Google Inc.

Salesforce Com, Inc.

Salesforce, Inc.

Christian Posse

Pei-Chun Chen

Jennifer Su

Rohit Kapoor

Mario Sergio Rodriguez

Sarah Aerni

Alex Edelstein

Michael Weil

Till Christian Bergmann

Penny Tselikis

Mehmet Ezbiderli

Jason Teller

Xuejun Tao

Muhammad Tayyab Javed

Bruno Roustant

Francisco Borges

Guillaume Kempf

Arvind Srikantan

Ye Tian

Matthew Courtney

Seyed Reza Mir Ghaderi

Zhao Zhang

Methods, systems, apparatuses, devices, and computer program products are described. A system may identify, from an event log including log entries for a tenant of a multi-tenant database system, a pattern of log entries corresponding to main actions and satisfying a frequency threshold. The system may identify log entries associated with the pattern and corresponding to the main actions, detailed actions, or both. The system may retrieve data corresponding to a history field of a data object associated with the pattern and may determine at least a portion of a process flow for the data object according to the pattern and based on the log entries and the historical data. The process flow may include operations to perform using the data object. In some cases, the system may transmit, to a user device, an indication of the portion of the process flow for user review and implementation.

Automatic determination of alternative paths for a process flow using machine learning

Methods, systems, apparatuses, devices, and computer program products are described. An intelligent routing system may route a data object to a path in a process flow using a model, such as a machine-learned model. The system may receive a first data object and may route the first data object along a path of the process flow using a random routing procedure, for example, for model training. The routing may involve performing operations based on the path and the features of the first data object. The system may update one or more models based on an outcome of the operations. Following training, the system may insert a model into the process flow at a decision point between paths. The system may receive a second data object and may route the second data object to a path using the model and based on features of the second data object.

Intelligent routing of data objects between paths using machine learning

Methods, apparatus, systems, and computer-readable media are provided for increasing dimensionality of data structures associated with filling positions. In some implementations, a prediction of desired experience for a given position to be filled may be incorporated into a searchable field of the data structure. Among other things, increasing the dimensionality of the data structure may facilitate more granular searching of positions and guided creation of new positions to be filled. In some implementations, a predicted desired experience may be used to notify a user posting a new position whether a specified desired experience corresponds to a predicted desired experience.

Increasing dimensionality of data structures

Methods, apparatus, systems, and computer-readable media are provided for increasing dimensionality of data structures associated with filling positions. In some implementations, a prediction of desired experience for a given position to be filled may be used to increase the dimensionality of a searchable data structure that represents the given position. For example, the predicted desired experience may be incorporated into a searchable field of the data structure. Among other things, increasing the dimensionality of the data structure may facilitate more granular searching of positions and guided creation of new positions to be filled. In some implementations, a predicted desired experience may be used to notify a user posting a new position whether a specified desired experience corresponds to a predicted desired experience.

A computer system is configured to receive a plurality of previous user selections by a user of previous database entries, each of which has as plurality of database field. The computer system is configured to determine weights for the various database fields included in the previous user selections and rank subsequent search results for a subsequent search of the database based on the determined weights, where the one or more weights affect a ranking of a search result based on a match associated with the particular database field. The computer system is further configured to receive customized search result layout settings specifying that one or more specified database fields are displayed to the user when the search results are displayed, where one or more weights for the particular database field are based on the customized search result layout settings.

Ranking results of searches of databases

In disclosed techniques, a computing system causes presentation of a user interface having an input field operable to receive, from a user, a search query for a database. The computing system may classify the search query by: determining whether the search query includes terms that are within a specified vocabulary indicative of a natural language query and determining whether the search query includes terms that identify an object defined in a schema of the database. In response to classifying the search query as a natural language query, the computing system returns query results determined by identifying values in the database corresponding to the object defined in the schema. In response to classifying the search query as a keyword query, the computing system returns query results determined by comparing terms of the search query to values within records in the database.

