Database-documentation propagation via temporal log backtracking

    公开(公告)号:US11169979B2

    公开(公告)日:2021-11-09

    申请号:US16731399

    申请日:2019-12-31

    Applicant: INTUIT INC.

    Abstract: Aspects of the present disclosure provide techniques for database documentation propagation. Embodiments include scanning a log comprising a plurality of database queries to identify one or more database queries of the plurality of database queries, the one or more database queries being associated with generating a new table of a database based on information in an existing table of the database. Embodiments include generating, based on the one or more database queries identified during the scanning, a directed acyclic graph (DAG) comprising: a first vertex representing the existing table; a second vertex representing the new table; and a directed edge connecting the first vertex to the second vertex. Embodiments include obtaining documentation associated with the existing table. Embodiments include propagating, based on the DAG, at least a subset of the documentation associated with the existing table to the new table.

    USING SCENARIOS TO MITIGATE SELLER RISK TO ENTER ONLINE PLATFORMS

    公开(公告)号:US20210334868A1

    公开(公告)日:2021-10-28

    申请号:US16859604

    申请日:2020-04-27

    Applicant: Intuit Inc.

    Abstract: A method may include generating, using a flow proportionalized graph, scores for platform sellers of an online platform. The flow proportionalized graph may include nodes corresponding to the platform sellers and buyers, and edges each connecting a buyer node corresponding to a buyer initiating a monetary transfer and a platform seller node corresponding to a platform seller receiving the monetary transfer. Each edge may have a weight that is a proportion of total monetary transfers by the buyer received by the platform seller. The method may further include matching, using the scores and a seller similarity metric, a non-platform seller with a platform seller, receiving a scenario for the platform seller to sell an item of the non-platform seller via the online platform, and generating a prediction regarding an outcome of the scenario by applying a model to scenarios.

    DETECTING FRAUD BY CALCULATING EMAIL ADDRESS PREFIX MEAN KEYBOARD DISTANCES USING MACHINE LEARNING OPTIMIZATION

    公开(公告)号:US20210295179A1

    公开(公告)日:2021-09-23

    申请号:US16823642

    申请日:2020-03-19

    Applicant: Intuit Inc.

    Abstract: This disclosure relates to systems and methods for identifying fraudulent email addresses associated with an electronic payment service. In some implementations, a computing device receives an email with a prefix having a number of characters and characterized by a prefix length indicative of the number of characters in the prefix. The computing device identifies each of a number of bigrams is identified within the prefix, and determines a row and column distance for each bigram between two consecutive characters of the bigram as positioned on a keyboard. The computing device calculates a Euclidean distance between the two consecutive characters of the bigram based on the row and column distances, and determines a normalized distance based on the prefix length and an average of the Euclidean distances calculated for the number of bigrams in the prefix. The normalized distance is compared with a value to classify the email as suspicious or as not suspicious.

    IDENTIFYING CHECKSUM MECHANISMS USING LINEAR EQUATIONS

    公开(公告)号:US20210263996A1

    公开(公告)日:2021-08-26

    申请号:US17316822

    申请日:2021-05-11

    Applicant: INTUIT INC.

    Abstract: Certain aspects of the present disclosure provide techniques for detecting errors in account numbers. One example method generally includes receiving, from a user device, an entered number associated with a user and determining, based on a first portion of the entered number, an entity associated with the entered number. The method further includes obtaining, from an account number database, a plurality of account numbers associated with the entity and generating, from the plurality of account numbers, an account number matrix. The method further includes attempting to solve a multiplication equation of the account number matrix, wherein a solution of the multiplication equation is a vector of constants, upon determining a solution to the multiplication equation, determining whether the entered vector is a valid number for the entity and upon determining the entered vector is a valid number for the entity, storing the entered number in the account number database.

    USING MACHINE LEARNING TO DISCERN RELATIONSHIPS BETWEEN INDIVIDUALS FROM DIGITAL TRANSACTIONAL DATA

    公开(公告)号:US20210065245A1

    公开(公告)日:2021-03-04

    申请号:US16557958

    申请日:2019-08-30

    Applicant: Intuit Inc.

    Abstract: A method including receiving a data structure describing transactions between electronic user accounts associated with users. A relationship graph is constructed from the data in the data structure. The relationship graph has nodes representing entities described in the transactions. The relationship graph has edges representing connections between the nodes. The method also includes clustering groups of nodes within the nodes to form clusters among the nodes. The edges are labeled as relationships types. Labeling is performed by receiving, as input to a machine learning model, a vector having attributes representing the clusters, the nodes, and the edges. Labeling is also performed by outputting, from the machine learning model, probabilities. Each of the probabilities corresponds to a corresponding probability that an edge in the edges represents a relationship type between two nodes in the nodes. Labeling is also performed by labeling, based on the output, the edges as the relationship types.

    METHOD AND SYSTEM FOR REAL-TIME AUTOMATED IDENTIFICATION OF FRAUDULENT INVOICES

    公开(公告)号:US20210035119A1

    公开(公告)日:2021-02-04

    申请号:US16525228

    申请日:2019-07-29

    Applicant: Intuit Inc.

    Abstract: Known fraudulent invoice data, including defined and known fraudulent invoice feature data, is used to train a machine learning-based fraudulent invoice detection model to generate a fraudulent invoice score for invoices indicating a determined probability that a given invoice is fraudulent. The machine learning-based fraudulent invoice detection model is then used to generate a fraudulent invoice score for subsequent invoices before those invoices are paid by, and in some cases before the invoices are provided to, the parties being asked to pay the invoices. The fraudulent invoice score for the subsequent invoice is then used to determine if the subsequent invoice should be passed on to the parties being asked to pay the invoices for payment, or if one or more protective actions should be taken.

    METHOD AND SYSTEM FOR USING TEST DEPOSITS TO DETECT UNLISTED ACCOUNTS ASSOCIATED WITH USERS OF A DATA MANAGEMENT SYSTEM

    公开(公告)号:US20210027365A1

    公开(公告)日:2021-01-28

    申请号:US16522075

    申请日:2019-07-25

    Applicant: Intuit Inc.

    Abstract: Test deposit mechanisms used by financial institutions to link accounts are used to identify undisclosed accounts associated with users of a data management system. The potential existence of undisclosed accounts is determined based on the assumption that the presence of test deposit transactions in user account data is a strong indication that an undisclosed user account exists. Using this assumption, transaction data from user accounts disclosed to a user data management system is scanned to identify test deposit transactions listed in the transaction data. If test deposit transactions are identified, the user of the data management system is queried regarding the existence of the undisclosed user account. If the user confirms the existence of the undisclosed account, the formally undisclosed account is added to a set of disclosed user accounts with the data management system.

    Predicting locations based on transaction records

    公开(公告)号:US10678865B1

    公开(公告)日:2020-06-09

    申请号:US16012918

    申请日:2018-06-20

    Applicant: INTUIT INC.

    Abstract: Certain aspects of the present disclosure provide techniques for predicting a location based on transaction record data. An example technique includes obtaining a first set of transaction records and determining a merchant associated with each transaction record. The example further includes based on the merchant, determining and appending a branch identifier to each transaction record associated with the merchant to generate a first set of extended transaction records. The example further includes creating a consumption graph based on the first set of extended transaction records and determining an estimated location based on the consumption graph. The example further includes determining a precise point location based on the estimated location.

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