公开(公告)号：US20160117359A1

公开(公告)日：2016-04-28

申请号：US14525385

申请日：2014-10-28

Applicant: salesforce.com, inc.

Inventor： Arun Jagota

IPC: G06F17/30 ,  H04L12/58

CPC classification number: H04L51/22 ,  G06F17/30663 ,  G06F17/30684 ,  H04L51/28

Abstract: Identifying entities in email signature blocks is described. A system scores each token, in a sequence of tokens from an email signature block, based on entity types, wherein each token is a word, a punctuation symbol, or an end-of-line character. The system identifies each entity sequence which includes a number of entities that matches the number of tokens in the sequence of tokens. The system identifies an entity sequence with a highest score based on applying scores for each token in the sequence of tokens to each identified entity sequence. The system outputs the sequence of tokens as an identified set of entities based on the entity sequence with the highest score.

Abstract translation: 描述电子邮件签名块中的实体。 系统根据实体类型对来自电子邮件签名块的一系列令牌对每个令牌进行分值，其中每个令牌是一个单词，一个标点符号或一个行尾字符。 该系统识别每个实体序列，其包括与令牌序列中的令牌数量匹配的多个实体。 该系统基于将令牌序列中的每个令牌的分数应用于每个识别的实体序列来识别具有最高分数的实体序列。 该系统基于具有最高分数的实体序列将令牌序列作为确定的一组实体输出。

公开(公告)号：US10354264B2

公开(公告)日：2019-07-16

申请号：US14486111

申请日：2014-09-15

Applicant: salesforce.com, inc.

Inventor： Arun Jagota ,  Gregory Haardt ,  Govardana Sachithanandam Ramachandran ,  Lei Ming ,  Matthew Fuchs ,  George Vitchev ,  Fang Wong

IPC: G06Q30/00 ,  G06Q30/02 ,  G06Q10/06 ,  G06Q40/00 ,  G06Q10/10 ,  G06Q30/06

Abstract: Contact recommendations based on purchase history are described. A system creates a directed graph of nodes in which at least some of the nodes are connected by directed arcs, wherein a directed arc from a first node to a second node represents a conditional probability that previous users who purchased a first contact also purchased a second contact. The system identifies a set of contacts purchased by a current user. The system estimates a prospective purchase probability based on a historical probability that previous users purchased a specific contact and a related probability that previous users who purchased the specific contact also purchased a contact in the set of contacts, for each candidate contact. The system outputs a recommendation for the current user to purchase a recommended candidate contact based on a corresponding prospective purchase probability.

公开(公告)号：US10163056B2

公开(公告)日：2018-12-25

申请号：US15162505

申请日：2016-05-23

Applicant: salesforce.com, inc.

Inventor： Matthew Fuchs ,  Arun Jagota

IPC: G06N5/02 ,  G06N7/00 ,  G06N99/00 ,  G06F17/30

Abstract: The technology disclosed relates to methods for partitioning sets of features for a Bayesian classifier, finding a data partition that makes the classification process faster and more accurate, while discovering and taking into account feature dependence among sets of features in the data set. It relates to computing class entropy scores for a class label across all tuples that share the feature-subset and arranging the tuples in order of non-decreasing entropy scores for the class label, and constructing a data partition that offers the highest improvement in predictive accuracy for the data set. Also disclosed is a method for partitioning a complete set of records of features in a batch computation, computing increasing predictive power; and also relates to starting with singleton partitions, and using an iterative process to construct a data partition that offers the highest improvement in predictive accuracy for the data set.

公开(公告)号：US09349101B2

公开(公告)日：2016-05-24

申请号：US14473717

申请日：2014-08-29

Applicant: salesforce.com, inc.

