Reputation.com, Inc.

Thomas Gerardo Dignan

Brent Michael Franson

Jeffrey James Piper

James Andrew Saxe

Bradley William Null

Michael Benjamin Selkowe Fertik

Noah Benjamin Lang

John Andrew Rehling

Tony C Scott

Shrey A Bhatia

Sherman Wang

Cheng-chao Yang

Nhan Van Phan

Michael Benjamine Selkowe Fertik

Therani Madhusudan

Enterprise reputation evaluation techniques are disclosed. Reputation data extracted from each of a plurality of data sources is received. The reputation data is associated with a plurality of physical locations of an enterprise entity. An aggregate reputation score is determined for the entity using at least a portion of the received data. The aggregate reputation score is provided as output.

Enterprise reputation evaluation

Recommending reviewers from which to request reviews is disclosed. A list of potential reviewers is received. A determination is made that at least one potential reviewer included in the list of potential reviewers should be targeted with a request to review an entity. The transmission of a review request to the potential reviewer is facilitated.

Reviewer recommendation

Determining themes is disclosed. Reputation data extracted from at least one data source is received. The reputation data includes a plurality of user-authored reviews. The presence of a first keyword is detected in a first review. The presence of a second keyword that is different from but associated with the first keyword is detected in a second review. A sentiment for a theme is determined based on the detected presence of the first and second keywords. A report that indicates the sentiment for the theme is provided as output.

Determining themes

Review data associated with a plurality of entities is received. Each of the entities is associated with a particular industry and the review data comprises review data obtained from a plurality of review sites. From at least a portion of the received review data, a review benchmark for the industry is determined. The benchmark is provided as output.

Industry review benchmarking

The transmission of a review request to a potential author of a review on a review site is facilitated. A determination is made that the potential reviewer has not, subsequent to the transmission, authored a review on the review site. A follow-up action to take with the potential reviewer regarding the review request is determined.

Follow-up determination

The generation of an online reputation report for an individual is disclosed. First, an online reputation assessment is performed for an individual. Then, a report is generated. The report includes an individualized recommendation based on the online reputation assessment. The recommendation can be directed to the individual and can also be directed to an entity other than the individual.

Reputation report with recommendation

An online reputation assessment of an individual is performed. A reputation score is determined based on the performed online reputation assessment. The reputation score is provided as output. In some cases, the reputation score is provided to an entity that is not the individual, for example, based on the receipt of an authorization from the individual to disclose the score to the entity.

Reputation report with score

A user is solicited to provide a review for a business, while the user is located on a premise of the business. Review data is received from the user. The review data is transmitted to a reputation platform configured to evaluate the received review data. If the review data indicates a positive review, a review request action is commenced. If the review data indicates a negative review, a remedial action is commenced.

Stimulating reviews at a point of sale

Presented are systems and methods for identifying information about a particular entity including acquiring electronic documents having unstructured text, that are selected based on one or more search terms from a plurality of terms related to the particular entity. Tokenizing the acquired documents to form a data matrix and then calculating a plurality of eigenvectors, using the data matrix and the transpose of the data matrix. The variance is then acquired for determining the amount of intra-clustering between the documents and then the acquired documents are clustered using some of the eigenvectors and the variance.

Identifying information related to a particular entity from electronic sources, using dimensional reduction and quantum clustering

Presented are systems and methods for creating a set of signatures including acquiring a data set and converting the data set into a plurality of data matrices. The system determines a prime number and determines a plurality of primitive roots to the prime number. The system calculates a template matrix using a first and second primitive root, of the plurality of primitive roots, and selects a data matrix property of interest. The system calculates a first hash function for each of the data matrices to create a first signature for each data matrix such that a main set of signatures is formed, wherein the first hash function is calculated using said property of interest, the prime number, and the template matrix. The system generates a main set of signatures based on the first hash functions, wherein the main set of signatures comprises a first signature for each data matrix.

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Mountain View, CA, United States of America

Thomas Gerardo Dignan