公开(公告)号：US20180285186A1

公开(公告)日：2018-10-04

申请号：US15638938

申请日：2017-06-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Patrice GODEFROID ,  Rishabh SINGH ,  Hila PELEG

IPC: G06F11/07 ,  H04L29/06 ,  G06N99/00 ,  G06F11/36

Abstract: Provided are methods and systems for automatically generating input grammars for grammar-based fuzzing by utilizing machine-learning techniques and sample inputs. Neural-network-based statistical learning techniques are used for the automatic generation of input grammars. Recurrent neural networks are used for learning a statistical input model that is also generative in that the model is used to generate new inputs based on the probability distribution of the learnt model.

公开(公告)号：US20250068837A1

公开(公告)日：2025-02-27

申请号：US18734203

申请日：2024-06-05

Applicant: Microsoft Technology Licensing, LLC

Inventor： Benjamin Goth ZORN ,  Marc Manuel Johannes BROCKSCHMIDT ,  Pallavi CHOUDHURY ,  Oleksandr POLOZOV ,  Rishabh SINGH ,  Saswat PADHI

IPC: G06F40/18 ,  G06F16/338 ,  G06N3/04 ,  G06N3/08 ,  G06N5/046

Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved table identification in a spreadsheet. One method including: receiving a spreadsheet including at least one table; identifying, using machine learning, one or more classes of a plurality of classes for each cell of the received spreadsheet, wherein the plurality of classes include corners and not-a-corner; and inducing at least one table in the received spreadsheet based on the one or more identified classes for each cell of the received spreadsheet.

公开(公告)号：US20200019603A1

公开(公告)日：2020-01-16

申请号：US16034447

申请日：2018-07-13

Applicant: Microsoft Technology Licensing, LLC

Inventor： Benjamin Goth ZORN ,  Marc Manuel Johannes BROCKSCHMIDT ,  Pallavi CHOUDHURY ,  Oleksandr POLOZOV ,  Rishabh SINGH ,  Saswat PADHI

IPC: G06F17/24 ,  G06N3/04 ,  G06N3/08 ,  G06N5/04 ,  G06F17/30

Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved table identification in a spreadsheet. One method including: receiving a spreadsheet including at least one table; identifying, using machine learning, one or more classes of a plurality of classes for each cell of the received spreadsheet, wherein the plurality of classes include corners and not-a-corner; and inducing at least one table in the received spreadsheet based on the one or more identified classes for each cell of the received spreadsheet.

公开(公告)号：US20180246915A1

公开(公告)日：2018-08-30

申请号：US15443531

申请日：2017-02-27

Applicant: Microsoft Technology Licensing, LLC

Inventor： Rishabh SINGH ,  Sumit GULWANI ,  Dana DRACHSLER COHEN

IPC: G06F17/30

CPC classification number: G06F16/221 ,  G06F16/25

Abstract: Techniques are disclosed which provide for transforming a hierarchical table to a relational table. A hierarchical table may be received, in which a headline row is identified. A candidate row may be determined in the hierarchical table. The process may include systematically classifying headlines as data headlines or descriptors. For each data headline a new column may be generated, while for each descriptor headline, the table may be split to produce a resultant table. The resultant table may be stored and the process may be repeated until there are no headlines left to be classified. The steps performed by the system to transform the table can then be displayed on a user device using a program in the Domain-specific language, which can then be further inspected or modified to perform the desired table transformation.

公开(公告)号：US20180232351A1

公开(公告)日：2018-08-16

申请号：US15633875

申请日：2017-06-27

Applicant: Microsoft Technology Licensing, LLC

Inventor： Rishabh SINGH ,  Jeevana Priya INALA

IPC: G06F17/24 ,  H04L29/12 ,  H04L29/08 ,  G06F17/30 ,  G06F3/0482

Abstract: Provided are methods and systems for joining semi-structured data from the web with relational data in a spreadsheet table using input-output examples. A first sub-task performed by the system learns a string transformation program to transform input rows of a table to URL strings that correspond to the webpages where the relevant data is present. A second sub-task learns a program in a rich web data extraction language to extract desired data from the webpage given the example extractions. Hierarchical search and input-driven ranking are used to efficiently learn the programs using few input-output examples. The learnt programs are then run on the remaining spreadsheet entries to join desired data from the corresponding web pages.