Inventor： Matthew D. Fuchs ,  Arun Jagota

IPC: G06N7/00 ,  G06N99/00

CPC classification number: G06N5/02 ,  G06F17/30292 ,  G06N7/005 ,  G06N99/005

Abstract: The technology disclosed relates to methods for partitioning sets of features for a Bayesian classifier, finding a data partition that makes the classification process faster and more accurate, while discovering and taking into account feature dependence among sets of features in the data set. It relates to computing class entropy scores for a class label across all tuples that share the feature-subset and arranging the tuples in order of non-decreasing entropy scores for the class label, and constructing a data partition that offers the highest improvement in predictive accuracy for the data set. Also disclosed is a method for partitioning a complete set of records of features in a batch computation, computing increasing predictive power; and also relates to starting with singleton partitions, and using an iterative process to construct a data partition that offers the highest improvement in predictive accuracy for the data set.

Abstract translation: 所公开的技术涉及用于分配贝叶斯分类器的特征集合的方法，找到使分类处理更快更准确的数据分区，同时发现并考虑数据集中的特征集合之间的特征依赖性。 它涉及跨共享特征子集的所有元组的类标签的计算类熵分数，并且按照类标签的非递减熵分数的顺序排列元组，以及构建提供预测精度最高改进的数据分区 用于数据集。 还公开了一种用于分割批量计算中的特征记录的完整集合的方法，计算增加的​​预测能力; 并且还涉及从单例分区开始，并且使用迭代过程构造提供数据集的预测精度最高改进的数据分区。

公开(公告)号：US09740743B2

公开(公告)日：2017-08-22

申请号：US14518145

申请日：2014-10-20

Applicant: salesforce.com, inc.

Inventor： Arun Jagota ,  Chenghung Ker ,  Parth Vijay Vaishnav ,  Danil Dvinov ,  David Hacker ,  Susan Levine

IPC: G06F17/30

CPC classification number: G06F17/30489

Abstract: Matching objects using keys based on match rules is described. A system generates a match rule key based on a match rule, wherein the match rule specifies whether two objects match. The system creates candidate keys by applying the match rule key to data objects. The system creates a probe key by applying the match rule key to a probe object. The system determines whether the probe key matches a candidate key. The system determines whether the probe object matches a candidate object based on applying the match rule to the probe object and the candidate object if the probe key matches the candidate key corresponding to the candidate object. The system identifies the probe object and the candidate object as matching based on the match rule if the probe object matches the candidate object.

公开(公告)号：US20160140355A1

公开(公告)日：2016-05-19

申请号：US14548027

申请日：2014-11-19

Applicant: salesforce.com, inc.

Inventor： Arun Jagota ,  Gregory Haardt ,  Govardana Sachithanandam Ramachandran ,  Stanislav Georgiev ,  Matthew Fuchs Fuchs

IPC: G06F21/62 ,  G06F21/50

CPC classification number: G06F21/6218 ,  G06F2221/2117

Abstract: User trust scores based on registration features is described. A system identifies registration features associated with a user registered to interact with a database. The system calculates a registration trust score for the user based on a comparison of multiple registration features associated with the user to corresponding registration features associated with previous users who are restricted from interacting with the database and/or corresponding registration features associated with previous users who are enabled to interact with the database. The system restricts the user from interacting with the database if the registration trust score is above a registration threshold.

Abstract translation: 描述基于注册功能的用户信任评分。 系统识别与注册为与数据库交互的用户相关联的注册特征。 该系统基于与用户相关联的多个注册特征与与先前用户相关联的对应注册特征的比较来计算用户的注册信任分数，所述注册特征与被限制在与数据库交互的先前用户和/或与先前用户相关联的相应注册特征 启用与数据库交互。 如果注册信任分数高于注册阈值，则系统限制用户与数据库交互。

公开(公告)号：US20160063389A1

公开(公告)日：2016-03-03

申请号：US14473717

申请日：2014-08-29

Applicant: salesforce.com, inc.

