公开(公告)号：US11663493B2

公开(公告)日：2023-05-30

申请号：US16262208

申请日：2019-01-30

Applicant: Intuit Inc.

Inventor： Shashank Shashikant Rao ,  Sambarta Dasgupta ,  Colin Dillard

IPC: G06F16/20 ,  G06N3/126 ,  G06N5/04 ,  G06F16/28

CPC classification number: G06N3/126 ,  G06F16/285 ,  G06N5/04

Abstract: Forecasts are provided based on dynamic model selection for different sets of time series. A model comprises a transformation and a prediction algorithm. Given a time series, a transformation is selected for the time series and a prediction algorithm is selected to make a forecast based on the transformed time series. Sets of time series are distinguished from each other based on diverse sparsities, temporal scales and other time series attributes. A model is dynamically selected based on time series attributes to increase forecasting accuracy and decrease forecasting computation time. The dynamic model selection is based on the creation of a meta-model from historical sets of historical time series.

公开(公告)号：US20220180227A1

公开(公告)日：2022-06-09

申请号：US17334730

申请日：2021-05-29

Applicant: Intuit Inc.

Inventor： Sricharan Kallur Palli Kumar ,  Sambarta Dasgupta

IPC: G06N5/04 ,  G06N20/00 ,  G06Q10/04

Abstract: This disclosure relates to predictions based on a Bernoulli uncertainty characterization used in selecting between different prediction models. An example system is configured to perform operations including determining a prediction by a first prediction model. The first prediction model is associated with a loss function. The system is also configured to determine whether the prediction is associated with the first prediction model or a second prediction model based on a joint loss function. The second prediction model is associated with a likelihood function, and the joint loss function is based on the loss function and the likelihood function. The system is further configured to indicate the prediction to the user in response to determining that the prediction is associated with the first prediction model. If the prediction is associated with the second prediction model, the system may prevent indicating the prediction to the user.

公开(公告)号：US11810187B2

公开(公告)日：2023-11-07

申请号：US17862494

申请日：2022-07-12

Applicant: Intuit Inc.

Inventor： Sambarta Dasgupta ,  Sricharan Kallur Palli Kumar ,  Shashank Shashikant Rao ,  Colin R. Dillard

IPC: G06Q40/02 ,  G06F17/17 ,  G06N3/082 ,  G06N3/084 ,  G06F18/231 ,  G06F18/21

CPC classification number: G06Q40/02 ,  G06F17/17 ,  G06F18/217 ,  G06F18/231 ,  G06N3/082 ,  G06N3/084

Abstract: Systems and methods for forecasting cashflows across one or more accounts of a user disclosed. One example method may include retrieving a data set for each of a plurality of accounts from a database, constructing a graph including a plurality of nodes linked together by a multitude of edges, wherein each node identifies a time series value corresponding to one of the accounts, and each edge indicates a time series value of a corresponding set of transactions occurring between a corresponding pair of accounts, determining a plurality of constraints, determining a specified loss function based on the plurality of constraints, back-propagating a derivative of the specified loss function into a deep neural network (DNN) to determine a set of neural network parameters, forecasting, using the DNN, a time sequence for one or more of the nodes and one or more of the edges, and providing the forecasted time sequences to the user.

公开(公告)号：US11379726B2

公开(公告)日：2022-07-05

申请号：US16212601

申请日：2018-12-06

Applicant: INTUIT INC.

Inventor： Sambarta Dasgupta ,  Sricharan Kumar ,  Ashok Srivastava

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06N20/10

Abstract: Certain aspects of the present disclosure provide techniques for performing finite rank deep kernel learning. In one example, a method for performing finite rank deep kernel learning includes receiving a training dataset; forming a set of embeddings by subjecting the training data set to a deep neural network; forming, from the set of embeddings, a plurality of dot kernels; combining the plurality of dot kernels to form a composite kernel for a Gaussian process; receiving live data from an application; and predicting a plurality of values and a plurality of uncertainties associated with the plurality of values simultaneously using the composite kernel.

公开(公告)号：US11682069B2

公开(公告)日：2023-06-20

申请号：US16881172

申请日：2020-05-22

Applicant: INTUIT INC.

Inventor： Sricharan Kallur Palli Kumar ,  Sambarta Dasgupta ,  Sameeksha Khillan

IPC: G06N20/00 ,  G06N3/084 ,  G06N3/047 ,  G06Q40/02

CPC classification number: G06Q40/02 ,  G06N3/047 ,  G06N3/084 ,  G06N20/00

Abstract: In one embodiment a finite rank deep kernel learning method includes: receiving a training dataset; forming a plurality of training data subsets from the training dataset; for each respective training data subset of the plurality of training data subsets: calculating a subset-specific loss based on a loss function and the respective training data subset; and optimizing a model based on the subset-specific loss; determining a set of embeddings based on the optimized model; determining, based on the set of embeddings, a plurality of dot kernels; combining the plurality of dot kernels to form a composite kernel for a Gaussian process; receiving live data from an application; and predicting a plurality of values and a plurality of uncertainties associated with the plurality of values simultaneously using the composite kernel.

