公开(公告)号：US11704591B2

公开(公告)日：2023-07-18

申请号：US16353076

申请日：2019-03-14

Applicant: Adobe Inc.

Inventor： Sunny Dhamnani ,  Dhruv Singal ,  Ritwik Sinha

IPC: G06N20/00 ,  G06N5/025 ,  G06F16/901 ,  G06F18/243 ,  G06F18/2115 ,  G06N7/01

CPC classification number: G06N20/00 ,  G06F16/9014 ,  G06F18/2115 ,  G06F18/24323 ,  G06N5/025 ,  G06N7/01

Abstract: An IDS generator determines multiple classes for electronic data items. The IDS generator determines, for each class, a class-specific candidate ruleset. The IDS generator performs a differential analysis of each class-specific candidate ruleset. The differential analysis is based on differences between result values of a scoring objective function. In some cases, the differential analysis determines at least one of the differences based on additional data structures, such as an augmented frequent-pattern tree. A probability function based on the differences is compared to a threshold probability At least one testing ruleset is modified based on the comparison. The IDS generator determines, for each class, a class-specific optimized ruleset based on the differential analysis of each class-specific candidate ruleset. The IDS generator creates an optimized interpretable decision set based on combined class-specific optimized rulesets for the multiple classes.

公开(公告)号：US11068285B2

公开(公告)日：2021-07-20

申请号：US16576310

申请日：2019-09-19

Applicant: Adobe Inc.

Inventor： Nikhil Sheoran ,  Nayan Raju Vysyaraju ,  Varun Srivastava ,  Nisheeth Golakiya ,  Dhruv Singal ,  Deepali Jain ,  Atanu Sinha

IPC: G06F9/451 ,  G06K9/62 ,  G06F11/34 ,  G06N20/00 ,  G06F3/048

Abstract: In some embodiments, interaction data associated with user interactions with a user interface of an interactive computing environment is identified, and goal clusters of the interaction data are computed based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, likelihood values of additional sequences of user interactions falling within the goal clusters are computed based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Computing interface experience metrics of the additional sequences are computed using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform. The interface experience metrics are usable for changing arrangements of interface elements to improve the interface experience metrics.

公开(公告)号：US10929438B2

公开(公告)日：2021-02-23

申请号：US16008601

申请日：2018-06-14

Applicant: Adobe Inc.

Inventor： Ritwik Sinha ,  Pranav Ravindra Maneriker ,  Dhruv Singal ,  Atanu R. Sinha

IPC: G06F7/00 ,  G06F16/28 ,  G06F16/25 ,  G06F16/2457

Abstract: Systems and techniques for identifying segments in categorical data include receiving multiple transaction ID (TID) lists with univariate values that satisfy a thresholding metric with each TID list representing an occurrence of a single attribute in a set of transactions. The TID lists are stored with the univariate values that satisfy the thresholding metric in a data structure. In a loop, candidate itemsets to form from combinations of TID lists are determined using only the combinations of TID lists that satisfy categorical constraints. In the loop, for the candidate itemsets that satisfy categorical constraints, both the thresholding metric and a similarity metric are applied to the candidate itemsets. Final itemsets are formed from only the candidate itemsets that satisfy both the thresholding metric and the similarity metric.

公开(公告)号：US20190384853A1

公开(公告)日：2019-12-19

申请号：US16008601

申请日：2018-06-14

Applicant: Adobe Inc.

Inventor： Ritwik Sinha ,  Pranav Ravindra Maneriker ,  Dhruv Singal ,  Atanu R. Sinha

IPC: G06F17/30

Abstract: Systems and techniques for identifying segments in categorical data include receiving multiple transaction ID (TID) lists with univariate values that satisfy a thresholding metric with each TID list representing an occurrence of a single attribute in a set of transactions. The TID lists are stored with the univariate values that satisfy the thresholding metric in a data structure. In a loop, candidate itemsets to form from combinations of TID lists are determined using only the combinations of TID lists that satisfy categorical constraints. In the loop, for the candidate itemsets that satisfy categorical constraints, both the thresholding metric and a similarity metric are applied to the candidate itemsets. Final itemsets are formed from only the candidate itemsets that satisfy both the thresholding metric and the similarity metric.

公开(公告)号：US20190180193A1

公开(公告)日：2019-06-13

申请号：US15837929

申请日：2017-12-11

Applicant: Adobe Inc.

Inventor： Ritwik Sinha ,  Virgil-Artimon Palanciuc ,  Pranav Ravindra Maneriker ,  Manish Dash ,  Tharun Mohandoss ,  Dhruv Singal

IPC: G06N5/04 ,  G06N3/04 ,  G06N3/08

Abstract: Various embodiments describe user segmentation. In an example, potential rules are generated by applying a frequency-based analysis to user interaction data points. Each of the potential rules includes a set of attributes of the user interaction data points and indicates that these data points belong to a segment of interest. An objective function is used to select an optimal set of rules from the potential rules for the segment of interest. The potential rules are used as variable inputs to the objective function and this function is optimized based on interpretability and accuracy parameters. Each rule from the optimal set is associated with a group of the segment of interest. The user interaction data points are segments into the groups by matching attributes of these data points with the rules.