Microsoft Technology Licensing, LLC

Jatin Kakkar

Jagan M Peri

Vipindeep Vangala

Perraju Bendapudi

Pankaj Khanzode

Jithendra K Veeramachaneni

Daraksha Parveen

Beethika Tripathi

Hari Krishnan S

Amit Kumar Yadav

Rashi Anand

Madan Gopal Jhanwar

Ranganath Kondapally

Priyam Bakliwal

Jimish Bhayani

Neha Singh

Vijay Amrit Agrawal

Rohit Kapoor

Gangula Rama Rohit Reddy

Rishabh Malhotra

Pragya Agrawal

A computing system obtains a document that includes text. The computing system identifies a fact referenced in the text of the document, where the fact includes a fact name and a fact value. The computing system determines a topic of the document based upon the text of the document. The computing system identifies a factoid stored in a data store based upon the topic and the fact name, where the factoid includes the fact name and a second fact value, and further where the factoid has been generated based upon second text of a second document. While the document is being displayed to a user, the computing system causes a message to be displayed to the user, where the message prompts the user to accept or reject replacement of the fact value in the document with the second fact value.

Computing system for detecting and updating stale information

An entity interlinkage platform may receive a corpus of enterprise documents and user profile data that corresponds to an individual user. The entity interlinkage platform utilizes topic extraction models to extract entities from the corpus and further utilizes the user profile data to generate a knowledge graph that includes interlinkages between the extracted entities. The entity interlinkage platform may identify a multitude of topic descriptions and corresponding topic terms from source documents that an access control list permits an individual user to access. Then, based on the user profile data, the entity interlinkage platform may generate a knowledge graph that is tailored for the individual user's specific purposes within the enterprise. For example, the knowledge graph may be generated based on knowledge graph preferences (which may be defined by the individual user) indicating preferred topic description types and/or levels of detail in association with specific topics and/or categories of topics.

Generating user-specific entity interlinkages of extracted enterprise topic descriptions

A multi-tenant, elastically scalable cache as a service is disclosed. Embodiments of the cache service eliminate the need for applications to manage their own cache tier. The multi-tenant cache service is implemented by maintaining/creating multiple named caches in a cache cluster and mapping each tenant's cache to a named cache in the cluster. Strict quotas are enforced on cache sizes This allows caches with different replication attributes to co-exist on the same cache server, allows migration of a cache from one cluster to another for load balancing purposes, and allows a cache to inflate/deflate to meet business needs. A network load balancer is used to route cache items to servers.

High-density multi-tenant distributed cache as a service

Gathering tenant usage data of server resources. A method includes a server in a cluster providing server resources for one or more tenants of the server. Data is stored in a local usage cache at the server. The data characterizes the resources provided to the one or more tenants of the server. At the server, data stored in the local usage cache is aggregated on a tenant basis, such that data is aggregated for given tenants. The aggregated data is sent to a distributed cache. At the server, aggregated data from other servers in the cluster is received from the distributed cache. The aggregated data from other servers in the cluster is globally aggregated and stored at an aggregated usage cache at the server in the globally aggregated form.

Near-real time distributed usage aggregation system

Growing community of inventors

Hyderabad Telangana, India

Jatin Kakkar