Microsoft Technology Licensing, LLC

Tanuja Abhay Joshi

Joseph M Joy

Vibhuti Singh Sengar

Narendranath Datha Govindachetty

Vinay Krishnaswamy

Eric Joel Stollnitz

Aditya Sankar

Arumugam Kumaran

Narendranath Datha

Kanchen Rajanna

Udayan Khurana

Tobias W M Kellner

Sujith Radhakrishnan Warrier

A method described herein includes receiving a query that is configured by an issuer of the query to be utilized to retrieve a portion of local search content, wherein the local search content comprises a first dataset and a second data set, wherein the first dataset comprises a first entity and the second dataset comprises a second entity, and wherein the query comprises a first token and a second token. The method also includes parsing the query such that the first token of the query is mapped to the first entity in the first dataset and the second token of the query is mapped to the second entity in the second dataset. The method also includes returning search results based at least in part upon the parsing of the query.

Custom local search

'Experience Streams' (ESs) are used by a 'rich interactive narrative' (RIN) data model as basic building blocks that are combined in a variety of ways to enable or construct a large number of RIN scenarios for presenting interactive narratives to the user. In general various ES types contain all the information required to define and populate a particular RIN, as well as the information (in the form of a series of navigable states) that charts an animated and interactive course through each RIN. In other words, combinations of various ES provide a scripted path through a RIN environment, as well as various UI controls and/or toolbars that enable user interaction with the interactive narrative provided by each RIN. Example ESs include, but are not limited, content browser experience streams, zoomable media experience streams, relationship graph experience streams, player-controls/toolbar experience streams, etc.

Experience streams for rich interactive narratives

A cross-lingual location search uses a combination of translation and transliteration of query tokens to develop a set of candidate matches for further searching. A query is broken up into individual tokens (e.g. address parts) and a list of transliterations and/or translations for each token is developed. The translated and transliterated results are keyed against a spatial database using both literal database keys and transliterated database keys. Matches from the resulting searches are selected when a spatial overlap, or constraint, occurs among subsequences of the query tokens.

Cross lingual location search

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Tanuja Abhay Joshi