Classifying different query types

A computer system is configured to receive a plurality of previous user selection by a user of previous database entries, each of which has as plurality of database field. The computer system is configured to analyze the plurality of previous user selections to identify how frequently the same values are included in the various previous database entries. The computer system is configured to determine weights for the various database fields and rank subsequent search results for a subsequent search of the database based on the determined weights.

The present disclosure provides systems and methods that improve job posting structure and presentation by, for example, classifying portions of job postings into informative sections. As an example, given a job posting, a computing system implementing aspects of the present disclosure can separate the job posting into multiple portions. After separation into portions, the computing system can classify each portion into the most plausible job-posting-specific section. For example, the computing system can include and implement a machine-learned classification model to classify the portions into the sections. Following classification, the computing system can modify the job posting based on the classification of the portions. In particular, the structure and/or presentation of the job posting can be improved based on the classification of the portions into the sections.

Systems and methods to improve job posting structure and presentation

A system associates sequentially descending weights with items in a reference list. The system identifies overlapping items from the first position to the second position in both the reference list and a test list. The system determines a weighted overlap for the overlapping items by combining the weights for each overlapping item to create a reference item weight, combining the weights for each item from the first position to the second position in the reference list to create a reference position weight, and determining the ratio between the reference item weight and the reference position weight. The system identifies overlapping items from the first position to the third position in both the reference list and the test list, and then determines a weighted overlap for these overlapping items. The system outputs the average weighted overlap as an efficiency evaluation of the information retrieval system that created the test list.

Asymmetric rank-biased overlap

The present disclosure is directed to generating search results, recommendations, and/or the like. In particular, the methods and systems of the present disclosure can: receive data describing one or more aspects of an entity; determine, based at least in part on the aspect(s) of the entity, one or more additional aspects of the entity; determine, based at least in part on the aspect(s) of the entity and the additional aspect(s) of the entity, a metric indicating a reputational prominence of the entity relative to multiple other entities of similar type; generate, based on the metric, one or more search results, recommendations, and/or the like; and communicate, to a computing device (e.g., for display, and/or the like), data indicating the search result(s), recommendation(s), and/or the like.

Methods and systems for generating search results and recommendations based on multi-sourced two-way correspondence and relative entity prominence

The present disclosure is directed to generating search results, recommendations, and/or the like. In particular, the methods and systems of the present disclosure can: receive data describing one or more aspects of entities of different types; determine, based at least in part on the aspect(s) of the entities, one or more additional aspects of the entities; determine, based at least in part on the additional aspect(s) of the entities, a metric indicating an extent to which the entities correspond to one another; generate, based on the metric, one or more search results, recommendations, and/or the like; and communicate, to a computing device (e.g., for display, and/or the like), data indicating the search result(s), recommendation(s), and/or the like.

Methods, systems, and apparatus, including computer programs encoded on storage devices, for performing a job opportunity search. In one aspect, a system includes a data processing apparatus, and a computer-readable storage device having stored thereon instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations. The operations include defining a vector vocabulary, defining an occupation taxonomy that includes multiple different occupations, obtaining multiple labeled training data items, wherein each labeled training data item is associated with at least (i) a job title, and (ii) an occupation, generating, for each of the respective labeled training data items, an occupation vector that includes a feature weight for each respective term in the vector vocabulary, and associating each respective occupation vector with an occupation in the occupation taxonomy based on the occupation of the labeled training data item used to generate the occupation vector.

Search engine

Systems and methods for autosuggesting related objects to a user are provided. In one embodiment, a method includes receiving data indicative of a user input. The method includes identifying one or more ontologies based, at least in part, on the user input. Each ontology is associated with a category that is related to the user input. Each ontology includes a plurality of object types, each object type including one or more terms. The method includes determining one or more suggested related objects based, at least in part, on the user input and one or more of the plurality of object types. The one or more suggested related objects include one or more of the terms that are related to the user input. The method includes providing data indicative of the suggested related objects for display on a user interface via a display device.

Growing community of inventors

Belmont, CA, United States of America

Christian Posse