Salesforce Com, Inc.

Micron Technology Incorporated

Salesforces.com, Inc.

Prior Knowledge, Inc.

Eric Michael Jonas

Beau David Cronin

Cap Christian Petschulat

Jonathan Glidden

Fritz Heinrich Obermeyer

Vikash K Mansinghka

Paul Glendenning

Yao Fu

Tommy Tracy, Ii

Max Gasner

An apparatus includes a processing resource configured to receive a feature vector of a data stream. The feature vector includes a set of feature values. The processing resource is further configured to calculate a set of feature labels based at least in part on the set of feature values to generate a label vector, provide the label vector to another processing resource, and receive a plurality of classifications corresponding to each feature label of the label vector from the other processing resource. The plurality of classifications are generated based at least in part on a respective range of feature values of the set of feature values. The processing resource is configured to then combine the plurality of classifications to generate a final classification of the data stream.

Space efficient random decision forest models implementation utilizing automata processors

Techniques described herein may be used to solve a stochastic problem by dividing the stochastic problem into multiple fragments. In some cases, each fragment may be related to a random variable that forms a part of the problem, such that each fragment may produce samples from a probability distribution for that variable. Each fragment of the stochastic problem may then be assigned to a configurable circuit to solve the stochastic fragment. Configurable circuits may be implemented using any suitable combination of hardware and/or software, including using stochastic circuitry. In some embodiments, stochastic circuitry may include a stochastic tile and/or a stochastic memory.

Systems, methods, and apparatuses for solving stochastic problems using probability distribution samples

Disclosed herein are systems and methods for implementing a RELATED command with a predictive query interface including means for generating indices from a dataset of columns and rows, the indices representing probabilistic relationships between the rows and the columns of the dataset; storing the indices within a database of a host organization; exposing the database of the host organization via a request interface; receiving, at the request interface, a query for the database specifying a RELATED command term and a specified column as a parameter for the RELATED command term; querying the database using the RELATED command term and passing the specified column to generate a predictive record set; and returning the predictive record set responsive to the query, the predictive record set having a plurality of elements therein, each of the returned elements including a column identifier and a confidence indicator for the specified column passed with the RELATED command term, wherein the confidence indicator indicates whether a latent relationship exists between the specified column passed with the RELATED command and the column identifier returned for the respective element. Other related embodiments are further disclosed.

Systems, methods, and apparatuses for implementing a related command with a predictive query interface

An apparatus includes a processing resource configured to receive a feature vector of a data stream. The feature vector includes a set of feature values. The processing resource is further configured to calculate a set of feature labels based at least in part on the set of feature values to generate a label vector, provide the label vector to another processing resource, and to receive a plurality of classifications corresponding to each feature label of the label vector from the other processing resource. The plurality of classifications are generated based at least in part on a respective range of feature values of the set of feature values. The processing resource is configured to then combine the plurality of classifications to generate a final classification of the data stream.

Disclosed herein are systems and methods for populating a table having null values using a predictive query interface including means for receiving a tabular dataset from a user as input, the tabular dataset having data values organized as columns and rows; identifying a plurality of null values within the tabular dataset, the null values being dispersed across multiple rows and multiple columns of the tabular dataset; generating indices from the tabular dataset of columns and rows, the indices representing probabilistic relationships between the rows and the columns of the tabular dataset; displaying the tabular dataset as output to the user, the displayed output including the data values depicted as known values and the null values depicted as unknown values; receiving input from the user to populate at least a portion of the unknown values within the displayed tabular dataset with predicted values; querying the indices for the predicted values; and displaying the predicted values as updated output to the user. Other related embodiments are further disclosed.

