics; determining, based at least in part on the industry identifier, a weighted score for at least a portion of the first characteristics; and wherein generating the first prioritization value comprises generating the first prioritization value based at least in part on the weighted score for the at least the portion of the first characteristics.

“4. The system of claim 1, the operations further comprising: identifying one or more terms associated with the insurance policy; generating, based at least in part on the loss-probability distribution and the one or more terms, a cost recommendation indicating a recommended cost to the first entity for the insurance policy; and wherein the recommendation includes the cost recommendation.

“5. A method comprising: receiving first entity data indicating first characteristics associated with a first entity; generating, based at least in part on the first entity data and utilizing a predictive model trained utilizing feedback data indicating acceptance of insurance policies by other entities, a first prioritization value associated with the first entity, the first prioritization value indicating a likelihood that the first entity will acquire an insurance policy insuring against a claim of intellectual-property infringement; determining, based at least in part on the first entity data, a loss-probability distribution associated with the claim of intellectual-property infringement, the loss-probability distribution indicating a severity of the claim of intellectual-property infringement and a frequency associated with the claim of intellectual-property infringement; generating a joint probability value indicating a likelihood and expected monetary amount of loss associated with the first entity being involved in defending the claim of intellectual-property infringement based, the joint probability value based at least in part on the loss-probability distribution; receiving second entity data indicating second characteristics associated with a second entity; generating, based at least in part on the second entity data and utilizing the predictive model, a second prioritization value associated with the second entity; generating, based at least in part on the first prioritization value and the second prioritization value, a ranking indicating that the first entity is more prioritized than the second entity; and generating a recommendation including the ranking and the joint probability value.

“6. The method of claim 5, the operations further comprising: determining, based at least in part on the first entity data, a score associated with individual ones of the first characteristics; determining, based at least in part on an industry identifier associated with the first entity, a weighted score for at least a portion of the first characteristics; and wherein generating the first prioritization value comprises generating the first prioritization value based at least in part on the weighted score for the at least the portion of the first characteristics.

“7. The method of claim 5, wherein the first entity data includes: an industry identifier associated with the first entity; a revenue associated with the first entity; an indication of intellectual-property litigation history; and a number of employees of the first entity.

“8. The method of claim 5, further comprising: receiving information indicating intellectual property associated with the first entity; determining a degree of coverage of the intellectual property with respect to an industry associated with the first entity; and wherein generating the recommendation comprises generating the recommendation based at least in part on the degree of coverage.

“9. The method of claim 5, further comprising: storing data corresponding to one or more prior rankings that are unassociated with the first prioritization value and the second prioritization value; and wherein generating the ranking comprises generating the ranking based at least in part on the one or more prior rankings.

“10. The method of claim 5, wherein the claim of intellectual-property infringement is associated with a first country, and the operations further comprise: identifying a second country in which intellectual-property infringement may be asserted against the first entity; determining, utilizing a predictive model, a weighting value associated with litigation in the second country; and wherein generating the recommendation comprises generating the recommendation based at least in part on the weighting value.

“11. A system, comprising: one or more processors; and non-transitory computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving first entity data indicating first characteristics associated with a first entity; generating, based at least in part on the first entity data and utilizing a predictive model trained utilizing feedback data indicating acceptance of insurance policies by other entities, a first prioritization value associated with the first entity, the first prioritization value indicating a likelihood that the first entity will acquire an insurance policy insuring against a claim of intellectual-property infringement; determining, based at least in part on the first entity data, a loss-probability distribution associated with the claim of intellectual-property infringement, the loss-probability distribution indicating a severity of the claim of intellectual-property infringement and a frequency associated with the claim of intellectual-property infringement; generating a joint probability value indicating a likelihood and expected monetary amount of loss associated with the first entity being involved in defending the claim of intellectual-property infringement based, the joint probability value based at least in part on the loss-probability distribution; receiving second entity data indicating second characteristics associated with a second entity; generating, based at least in part on the second entity data and utilizing the predictive model, a second prioritization value associated with the second entity; generating, based at least in part on the first prioritization value and the second prioritization value, a ranking indicating that the first entity is more prioritized than the second entity; and generating a recommendation including the ranking and the joint probability value.”

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For more information, see this patent: Chmielewski, Nicholas Joseph. Entity prioritization and analysis systems. U.S. Patent Number 11188985, filed August 16, 2019, and published online on November 30, 2021. 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=11188985.PN.&OS=PN/11188985RS=PN/11188985

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