公开(公告)号：US20190286978A1

公开(公告)日：2019-09-19

申请号：US15921369

申请日：2018-03-14

Applicant: Adobe Inc.

Inventor： Milan Aggarwal ,  Balaji Krishnamurthy ,  Shagun Sodhani

IPC: G06N3/08 ,  G06F17/30 ,  G06N7/00

Abstract: Systems and techniques map an input field from a data schema to a hierarchical standard data model (XDM). The XDM includes a tree of single XDM fields and each of the single XDM fields includes a composition of single level XDM fields. An input field from a data schema is processed by an unsupervised learning algorithm to obtain a sequence of vectors representing the input field and a sequence of vectors representing single level hierarchical standard data model (XDM) fields. These vectors are processed by a neural network to obtain a similarity score between the input field and each of the single level XDM fields. A probability of a match is determined using the similarity score between the input field and each of the single level XDM fields. The input field is mapped to the XDM field having the probability of the match with a highest score.

公开(公告)号：US20190188463A1

公开(公告)日：2019-06-20

申请号：US15843953

申请日：2017-12-15

Applicant: Adobe Inc.

Inventor： Shagun Sodhani ,  Kartikay Garg ,  Balaji Krishnamurthy

IPC: G06K9/00 ,  G06K9/62

CPC classification number: G06K9/00456 ,  G06K9/00463 ,  G06K9/6262 ,  G06K9/627 ,  G06K2209/01 ,  G06N3/0445 ,  G06N3/0481 ,  G06N3/084

Abstract: Techniques for determining reading order in a document. A current labeled text run (R1), RIGHT text run (R1) and DOWN text run (R3) are generated. The R1 labeled text run is processed by a first LSTM, the R2 labeled text run is processed by a second LSTM, and the R3 labeled text run is processed by a third LSTM, wherein each of the LSTMs generates a respective internal representation (R1′, R2′ and R3′). Deep learning tools other than LSTMs can be used, as will be appreciated. The respective internal representations R1′, R2′ and R3′ are concatenated or otherwise combined into a vector or tensor representation and provided to a classifier network that generates a predicted label for a next text run as RIGHT, DOWN or EOS in the reading order of the document.

公开(公告)号：US20190147369A1

公开(公告)日：2019-05-16

申请号：US15812991

申请日：2017-11-14

Applicant: Adobe Inc.

Inventor： Piyush Gupta ,  Sukriti Verma ,  Pratiksha Agarwal ,  Nikaash Puri ,  Balaji Krishnamurthy

IPC: G06N99/00 ,  G06N5/02

Abstract: Rule determination for black-box machine-learning models (BBMLMs) is described. These rules are determined by an interpretation system to describe operation of a BBMLM to associate inputs to the BBMLM with observed outputs of the BBMLM and without knowledge of the logic used in operation by the BBMLM to make these associations. To determine these rules, the interpretation system initially generates a proxy black-box model to imitate the behavior of the BBMLM based solely on data indicative of the inputs and observed outputs—since the logic actually used is not available to the system. The interpretation system generates rules describing the operation of the BBMLM by combining conditions—identified based on output of the proxy black-box model—using a genetic algorithm. These rules are output as if-then statements configured with an if-portion formed as a list of the conditions and a then-portion having an indication of the associated observed output.

公开(公告)号：US10268883B2

公开(公告)日：2019-04-23

申请号：US15674100

申请日：2017-08-10

Applicant: Adobe Inc.

Inventor： Mausoom Sarkar ,  Balaji Krishnamurthy

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/64 ,  G06K9/72 ,  G06T3/40 ,  G06T7/11 ,  G06T7/143

Abstract: A method and system for detecting and extracting accurate and precise structure in documents. A high-resolution image of documents is segmented into a set of tiles. Each tile is processed by a convolutional network and subsequently by a set of recurrent networks for each row and column. A global-lookup process is disclosed that allows “future” information required for accurate assessment by the recurrent neural networks to be considered. Utilization of high-resolution image allows for precise and accurate feature extraction while segmentation into tiles facilitates the tractable processing of the high-resolution image within reasonable computational resource bounds.

公开(公告)号：US20190114673A1

公开(公告)日：2019-04-18

申请号：US15787369

申请日：2017-10-18

Applicant: Adobe Inc.

Inventor： Piyush Gupta ,  Nikaash Puri ,  Balaji Krishnamurthy

IPC: G06Q30/02 ,  G06N7/00 ,  G06N5/02 ,  G06N3/02

Abstract: Digital experience targeting techniques are disclosed which serve digital experiences that have a high probability of conversion with regard to a given user visit profile. In some examples, a method may include predicting a probability of each digital experience in a campaign being served based on a user visit profile and an indication that a user exhibiting the user visit profile is going to convert, predicting a probability of each digital experience in the campaign being served based on the user visit profile and an indication that the user exhibiting the user visit profile is not going to convert, and deriving, for the user visit profile, a probability of conversion for each digital experience in the campaign. The probability of conversion for each digital experience in the campaign for the user visit profile may be derived using a Bayesian framework.