Systems, methods, and apparatuses for populating a table having null values using a predictive query interface

Disclosed herein are systems and methods for implementing data upload, processing, and predictive query API exposure including means for receiving a dataset in a tabular form, the dataset having a plurality of rows and a plurality of columns; processing the dataset to generate indices representing probabilistic relationships between the rows and the columns of the dataset; storing the indices in a database; exposing an Application Programming Interface (API) to query the indices in the database; receiving a request for a predictive query or a latent structure query against the indices in the database; querying the database for a prediction based on the request via the API; and returning the prediction responsive to the request. Other related embodiments are further disclosed.

Systems, methods, and apparatuses for implementing data upload, processing, and predictive query API exposure

Disclosed herein are systems and methods for implementing a GROUP command with a predictive query interface including means for generating indices from a dataset of columns and rows, the indices representing probabilistic relationships between the rows and the columns of the dataset; storing the indices within a database of a host organization; exposing the database of the host organization via a request interface; receiving, at the request interface, a query for the database specifying a GROUP command term and a specified column as a parameter for the GROUP command term; querying the database using the GROUP command term and passing the specified column to generate a predictive record set; and returning the predictive record set responsive to the query, the predictive record set having a plurality of groups specified therein, each of the returned groups of the predictive record set including a group of one or more rows of the dataset. Other related embodiments are further disclosed.

Systems, methods, and apparatuses for implementing a group command with a predictive query interface

Disclosed herein are systems and methods for implementing a PREDICT command with a predictive query interface including means for generating indices from a dataset of columns and rows, the indices representing probabilistic relationships between the rows and the columns of the dataset; storing the indices within a database of a host organization; exposing the database of the host organization via a request interface; receiving, at the request interface, a query for the database specifying at least (i) a PREDICT command term, (ii) one or more specified columns to be predicted, and (iii) one or more column name=value pairs specifying column names to be fixed and the values by which to fix them; and querying the database using the PREDICT command term and passing the one or more specified columns to be predicted and the one or more column name=value pairs to generate a representation of a joint conditional distribution of the one or more specified columns to be predicted fixed according to the column name=value pairs using the indices stored in the database. Other related embodiments are further disclosed.

Systems, methods, and apparatuses for implementing a predict command with a predictive query interface

Disclosed herein are systems and methods for implementing a SIMILAR command with a predictive query interface including means for generating indices from a dataset of columns and rows, the indices representing probabilistic relationships between the rows and the columns of the dataset; storing the indices within a database of a host organization; exposing the database of the host organization via a request interface; receiving, at the request interface, a query for the database specifying a SIMILAR command term, a specified row as a parameter for the SIMILAR command term, and a specified column as a parameter for the SIMILAR command term; querying the database using the SIMILAR command term and passing the specified row and the specified column to generate a predictive record set; and returning the predictive record set responsive to the query, the predictive record set having a plurality of elements therein, each of the returned elements of the predictive record set including (i) a row identifier which corresponds to a row of the dataset assessed to be similar, according to a latent structure, to the specified row passed with the SIMILAR command term based on the specified column and (ii) a confidence indicator which indicates a likelihood of a latent relationship between the specified row passed with the SIMILAR command and the row identifier returned for the respective element. Other related embodiments are further disclosed.

Systems, methods, and apparatuses for implementing a similar command with a predictive query interface

Disclosed herein are systems and methods for implementing predictive query interface as a cloud service including means for exposing an interface to client devices operating remotely from a host organization, wherein the interface is accessible by the client devices via a public Internet; executing a predictive database at the host organization as an on-demand cloud based service for one or more subscribers; authenticating one of the client devices by verifying the client device is associated with one of the subscribers and based further on authentication credentials for the respective subscriber; receiving a request from the authenticated subscriber via the interface; executing a predictive query or a latent structure query against indices of the predictive database generated from a dataset of columns and rows on behalf of the authenticated subscriber, the indices representing probabilistic relationships between the rows and the columns of the dataset; and returning a predictive record set to the authenticated subscriber responsive to the request. Other related embodiments are further disclosed.

Growing community of inventors

San Francisco, CA, United States of America

Eric Michael Jonas