Systems and methods for automated generation of new content responses to answer user queries are provided. The systems and methods for automated generation of new content responses answer user queries utilizing deep learning and a reasoning algorithm. The generated response is composed of new content and is not merely cut or copied information from one or more search results. Accordingly, the systems and methods for automated generation of new content responses provide tailored query specific answers that can be long and detailed including several sentences of information or that can be short and concise, such as \u201cyes\u201d or \u201cno.\u201d The ability of the systems and methods described herein to create or generate new content in response to a user query improves the usability, improves the performance, and\/or improves user interactions of\/with a search query system.<\/p>\n","protected":false},"excerpt":{"rendered":"
Systems and methods for automated generation of new content responses to answer user queries are provided. The systems and methods for automated generation of new content responses answer user queries utilizing deep learning and a reasoning algorithm. The generated response is composed of new content and is not merely cut or copied information from one […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13555],"msr-publication-type":[193724],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-649755","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2018-6-7","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"Google Patents","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/patents.google.com\/patent\/US20180157747A1\/en","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Saurabh Kumar Tiwary","user_id":0,"rest_url":false},{"type":"text","value":"Mir Rosenberg","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Jianfeng Gao","user_id":32246,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Jianfeng Gao"},{"type":"text","value":"Xia Song","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Rangan Majumder","user_id":38931,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Rangan Majumder"},{"type":"text","value":"Li Deng","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[],"msr_project":[691494,649749],"publication":[],"video":[],"download":[],"msr_publication_type":"miscellaneous","related_content":{"projects":[{"ID":691494,"post_title":"Project Turing","post_name":"project-turing","post_type":"msr-project","post_date":"2020-09-13 20:41:57","post_modified":"2021-11-01 18:05:54","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-turing\/","post_excerpt":"A deep learning initiative inside Microsoft to build the best-in-class models for use by Microsoft and power AI applications across entire Microsoft product family (Word, PowerPoint, Office, Dynamics, etc.) and make them available for use through Azure.","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/691494"}]}},{"ID":649749,"post_title":"AI at Scale","post_name":"ai-at-scale","post_type":"msr-project","post_date":"2020-05-19 08:01:11","post_modified":"2024-09-09 08:40:22","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/ai-at-scale\/","post_excerpt":"AI at Scale is an applied research initiative that works to evolve Microsoft products with the adoption of deep learning for both natural language text and image processing. 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