Patents by Inventor Premalatha Thangamani
Premalatha Thangamani has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11854055Abstract: This application relates to apparatus and methods for identifying anomalies within a time series. In some examples, a computing device receives sales data identifying a sale of at least one item, and aggregates the received data in a database. The computing device may generate a plurality of time series based on the aggregated sales data. The computing device may extract features from the plurality of time series, and generate an alerting algorithm that is based on clusters of the extracted features. The computing device may apply the alerting algorithm to a time series generated from received sales data to determine whether the time series is an anomaly. Based on the determination, the computing device may generate and transmit anomaly data identifying whether the time series is an anomaly, such as to another computing device.Type: GrantFiled: November 8, 2021Date of Patent: December 26, 2023Assignee: Walmart Apollo, LLCInventors: Lian Liu, Hui-Min Chen, Sangita Fatnani, Premalatha Thangamani
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Patent number: 11494775Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. In some examples, a computing device generates a decision matrix to identify fraudulent transactions. To generate the decision matrix, the computing device may determine scores for a plurality of transactions, and may determine transaction categories for each transaction based on the scores. The computing device may also determine a number of predictable features based on applying machine learning techniques to the transactions. A risk category is then determined for the number of predictable features. The computing device generates the decision matrix based on the transaction categories and the risk categories. In some examples, the computing device applies the generated decision matrix to an ongoing purchase transaction to determine if the ongoing purchase transaction is fraudulent.Type: GrantFiled: September 4, 2019Date of Patent: November 8, 2022Assignee: Walmart Apollo, LLCInventors: Linhong Kang, Kazuo Matsumoto, Premalatha Thangamani, Jing XiA, Saurabh Vivek Gagpalliwar
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Patent number: 11455638Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. In some examples, a computing device generates a decision matrix to identify fraudulent transactions. To generate the decision matrix, the computing device may determine scores for a plurality of transactions, and may determine transaction categories for each transaction based on the scores. The computing device may also determine a number of predictable features based on applying machine learning techniques to the transactions. A risk category is then determined for the number of predictable features. The computing device generates the decision matrix based on the transaction categories and the risk categories. In some examples, the computing device applies the generated decision matrix to an ongoing purchase transaction to determine if the ongoing purchase transaction is fraudulent.Type: GrantFiled: September 4, 2019Date of Patent: September 27, 2022Assignee: Walmart Apollo, LLCInventors: Linhong Kang, Kazuo Matsumoto, Premalatha Thangamani, Jing Xia, Saurabh Vivek Gagpalliwar
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Publication number: 20220058705Abstract: This application relates to apparatus and methods for identifying anomalies within a time series. In some examples, a computing device receives sales data identifying a sale of at least one item, and aggregates the received data in a database. The computing device may generate a plurality of time series based on the aggregated sales data. The computing device may extract features from the plurality of time series, and generate an alerting algorithm that is based on clusters of the extracted features. The computing device may apply the alerting algorithm to a time series generated from received sales data to determine whether the time series is an anomaly. Based on the determination, the computing device may generate and transmit anomaly data identifying whether the time series is an anomaly, such as to another computing device.Type: ApplicationFiled: November 8, 2021Publication date: February 24, 2022Inventors: Lian Liu, Hui-Min Chen, Sangita Fatnani, Premalatha Thangamani
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Patent number: 11200607Abstract: This application relates to apparatus and methods for identifying anomalies within a time series. In some examples, a computing device receives sales data identifying a sale of at least one item, and aggregates the received data in a database. The computing device may generate a plurality of time series based on the aggregated sales data. The computing device may extract features from the plurality of time series, and generate an alerting algorithm that is based on clusters of the extracted features. The computing device may apply the alerting algorithm to a time series generated from received sales data to determine whether the time series is an anomaly. Based on the determination, the computing device may generate and transmit anomaly data identifying whether the time series is an anomaly, such as to another computing device.Type: GrantFiled: January 28, 2019Date of Patent: December 14, 2021Assignee: Walmart Apollo, LLCInventors: Lian Liu, Hui-Min Chen, Sangita Fatnani, Premalatha Thangamani
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Publication number: 20210233082Abstract: An approach is disclosed for identifying fraudulent transactions. The approach receives transaction order data for a transaction order. The approach applies a fraud model to the received transaction order data and generates an initial score. The approach determines whether to tentatively accept the received transaction order based on the generated initial score being less than a first threshold value. The approach applies, in response to tentatively accepting the received transaction order, an incremental fraud model to the received transaction order data and generates a second score. The approach denies the received transaction order when the second score is greater than a second threshold value.Type: ApplicationFiled: January 28, 2020Publication date: July 29, 2021Inventors: Saurabh Vivek GAGPALLIWAR, Jing Xia, Premalatha THANGAMANI, Kirti PANDE, Suyog Ramesh CHOUDHARI
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Publication number: 20210065193Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. In some examples, a computing device generates a decision matrix to identify fraudulent transactions. To generate the decision matrix, the computing device may determine scores for a plurality of transactions, and may determine transaction categories for each transaction based on the scores. The computing device may also determine a number of predictable features based on applying machine learning techniques to the transactions. A risk category is then determined for the number of predictable features. The computing device generates the decision matrix based on the transaction categories and the risk categories. In some examples, the computing device applies the generated decision matrix to an ongoing purchase transaction to determine if the ongoing purchase transaction is fraudulent.Type: ApplicationFiled: September 4, 2019Publication date: March 4, 2021Inventors: Linhong KANG, Kazuo MATSUMOTO, Premalatha THANGAMANI, Jing XIA, Saurabh Vivek GAGPALLIWAR
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Publication number: 20210065192Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. In some examples, a computing device generates a decision matrix to identify fraudulent transactions. To generate the decision matrix, the computing device may determine scores for a plurality of transactions, and may determine transaction categories for each transaction based on the scores. The computing device may also determine a number of predictable features based on applying machine learning techniques to the transactions. A risk category is then determined for the number of predictable features. The computing device generates the decision matrix based on the transaction categories and the risk categories. In some examples, the computing device applies the generated decision matrix to an ongoing purchase transaction to determine if the ongoing purchase transaction is fraudulent.Type: ApplicationFiled: September 4, 2019Publication date: March 4, 2021Inventors: Linhong KANG, Kazuo MATSUMOTO, Premalatha THANGAMANI, Jing XIA, Saurabh Vivek GAGPALLIWAR
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Publication number: 20200242673Abstract: This application relates to apparatus and methods for identifying anomalies within a time series. In some examples, a computing device receives sales data identifying a sale of at least one item, and aggregates the received data in a database. The computing device may generate a plurality of time series based on the aggregated sales data. The computing device may extract features from the plurality of time series, and generate an alerting algorithm that is based on clusters of the extracted features. The computing device may apply the alerting algorithm to a time series generated from received sales data to determine whether the time series is an anomaly. Based on the determination, the computing device may generate and transmit anomaly data identifying whether the time series is an anomaly, such as to another computing device.Type: ApplicationFiled: January 28, 2019Publication date: July 30, 2020Inventors: Lian Liu, Hui-Min Chen, Sangita Fatnani, Premalatha Thangamani