Intuit, Inc.

Bei Huang

Nhung Ho

Meng Chen

Linxia Liao

Ngoc Nhung Ho

George Roumeliotis

Eva Diane Chang

Ngoc Nhung Thi Ho

Brian A Wilt

The invention relates to a method for mapping topics. The method includes obtaining terms, obtaining tokens from each term, and identifying a first and a second set of topics. Each of the topics represents one or more of the terms. The method further includes identifying first and second topic names for the first and the second sets of topics. For each topic, the tokens associated with the terms assigned to the topic are analyzed for relevance, and a token with a high relevance is selected as the topic name. The method also includes selecting one of the first and one of the second sets of topics to obtain first and second selected topics, determining, based on the one or more terms, a similarity value between each of the first and the second selected topics, and establishing a mapping between similar first and second selected topics.

Mapping of topics within a domain based on terms associated with the topics

A method and system identify assistance offerings that are likely to be relevant to users of a data management system. The method and system utilize a multivariate random forest regression machine learning process to train an assistance offerings recommendation model to recommend relevant assistance offerings to users of the data management system. The multivariate random forest regression machine learning process replaces zero values in the training set data with negative numbers to increase the accuracy of the machine learning process.

Method and system for recommending assistance offerings

A method including obtaining terms that are specific to a domain. First and second sets of the terms are obtained from first and second users. The first set do not adhere to a standard; the second terms do adhere to the standard. Tokens are obtained from the terms. First and second topics, representing terms, are identified within the domain. The terms are assigned to exactly one corresponding topic. The terms are assigned to the topics. First and second topic names are identified for the first and second topics. Identifying includes analyzing, for relevance, ones of the tokens. Identifying also includes selecting a particular token as a selected topic name for a selected one of the first topics and the second topics. A similarity value is determined between the first and the second selected topics. A mapping is established, based on the similarity value, between the first and second selected topic.

Method and system for recommending assistance offerings using multivariate random forest leveraging negative samples

The invention relates to a method for generating a topic name for accounts grouped under a topic. The method includes obtaining account names associated with the accounts, generating a plurality of n-grams from the account names, and for each n-gram, obtaining a quality score based on relevance and meaningfulness of the n-gram. The relevance is determined using at least one relevance score that reflects how representative the n-gram is for the plurality of n-grams, and the meaningfulness is determined based on whether the n-gram exists in a knowledge base. The method further includes assigning one or more of the n-grams to the topic as the topic name, based on the quality score generated for each of the n-grams.

Method and system for generating topic names for groups of terms

A processor may identify a combination term including at least two individual terms within at least one source of truth stored in a memory in communication with the processor. The processor may identify at least one document including the at least two of the individual search terms. The processor may determine a document weight for the at least one document based on the combination search term and the at least two of the individual search terms within the combination search term. The processor may provide the document as a search result arranged according to the document weight and/or may index the document according to the document weight.

Searching and scoring using phrases and/or multiple words

A method for fraud detection may include receiving, via a first user account of a business management application (BMA), a first loan application for a first business entity. The first user account may be accessible to an accountant of an accounting firm. The method may further include receiving, via a second user account of the BMA, a second loan application for a second business entity. The second user account may be accessible to the accountant. The method may further include determining, using a cluster analysis, (i) a connection strength between the first business entity and the second business entity relative to the accounting firm, and (ii) a fraud score for the accounting firm, and determining, based on the connection strength and the fraud score, a probability that the first loan application is fraudulent.

Accountant account takeover fraud detection

A method for recommendation model integration includes obtaining, using a first model, multiple text scores ranking multiple software applications according to a first user submitted terms in a source software application, and obtaining, using a second model, multiple feature scores ranking the software applications according to multiple features of the software applications. The method further includes integrating the text scores and the feature scores to generate multiple combined scores for the software applications, and presenting, to a user, the software applications according to the combined scores.

Software application selection models integration

Financial data is obtained from two or more types of financial management systems and analyzed to obtain potential entities data identifying potential entities and attributes associated with the potential entities. Duplicate potential entity data is then identified and eliminated to generate a master entity list. The financial data is also analyzed to identify potential commercial transaction data and one or more attributes associated with the commercial transaction data, including data indicating commercial transactions and the parties associated with the commercial transactions. The commercial transaction data is then analyzed using the master entity list to match entities listed in the master entity list with the parties associated with the commercial transactions. The matched entities are then substituted for the respectively matched parties associated with the commercial transactions to create a master commercial transaction list. The master entity list and the master commercial transaction list data is then stored.

Method and system for identifying entities and obtaining financial profile data for the entities using de-duplicated data from two or more types of financial management systems

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Mountain View, CA, United States of America

Bei Huang