Bottomline Technologies Limited

Bottomline Technologies (de) Inc.

Sean Glerum

Brian Amend

Jessica Moran

Melissa Kutsch

William Cashman

Sandhya S Pillalamarri

Melissa Mikulski

Andrew Scarborough

Phillip Malone

Richard Gagne

Michelle Cloutier

Disclosed are a system and a method for communicating with a financial institution over a communication network to manage payments of payee initiated by a sender. The system includes a database for storing a plurality of modules, a central server for monitoring and updating the plurality of modules, a processing unit that processes the plurality of modules. The plurality of modules includes a notification module, a payee module, a dashboard module, a refunds module, a payment process module, and a remittance summary module. The notification module allows the sender to send a payment summary to each payee. The payee module allows the payee to upload the payee's details, to select a mode of payment to receive the payment. The payment process module processes the payment as per the mode selected by the payee in the payee module, further processes the payment via cheque mode if the payee fails to update the mode of payment in a pre-defined duration. The remittance summary module displays a summary of the payments made by the sender to the payee.

System for communicating with a financial institution to manage disbursements over a communication network

A novel technique for matching, merging, and combining a new database record with a master database record is described herein. The technique uses specific fields to compare in a unique exact match and fuzzy match combination to determine if a non-exact matching record pair is indeed a matched pair. Once the match is established, the records are merged or combined to utilize the best information from the two records.

Database record combination

A computer-implemented method for matching user inputted text to stored text. The user inputted text is compared to each of the text strings stored in a database using a string similarity score determined using a Levenshtein distance algorithm, the n-gram or trigram methods, the Jaro-Winkler algorithm, the Cosine similarity algorithm, the Hamming distance algorithm, the Damerau-Levenshtein distance algorithm, or similar. For each comparison, the string similarity score is analyzed to determine exact matches, non-matches, and probable matches. Probable matches are further analyzed using a keyboard distance algorithm to differentiate between matches and non-matches.

Identifying non-exactly matching text

Disclosed is a system and a method for communicating with a financial institution over a communication network to manage payments of payee initiated by a sender. The system includes a database for storing plurality of modules, a central server for monitoring and updating the plurality of modules, a processing unit processes the plurality of modules. The plurality of modules includes a notification module, a payee module and a payment process module. The notification module allows the sender to send a payment summary to each payee. The payee module allows the payee to upload payee's details in the central server, to select a mode of payment to receive the payment, to input banking details of the financial institution. The payment process module processes the payment as per the mode selected by the payee in the payee module, further processes the payment via cheque mode if the payee fails to update the mode of payment in a pre-defined duration.

A computer-implemented method for matching user inputted text to stored text. The user inputted text is compared to each of the text strings stored in a database using a Levenshtein distance algorithm. For each comparison, the Levenshtein distance is analyzed to determine exact matches, non-matches, and probable matches. Probable matches are further analyzed using a keyboard distance algorithm to differentiate between matches and non-matches.

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Portsmouth, NH, United States of America

Sean Glerum