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1. WO2022051000 - GENERATING STRUCTURED DATA FOR RICH EXPERIENCES FROM UNSTRUCTURED DATA STREAMS

Publication Number WO/2022/051000
Publication Date 10.03.2022
International Application No. PCT/US2021/035062
International Filing Date 31.05.2021
IPC
G06F 16/34 2019.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
30of unstructured textual data
34Browsing; Visualisation therefor
G06F 16/958 2019.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
90Details of database functions independent of the retrieved data types
95Retrieval from the web
958Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
CPC
G06F 16/345
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
30of unstructured textual data
34Browsing; Visualisation therefor
345Summarisation for human users
G06F 16/951
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
90Details of database functions independent of the retrieved data types
95Retrieval from the web
951Indexing; Web crawling techniques
G06F 16/958
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
90Details of database functions independent of the retrieved data types
95Retrieval from the web
958Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
G06F 16/972
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
90Details of database functions independent of the retrieved data types
95Retrieval from the web
958Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
972Access to data in other repository systems, e.g. legacy data or dynamic Web page generation
G06F 3/0482
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
3Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
01Input arrangements or combined input and output arrangements for interaction between user and computer
048Interaction techniques based on graphical user interfaces [GUI]
0481based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
0482interaction with lists of selectable items, e.g. menus
G06F 40/295
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
40Handling natural language data
20Natural language analysis
279Recognition of textual entities
289Phrasal analysis, e.g. finite state techniques or chunking
295Named entity recognition
Applicants
  • MICROSOFT TECHNOLOGY LICENSING, LLC [US]/[US]
Inventors
  • TUPAKULA, Pranathi R.
  • SINGHAL, Aman
  • SRINIVASAN, Prithvishankar
  • DEBARROS, Marcelo M.
Agents
  • SWAIN, Cassandra T.
  • BARKER, Doug
  • CHATTERJEE, Aaron C.
  • CHEN, Wei-Chen Nicholas
  • CHOI, Daniel
  • CHURNA, Timothy
  • DINH, Phong
  • EVANS, Patrick
  • GABRYJELSKI, Henry
  • GUPTA, Anand
  • HWANG, William C.
  • JARDINE, John S.
  • LEE, Sunah
  • LEMMON, Marcus
  • MARQUIS, Thomas
  • MEYERS, Jessica
  • ROPER, Brandon
  • SPELLMAN, Steven
  • SULLIVAN, Kevin
  • WALKER, Matt
  • WIGHT, Stephen A.
  • WISDOM, Gregg
  • WONG, Ellen
  • WONG, Thomas S.
  • ZHANG, Hannah
  • AKHTER, Julia
  • KADOURA, Judy M.
  • NIU, Bo
  • OLANIRAN, Qudus
  • BROWN, Renee
  • TRAN, Kimberly
Priority Data
17/141,63405.01.2021US
63/073,79102.09.2020US
Publication Language English (en)
Filing Language English (EN)
Designated States
Title
(EN) GENERATING STRUCTURED DATA FOR RICH EXPERIENCES FROM UNSTRUCTURED DATA STREAMS
(FR) GÉNÉRATION DE DONNÉES STRUCTURÉES POUR DES EXPÉRIENCES RICHES À PARTIR DE FLUX DE DONNÉES NON STRUCTURÉES
Abstract
(EN) Aspects of the present disclosure are directed to providing a rich content experience based on information received from unstructured content. A plurality of information items may be obtained from a plurality of data source, where each information item includes unstructured content. The plurality of information items may be provided to a trained machine learning model, where the model is trained with training data that includes information items and corresponding labeled entities for a plurality of historical events. In examples, a formatted request may be received, where the formatted request is associated with one or more labeled entities associated with the trained machine learning model. The trained machine learning model may identify multiple entities from the unstructured content based on the formatted request associated with the one or more labeled entities. In examples, each identified entity of the multiple identified entities is stored as structured content responsive to the formatted request.
(FR) Des aspects de la présente divulgation concernent la fourniture d'une expérience de contenu riche sur la base d'informations reçues à partir d'un contenu non structuré. Une pluralité d'éléments d'informations peut être obtenue à partir d'une pluralité de sources de données, chaque élément d'informations comprenant un contenu non structuré. La pluralité d'éléments d'informations peut être fournie à un modèle d'apprentissage automatique entraîné, le modèle étant entraîné avec des données d'entraînement qui comprennent des éléments d'informations et des entités étiquetées correspondantes pour une pluralité d'événements historiques. Dans des exemples, une requête formatée peut être reçue, la requête formatée étant associée à une ou plusieurs entités étiquetées associées au modèle d'apprentissage automatique entraîné. Le modèle d'apprentissage automatique entraîné peut identifier de multiples entités à partir du contenu non structuré sur la base de la requête formatée associée à la ou aux entités étiquetées. Dans des exemples, chaque entité identifiée parmi les multiples entités identifiées est stockée sous la forme d'un contenu structuré en réponse à la demande formatée.
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