Microsoft Technology Licensing, LLC

Andrew Donald Gordon

Cedric Fournet

Blair Brewster Dillaway

Moritz Y Becker

Advait Sarkar

Neil Toronto

Donald F Box

Bradford H Lovering

Vance Palmer Morrison

David E Langworthy

Jeffrey C Schlimmer

John David Doty

Thore Graepel

Gavin Mark Bierman

Claudio Vittorio Russo

Benjamin Edward Rampson

Benjamin Zorn

Jason Forrest Mackay

Donald Syme

Lucas Bordeaux

Simon Peyton Jones

Thore Kurt Hartwig Graepel

Carina Suzana Negreanu

John Herbert Martin Williams

Luca Cardelli

Giorgio Ghelli

Jonathon Forbes

Richard Matthew McCutchen

Brian Paul Slininger

Sruti Srinivasa Ragavan

Christopher G Kaler

Brian M Jones

Pushmeet Kohli

Brian LaMacchia

Christian Canton

John Michael Winn

Johnny S Campbell

Vu Minh Le

Sriram Kottarakurichi Rajamani

Jonathan A Forbes

Christopher E Miles

Andrew James Becker

Elnaz Nouri

Joseph John McDaid

Christopher John Gross

Filip Andrzej Radlinski

William Wagner

Margaret Novotny

Jeffrey James Duzak

Yoram Bachrach

Aditya Nori

Shraddha Govind Barke

Sameer Singh

Christian Leopold Bejamin Poelitz

Nicolas Philippe Marie Rolland

Eric Johannes Borgstrom

Jacob Miller

Yelisaveta Pesenson

Johannes Borgstroem

Aryn Shelander

Karthikeyan Bhargavan

Sheena Stevens

Tomasz Pierre Blanc

Don Syme

Anusha Iyer

The indirect querying of models to determine capabilities possessed by the model. Such indirect queries take the form of model input that potentially includes a natural language input user data. Such model input is structured such that the output of the model is either not natural language at all, or else is natural language that is not semantically responsive to the natural language input. Nevertheless, the output is evaluated to estimate or determine the capability possessed by the model. Thus, models may be more fully utilized to their better potential.

Model capability extraction

The generation of a response to a task prompt that represents a task to perform on declarative code. The response is generated with the aid of a language model that was trained on imperative code. The declarative code includes declarations about data. A task prompt represents a task to perform on the declarative code. At least a portion of the declarative code and at least a portion of the task prompt are converted into input imperative code. The input imperative code is then caused to be provided as input to the language model, resulting in the language model generating output imperative code. At least a portion of the output imperative code is then converted into a response to the task prompt.

Responding to task prompt on declarative code using language model

A system for provenance aware editing for propagating edits back to spreadsheets is provided. The system receives an indication of an operation to be performed at an output location. The operation indicates one or more input locations on a spreadsheet that each provides an input value. The input values are accessed and tagged with their corresponding input location on the spreadsheet. Based on the operation and the input value from each of the one or more input locations, the system derives and displays the result at the output location. A user edit to the result is received at the output location, which causes transmission of a propagation request to change the input value at one or more of the tagged input locations. Using where-provenance associated with the tagged input locations, the system propagates the user edit to one or more of the tagged input locations.

Provenance aware editing for spreadsheets

A spreadsheet application displays a spreadsheet of cells in tabular form. User input is received specifying an input range of cells of the spreadsheet, a destination range of cells of the spreadsheet and a local modification. A memory stores a representation of the spreadsheet indicating formatting, values and formulas. An update of the representation is computed by applying the local modifications to the representation. Values of cells of the input range of the spreadsheet are calculated by using the updated representation and the calculated values are displayed at the destination range.

Spreadsheet with reuse functionality

Checking and/or completing for data grids is described such as for grids having rows and columns of cells at least some of which contain data values such as numbers or categories. In various embodiments predictive probability distributions are obtained from an inference engine for one or more of the cells and the predictive probability distributions are used for various tasks such as to suggest values to complete blank cells, highlight cells having outlying values, identify potential errors, suggest corrections to potential errors, identify similarities between cells, identify differences between cells, cluster rows of the data grid, and other tasks. In various embodiments a graphical user interface displays a data grid and provides facilities for completing, error checking/correcting, and analyzing data in the data grid.

Checking and/or completion for data grids

Systems and methods for inferring user traits based on indirect questions. Indirect questions may be generated based on one or more triggers. The answers to the indirect questions provide cues to a system as to whether a user has one or more attributes associated with a trait. This information may be used to personalize a computing device.

Inferring cues for use with digital assistant

To prevent spreadsheet errors, a solution is disclosed that provides a multiple-region spreadsheet including additional representations, providing readily usable creation and editing of computational structure operations, without altering the conventional grid representation of a spreadsheet or its formula syntax. The multiple-region spreadsheet may include a traditional row-column arrangement of cells in a grid format, along with a calculation view pane intended to display underlying formulas used in the grid along with truncated cell assignments.