Inventor： Matthew D. Fuchs ,  Arun Jagota

IPC: G06N7/00 ,  G06N99/00

CPC classification number: G06N5/02 ,  G06F17/30292 ,  G06N7/005 ,  G06N99/005

Abstract: The technology disclosed relates to methods for partitioning sets of features for a Bayesian classifier, finding a data partition that makes the classification process faster and more accurate, while discovering and taking into account feature dependence among sets of features in the data set. It relates to computing class entropy scores for a class label across all tuples that share the feature-subset and arranging the tuples in order of non-decreasing entropy scores for the class label, and constructing a data partition that offers the highest improvement in predictive accuracy for the data set. Also disclosed is a method for partitioning a complete set of records of features in a batch computation, computing increasing predictive power; and also relates to starting with singleton partitions, and using an iterative process to construct a data partition that offers the highest improvement in predictive accuracy for the data set.

Abstract translation: 所公开的技术涉及用于分配贝叶斯分类器的特征集合的方法，找到使分类处理更快更准确的数据分区，同时发现并考虑数据集中的特征集合之间的特征依赖性。 它涉及跨共享特征子集的所有元组的类标签的计算类熵分数，并且按照类标签的非递减熵分数的顺序排列元组，以及构建提供预测精度最高改进的数据分区 用于数据集。 还公开了一种用于分割批量计算中的特征记录的完整集合的方法，计算增加的​​预测能力; 并且还涉及从单例分区开始，并且使用迭代过程构造提供数据集的预测精度最高改进的数据分区。

公开(公告)号：US20130117191A1

公开(公告)日：2013-05-09

申请号：US13667316

申请日：2012-11-02

Applicant: salesforce.com, inc.

Inventor： Arun Jagota ,  Pawan Nachnani

IPC: G06Q10/06

CPC classification number: G06Q10/06

Abstract: A system and method for building a profile record for a person from business contacts stored in a database. Contacts having similar name signatures are collected together, then pairs of such contacts are compared using defined criteria.

Abstract translation: 用于从存储在数据库中的业务联系人构建人员的简档记录的系统和方法。 具有相似名称签名的联系人被收集在一起，然后使用定义的标准来比较这样的联系人对。

公开(公告)号：US10592955B2

公开(公告)日：2020-03-17

申请号：US14504593

申请日：2014-10-02

Applicant: salesforce.com, inc.

Inventor： Arun Jagota ,  Matthew Fuchs ,  Gregory Haardt

IPC: G06Q30/06 ,  G06Q30/02 ,  G06Q30/00 ,  G06Q10/06 ,  G06Q40/00 ,  G06Q10/10

Abstract: A system creates a graph of nodes connected by arcs, and identifies a first compound attribute associated with contacts purchased by a current user. The first compound attribute includes a first attribute associated with a first value and a second attribute associated with a second value. The system identifies a directed arc from a first node to a second node. The directed arc is associated with a probability that previous users who purchased a first contact associated with the first compound attribute also purchased a second contact associated with a second compound attribute. The second compound attribute includes the first attribute, associated with a third value which matches the first value, and the second attribute, associated with a fourth value, which lacks a match with the second value. The system outputs a recommendation for the current user to purchase contacts associated with the second compound attribute if the probability exceeds a threshold.

公开(公告)号：US10110533B2

公开(公告)日：2018-10-23

申请号：US14525385

申请日：2014-10-28

Applicant: salesforce.com, inc.

Inventor： Arun Jagota

IPC: G06F17/30 ,  H04L12/58

Abstract: Identifying entities in email signature blocks is described. A system scores each token, in a sequence of tokens from an email signature block, based on entity types, wherein each token is a word, a punctuation symbol, or an end-of-line character. The system identifies each entity sequence which includes a number of entities that matches the number of tokens in the sequence of tokens. The system identifies an entity sequence with a highest score based on applying scores for each token in the sequence of tokens to each identified entity sequence. The system outputs the sequence of tokens as an identified set of entities based on the entity sequence with the highest score.