Microsoft Technology Licensing, LLC

Fortinet, Inc.

Pamela Bhattacharya

Charles Yin-Che Lee

Mayerber Loureiro De Carvalho Neto

Charles Yin-che Lee

Barun Patra

Yijou Chen

Todd D Newman

Michael Brian Palmer

Justin Brooks Cranshaw

Benjamin Gene Cheung

Warren David Johnson, Iii

Njall Skarphedinsson

Theodore M Reed

Chala Fekadu Fufa

Christopher A Meek

Jay Parikh

Ganesh Pandey

Úlfar Erlingsson

Oleksandr Polozov

Himesh Shah

Suri Raman

Alex James Boyd

Emad Mohamed Hamdy Elwany

Ben Cheung

Juliana Pena Ocampo

Vishwas Suryanarayanan

Timothy Chase

Daniel B Graves

Todd Daniel Newman

Guiding query creation for a generative artificial intelligence (AI)-enabled assistant, including: receiving, via a natural language interface for a security framework monitoring a cloud deployment, a natural language input comprising one or more entity identifiers; gathering information based on the one or more entity identifiers; providing, to a generative artificial intelligence (AI) model, a prompt based on the natural language input and comprising the gathered information; and receiving, from the generative AI model, a response to the prompt.

Guiding query creation for a generative artificial intelligence (AI)-enabled assistant

Methods, apparatuses, and products for providing a code analysis feedback loop for code created using generative AI, including: performing a code analysis on code generated by a generative AI model; determining, based on the code analysis, whether the code satisfies one or more conditions; responsive to the code failing to satisfy the one or more conditions, until the code satisfies the one or more conditions, repeatedly updating the code by receiving, from the generative AI model, updated code based on a previously performed code analysis; and providing the code generated by the generative AI model.

Code analysis feedback loop for code created using generative artificial intelligence ('AI')

Architecture for a generative artificial intelligence (AI)-enabled assistant, including: receiving, via a natural language interface for a security framework monitoring a cloud deployment, a natural language input; selecting, by a delegator generative artificial intelligence (AI) model and based on the natural language input, a particular generative AI model from a plurality of selectable generative AI models; prompting the particular generative AI model based on the natural language input; and providing, via the natural language interface and in response to the natural language input, a response from the particular generative AI model.

Architecture for a generative artificial intelligence (AI)-enabled assistant

Leveraging generative artificial intelligence ('AI') for securing a monitored deployment, including: receiving natural language input associated with the monitored deployment, the monitored deployment monitored by a monitoring tool; and receiving, from a generative AI application, a response to the natural language input, wherein: the generative AI application accesses publicly available information as well as data sources associated with the monitoring tool; and the response is generated based at least in part on information contained in the data sources associated with the monitoring tool.

Leveraging generative artificial intelligence ('AI') for securing a monitored deployment

A method and system for predicting an intended time interval for a content segment may include receiving a request for natural language processing (NLP) of the content segment, the content segment including one or more temporal expressions, accessing contextual data associated with each of the one or more temporal expressions, decoding the content segment into a program that describes a temporal logic of the content segment based on the one or more temporal expressions, evaluating the program using the contextual data to predict an intended time interval for the content segment, and providing the intended time interval as an output.

Intelligent interpretation of temporal expressions

In non-limiting examples of the present disclosure, systems, methods and devices for resolving temporal ambiguities are presented. A natural language input may be received. A temporal component of the input may be identified. A determination may be made that the temporal component includes a conjunction that separates temporal meeting block alternatives. A temporal ambiguity may be identified in one of the meeting block alternatives. A plurality of syntax tree permutations may be generated for the meeting block alternative where the ambiguity was identified. A machine learning model that has been trained to identify a most relevant permutation for a given natural language input may be applied to each of the plurality of permutations. A temporal meeting block alternative corresponding to the most relevant permutation may be surfaced.

Resolving temporal ambiguities in natural language inputs leveraging syntax tree permutations

The disclosure relates to systems and methods for identifying entities related to a task in a natural language input. An entity detection model is provided which receives a natural language input. The entity detection model processes the natural language input using an entity encoder and an input encoder. The entity encoder identifies and encodes relevant entities while the input encoder generates a contextual encoding which represents contextual information associated with a relevant entity. The encoded entity and contextual encodings may then be combined and processed to generate a probability score for an identified entity. A negation constraint model is also disclosed. The negation constraint model receives the natural language input and the identified entities. The natural language input is analyzed to identify negation cues and determine if the negation cue is associated with an identified entity.

