Aon Risk Services, Inc. of Maryland

Moat Metrics, Inc.

Daniel Crouse

Lewis C Lee

Samuel Cameron Fleming

David Craig Andrews

John E Bradley, Iii

Nicholas Joseph Chmielewski

Derek Charles Lietz

William Michael Edmund

Poh C Chua

Lewis Brendan Lee

Samuel John Desmond

Vinayak Thiagarajan

Iulia A Neagu

Rohitasva Dutta

Benjamin A Keim

Paul Kim

Jeffrey Scott Dwoskin

Peter Gerard Cross

Brian Hinman

Giles Humphrey Ffolliott Harlow

Martyn Frank Chattey

Systems and methods for digital property authentication and management are disclosed. A document representing a trade secret may be requested to be registered with a trade secret registry. A document obfuscation value corresponding to the document may be generated and may be registered with a blockchain. A record of the registration may be generated for the trade secret registry. The registry may be searchable and/or offer functionality such as valuation, insurance provision, and/or verification, among other benefits and functionalities.

Digital property authentication and management system

Systems and methods for intellectual property model parameter training and utilization are disclosed. For example, intellectual property assets are analyzed quickly and based on parsed data from multiple disparate datasets to generate quality scores. The quality scores are then utilized for multiple purposes, including determining financing amount indications, insurability indications, feedback loops, benchmarking, entity rating, and other purposes.

Intellectual property model parameter training and utilization

Systems and methods for intellectual property (IP) asset protection are disclosed. For example, by analyzing characteristic information associated with a user, along with feedback from one or more potential insurers, the system may determine various terms of an insurance policy for protecting the user against claims of IP infringement. The policy may provide for financial reimbursement for costs incurred while taking active measures to mitigate losses and/or defend against such claims. In addition, the system may analyze the characteristic information to identify various users having a low exposure of an infringement claim being asserted. The identified users may be actively targeted to acquire an IP protection policy.

Systems for generation of liability protection policies

Systems and methods for digital property protection are disclosed. For example, indication may be received that an entity has applied for an insurance policy to insure against theft of digital property. The digital property may be registered and a valuation of the digital property may be generated. Due diligence processes may be performed, including assessment of physical and/or network security mechanisms to prevent theft of the digital property. A recommendation to issue the insurance policy may be generated and sent in examples where the due diligence processes return favorable results. Terms generation and/or identification may be performed, and claims processing based at least in part on loss matrixes may also be performed.

Digital property protection systems

Techniques described herein are directed to analyzing intellectual-property data according to provide various intellectual property related services to organizations. In particular implementations, information related to products and/or services may be obtained from a number of data sources. Additionally, information related to intellectual-property assets, such as patents, trademarks, copyrights, trade secrets, and know-how, may be obtained. In various situations, the intellectual-property assets may be mapped to respective products and/or services. The mappings between the products and/or services and intellectual-property assets may be used to provide intellectual property related services that correspond to the intellectual-property assets, such as valuation services, strategy-related services, or risk-related services.

Analysis of intellectual-property data in relation to products and services

Manual human processing of documents often generates results that are subjective and include human-error. The cost and relatively slow speed of manual, human analysis makes it effectively impossible or impracticable to perform document analysis at the scale, speed, and cost desired in many industries. Accordingly, it may be advantageous to employ objective, accurate rule-based techniques to evaluate and process documents. This application discloses data processing equipment and methods specially adapted for a specific application: analysis of the breadth of documents. The processing may include context-dependent pre-processing of documents and sub-portions of the documents. The sub-portions may be analyzed based on word count and commonality of words in the respective sub-portions. The equipment and methods disclosed herein improve upon other automated techniques to provide document processing by achieving a result that quantitatively improves upon manual, human processing.

Automated document analysis for varying natural languages

This disclosure describes, in part, techniques for performing automatic document analysis. For instance, one or more computing devices may obtain a plurality of documents for analysis. The one or more computing devices may then analyze the documents to determine a respective comparative breadth score representing a breadth of each document, a respective comparative portion count score representing a number of documents portions included in each document, and a respective comparative differentiation score representing differentiation between the documents portions included in each document. In some instances, the one or more computing devices determine each of the scores based on comparing the respective breadth, respective number of document portions, and respective differentiation for each of the documents under analysis. The one or more computing devices can then determine a comprehensive score for each document using the respective scores, and generate a user interface that includes the overall scores.

Aggregating procedures for automatic document analysis

Systems and methods for automated digital-property analysis are disclosed. A document representing a digital property may be received and the document, or key elements and/or language characterizations representing the document, may be utilized to search one or more databases for similar documents. The search results may be ranked and displayed for review. If an indication that the search results do not correspond to the document is received, that indication may be utilized to promote registration of the document with a trade-secret registry, for provision of insurance policies, etc. Scheduled auditing may be established and utilized to confirm the digital property has not be disclosed and/or to determine a potential misappropriation event.

Systems for automated digital-property analysis

Systems and methods for digital property collateral protection are disclosed. For example, a pre-underwriting analysis may be performed to ascertain the quality of a given borrower and the intellectual property associated with the borrower. If the borrower passes the pre-underwriting analysis, an underwriting analysis including an intellectual property valuation may be performed. If the borrower passes the underwriting analysis and the terms of a loan and insurance policy are otherwise agreed upon, a lender may issue a loan to the borrower with the intellectual property as collateral, and an insurer may issue an insurance policy to the lender to insure against covered events.

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Seattle, WA, United States of America

Daniel Crouse