公开(公告)号：US11544753B2

公开(公告)日：2023-01-03

申请号：US17115097

申请日：2020-12-08

Applicant: Intuit Inc.

Inventor： Sambarta Dasgupta ,  Colin R. Dillard

IPC: G06Q30/04 ,  G06F16/22 ,  G06K9/62

Abstract: This disclosure relates to forecasting when and whether an invoice is to be paid and indicating such forecasts to a user. An example system is configured to perform operations including determining, by a classification model, a first confidence as to whether an invoice is to be paid, determining, by a regression model associated with the classification model, a first time associated with a second confidence as to when the invoice is likely to be paid, and indicating, to a user, whether the invoice is to be paid based on the first confidence and the first time when the invoice is likely to be paid based on the second confidence. The regression model may include one or more gradient boosted trees to determine the first time. Different times associated with different confidences can be determined by different gradient boosted trees, with the specific tree corresponding to the associated confidence.

公开(公告)号：US20220351002A1

公开(公告)日：2022-11-03

申请号：US17862494

申请日：2022-07-12

Applicant: Intuit Inc.

Inventor： Sambarta Dasgupta ,  Sricharan Kallur Palli Kumar ,  Shashank Shashikant Rao ,  Colin R. Dillard

IPC: G06K9/62 ,  G06F17/17 ,  G06N3/08

Abstract: Systems and methods for forecasting cashflows across one or more accounts of a user disclosed. One example method may include retrieving a data set for each of a plurality of accounts from a database, constructing a graph including a plurality of nodes linked together by a multitude of edges, wherein each node identifies a time series value corresponding to one of the accounts, and each edge indicates a time series value of a corresponding set of transactions occurring between a corresponding pair of accounts, determining a plurality of constraints, determining a specified loss function based on the plurality of constraints, back-propagating a derivative of the specified loss function into a deep neural network (DNN) to determine a set of neural network parameters, forecasting, using the DNN, a time sequence for one or more of the nodes and one or more of the edges, and providing the forecasted time sequences to the user.

公开(公告)号：US20220245526A1

公开(公告)日：2022-08-04

申请号：US17163018

申请日：2021-01-29

Applicant: Intuit Inc.

Inventor： Apoorva Banubakode ,  Sambarta Dasgupta

IPC: G06N20/20 ,  G06N7/00 ,  G06K9/62 ,  G06F16/904

Abstract: A server computer may receive and process a plurality of time series data to generate sparse datasets based on sparsity levels. The server computer applies a time series forecasting model to each respective subset of previous data points of the sparse datasets increasingly at the first time granularity to generate a set of prediction values and a set of residuals; applies a regression model to the set of the prediction residuals to generate a set of adjusted residuals for the sparse datasets; and generates a visualized explanation based on the set of the prediction values and the set of adjusted residuals for one or more of the sparse datasets.

公开(公告)号：US20220180413A1

公开(公告)日：2022-06-09

申请号：US17115097

申请日：2020-12-08

Applicant: Intuit Inc.

Inventor： Sambarta Dasgupta ,  Colin R. Dillard

IPC: G06Q30/04 ,  G06K9/62 ,  G06F16/22

Abstract: This disclosure relates to forecasting when and whether an invoice is to be paid and indicating such forecasts to a user. An example system is configured to perform operations including determining, by a classification model, a first confidence as to whether an invoice is to be paid, determining, by a regression model associated with the classification model, a first time associated with a second confidence as to when the invoice is likely to be paid, and indicating, to a user, whether the invoice is to be paid based on the first confidence and the first time when the invoice is likely to be paid based on the second confidence. The regression model may include one or more gradient boosted trees to determine the first time. Different times associated with different confidences can be determined by different gradient boosted trees, with the specific tree corresponding to the associated confidence.

公开(公告)号：US20220180232A1

公开(公告)日：2022-06-09

申请号：US17115297

申请日：2020-12-08

Applicant: Intuit Inc.

Inventor： Sambarta Dasgupta ,  Sricharan Kallur Palli Kumar

IPC: G06N7/00 ,  G06N20/00 ,  G06F16/25

Abstract: This disclosure relates to predictions based on a Bernoulli uncertainty characterization used in selecting between different prediction models. An example system is configured to perform operations including determining a prediction by a first prediction model. The first prediction model is associated with a loss function. The system is also configured to determine whether the prediction is associated with the first prediction model or a second prediction model based on a joint loss function. The second prediction model is associated with a likelihood function, and the joint loss function is based on the loss function and the likelihood function. The system is further configured to indicate the prediction to the user in response to determining that the prediction is associated with the first prediction model. If the prediction is associated with the second prediction model, the system may prevent indicating the prediction to the user.