Systems and methods for identifying entities and constraints in natural language input

A software agent, that is used to assist in providing a service, receives communications from a set of users that are attempting to use the software agent. The communications include communications that are interacting with the software agent, and communications that are not interacting with the software agent. The software agent performs natural language processing on all communications to identify such things as user sentiment, user concerns or other items in the content of the messages, and also to identify actions taken by the users in order to obtain a measure of user satisfaction with the software agent. One or more action signals are then generated based upon the identified user satisfaction with the software agent.

Signal analysis in a conversational scheduling assistant computing system

In non-limiting examples of the present disclosure, systems, methods and devices for assigning conference rooms are presented. A meeting request may be received by an electronic meeting service. Meeting fit scores may be calculated for the meeting request and one or more conference rooms. The meeting fit scores may be based on location, capacity, and/or audio-visual capabilities. The meeting request may be assigned to a conference room with a highest meeting fit score. A meeting request may be re-assigned to a different conference room based on a conference room becoming available that has a higher meeting fit score. A meeting request may be re-assigned to a different conference room based on characteristics of the meeting request being modified (e.g., fewer invitees, more invitees, different location specified), and thus, the meeting fit scores for conference rooms changing based on those modifications.

Electronic meeting analysis for automatic conference room assignment

In non-limiting examples of the present disclosure, systems, methods and devices for identifying relevant content in a natural language input are presented. An email may be received. A machine learning model may be applied to the email. The machine learning model may have been trained to rank sentences based on their relevance to a schedule meeting task. The machine learning model may comprise: an embedding layer for generating an embedding for each word in the email; a distinct sentence aggregation layer for aggregating the embeddings for each word in the email into a distinct embedding for each of the sentences in the email; a contextual aggregation layer for aggregating each distinct embedding for each of the sentences into a contextual embedding for each of the sentences; and a scoring layer for scoring and ranking each of the sentences based on their relevance to the schedule meeting task.

Artificial intelligence for identifying relevant content related to specific tasks

In non-limiting examples of the present disclosure, systems, methods and devices for prioritizing calendar events with artificial intelligence are presented. A request to schedule a new calendar event for a specified period may be received. A conflicting calendar event during the specified time period may be identified. An event priority score for the new calendar event may be compared with an event priority score for the conflicting calendar event, wherein the event priority scores are generated via application of a statistical machine learning model to a plurality of factors associated with the new calendar event and the conflicting calendar event. A selectable option to replace the conflicting calendar event with the new calendar event may be presented if the event priority score for the new calendar event is higher than the event priority score for the conflicting calendar event.

Artificial intelligence for calendar event conflict resolution

In non-limiting examples of the present disclosure, systems, methods and devices for assisting with scheduling a meeting are presented. A message comprising a plurality of sentences may be received. A hierarchical attention model may be utilized to identify a subset of sentences of the plurality of sentences that are relevant to a scheduling of the meeting. A subset of words in the subset of sentences that are potentially relevant to scheduling of the meeting may be identified based on relating to at least one meeting parameter. The subset of words may be split into a first group comprising words from the subset of words that are above a meeting relevance threshold value, and a second group comprising words from the subset of words that are below a meeting relevance threshold value. An automated action associated with scheduling the meeting may be caused to be performed.

Sentence attention modeling for event scheduling via artificial intelligence and digital assistants

In non-limiting examples of the present disclosure, systems, methods and devices for routing meeting requests by a digital assistant service are presented. A request to schedule a meeting between an invitee and a principal may be received by a digital assistant service, wherein the request is sent by an agent of the principal. The digital assistant service may determine that the agent is a delegate of the principal with scheduling authority. The digital assistant service may further determine that follow-up information for the meeting is required, and the digital assistant service may route an electronic message requesting the follow-up information directly the agent-delegate. Other aspects describe mechanisms for routing meeting requests from third parties directly to delegates, rather than sending those communications directly to principals.

Routing of meeting requests and follow-up queries by digital assistants

In non-limiting examples of the present disclosure, systems, methods and devices for matching user tone to digital assistant response types and tones while assisting with meeting scheduling are presented. An electronic message may be received by a digital assistant service. The digital assistant service may detect an intent to schedule a meeting and identify an urgency level associated with the message. The digital assistant may respond to the scheduling user with a message having a tone corresponding to the identified urgency level. The digital assistant may also perform a follow-up action for scheduling the meeting in a manner consistent with the urgency level of the scheduling user. For example, the digital assistant may attempt to schedule the meeting in a higher priority manner if there is a high urgency associated with the message, and a lower priority manner if there is a low urgency associated with the message.

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Redmond, WA, United States of America

Pamela Bhattacharya