Spreadsheet cell calculation view providing multiple-representation editing

Technology is disclosed herein for handing approximate (or uncertain) values in spreadsheet applications. More specifically, the technology describes spreadsheet applications that support arrays or sets of approximate (or uncertain) values as native entities. An approximate (or uncertain) value may be the value of a cell of the spreadsheet that is resolvable by formula, charts and other functionalities. In some implementations, approximate values may include a range of data and a probability distribution that can be automatically created by the spreadsheet application, generated based on context, input by the user, etc. Because the approximate (or uncertain) value is natively available, it can be incorporated in a spreadsheet like other values. Additionally, the approximate (or uncertain) values can automatically propagate through a spreadsheet calculation to obtain a final approximate result. The techniques also describe various ways to represent uncertain values in cells and to visualize the probability distribution.

Natively handling approximate values in spreadsheet applications

Methods for generation and execution of elastic sheet-defined functions and arrays are performed by systems, devices, and apparatuses. Elastic sheet-defined functions are generated from inelastic defined functions. Specific notations for tile ranges and range references of inputs for an inelastic function are generated, and heights, widths, rows, and columns of the inputs are modified with delta variables. Constraints for the delta variables are generated and mapped to solve for a constraints solution for a given inelastic function. Based on the constraints solution, an elastic sheet-defined function is generated that takes dynamic input ranges of different sizes. Elastic sheet-defined functions are executed to prevent overlap of tiles and ranges for variables by generating new sheets for tiles and ranges, dynamically moving tiles and ranges, and assigning cells as arrays. Software programming arrays are also dynamically sized in a similar manner.

System and method for generation and execution of elastic sheet-defined functions and arrays

Examples discussed herein relate to creating and handling lambdas in spreadsheet application, e.g., Microsoft Excel&#xae;. As discussed herein, lambdas are user defined functions (UDFs) that are natively creatable and maintainable through the use of a lambda registry in the spreadsheet application. In an implementation, a method of handling lambdas in a spreadsheet application is disclosed. The method includes detecting that a cell of a spreadsheet is populated with an expression that defines a lambda function and registering the lambda function with a lambda registry corresponding to the spreadsheet. The registration includes recording a home cell that identifies a location of the cell of the spreadsheet where the lambda function is defined. The method further includes replacing the expression that defines the lambda function with a reference to the registered lambda function.

Creating and handling lambda functions in spreadsheet applications

New methods of relational database management are described, for example, to enable completion and checking of data in relational databases, including completion of missing foreign key values, to facilitate understanding of data in relational databases, to highlight data that it would be useful to add to a relational database and for other applications. In various embodiments, the schema of a relational database is used to automatically create a probabilistic graphical model that has a structure related to the schema. For example, nodes representing individual rows are linked to rows of other tables according to the database schema. In examples, data in the relational database is used to carry out inference using inference algorithms derived from the probabilistic graphical model. In various examples, inference results, comprising probability distributions each for an individual table cell, are used to fill missing data, highlight errors, and for other purposes.

Relational database management

Database access is described, for example, where data in a database is accessed by an inference engine. In various examples, the inference engine executes inference algorithms to access data from the database and carry out inference using the data. In examples the inference algorithms are compiled from a schema of the database which is annotated with expressions of probability distributions over data in the database. In various examples the schema of the database is modified by adding one or more latent columns or latent tables to the schema for storing data to be inferred by the inference engine. In examples the expressions are compositional so, for example, an expression annotating a column of a database table may be used as part of an expression annotating another column of the database.

Database access

Security language constructs may be translated into logic language constructs and vice versa. Logic resolution may be effected using, for example, the logic language constructs. In an example implementation, translation of a security language assertion into at least one logic language rule is described. In another example implementation, translation of a proof graph reflecting a logic language into a proof graph reflecting a security language is described. In yet another example implementation, evaluation of a logic language program using a deterministic algorithm is described.

Security language translations with logic resolution

There is provided a method and system for modeling a data generating process. The method includes generating a dyadic Bayesian model including a pair of probabilistic functions representing a prior distribution and a sampling distribution, and modeling a data generating process based on the dyadic Bayesian model using observed data. The method includes generating a learner object for the dyadic Bayesian model. The method further includes training the dyadic Bayesian model with the learner object based on the observed data to produce a trained dyadic Bayesian model. The method also includes generating a posterior distribution over parameters based on the trained dyadic Bayesian model. The method also further includes generating a posterior predictive distribution based on the posterior distribution. The method also includes predicting an outcome of observable variables based on the posterior predictive distribution.

Modeling a data generating process using dyadic Bayesian models

An efficient, logical and expressive type system is provided for flexibly defining types of a declarative programming language so that efficient and structurally compact data intensive applications can be generated. The type system supports the combination of refinement types and type membership expressions, as well as a top type that encompasses all valid values as members. With the type system, types can be expressed as intersections and/or unions of other types. Thus, types can be efficiently represented for a full range of types of data as may be found in real world data stores.

Type system for declarative data scripting language

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Cambridge, United Kingdom

Andrew Donald Gordon