Abstract: In an embodiment, described herein is a system and method for creating a suggested task set to meet a target value. A cloud server, in response to receiving a request specifying a target value, retrieves completed task sets from a database. Each completed task set includes a same set of task categories. The cloud server derives a number of ratios from the retrieved completed task sets, including a composition ratio and a conversion rate for each task category, and an addition ratio for the number of completed task sets. Based on the derived ratios and the specified target value, the cloud server constructs the suggested task set, and displays in real-time the suggested task set together with current values for the task categories. The cloud server alerts users of a discrepancy between a current value and the corresponding suggested value for a task category when the discrepancy reaches a predetermined level.

Abstract: In an embodiment, a cloud server determines a point in time in a current task, and identifies a corresponding point in time in each of a number of completed tasks retrieved from a first database. User activity data from a second database is used to calculate time spent on the current task up to the identified point in time, a ratio between time spent on each completed task after the corresponding point in time and time spent on the completed task before the corresponding point in time. The cloud server also calculates a similarity score between each completed task and the current task based on a number of parameters and using a predetermined algorithm. A weighted arithmetic mean of the time ratios can be calculated using the similarity scores as weights, and can be used in conjunction with the time spent on the current task up to the identified point in time, to calculate time required to complete the current task.

Abstract: The disclosure describes various embodiments for determining emails that each need a response based on data from a customer relationship (CRM) system. In one embodiment, a method of determining such emails includes the operations of retrieving open tasks assigned to a user from a task database; determining one or more source email domains for one or more source contacts, and one or more target email domains for one or more target contacts; and determining one or more threads emails exchanged between the source contacts and the target contacts based on the source email domains and the target email domains. The method further includes the operations of creating an email list from the threads of emails, including a latest email from a group that was sent by a target contact; and generating a subset of the list of emails by analyzing contents of each of the list of emails using a machine learning model.

Abstract: According to one embodiment, a customer relationship management (CRM) system is queried to obtain a project that is associated with an entity (e.g., a user, a group of users, a client). A first list of one or more contacts (e.g., contact persons, referred to herein target contacts) associated with the project from the CRM system. For each of the contacts in the first list, a domain name is determined based on contact information of the contacts (e.g., emails, Web addresses, name of an account associated with the contacts). A first set of email addresses, referred to as target email addresses, is determined based on the domain name and contacts using a set of email identification rules. An email server is queried to retrieve a list of one or more emails and/or calendar events based on the first set of email addresses.

Abstract: Activity data of a set of tasks as a training set is obtained from a list of communication platforms associated with the tasks. For each of the tasks in the training set, a set of activity metrics is compiled according to a set of predetermined activity categories based on the activity data of each task. The activity metrics of all of the tasks in the training set are aggregated based on the activity categories to generate a data matrix. A principal component analysis is performed on the metrics of its covariance matrix to derive an activity dimension vector, where the activity dimension vector represents a distribution pattern of the activity metrics of the tasks. The activity dimension vector can be utilized to determine an activity score of a particular task, where the activity score of a task can be utilized to estimate a probability of completeness of the task.

Abstract: Described herein are systems and methods for facilitating the information entry and task updates to a task database in a cloud server. In one embodiment, a method includes the operations of receiving, by one or more neutral network models on a cloud server, voice instructions captured by an interactive voice response (IVR) application on a mobile device, wherein the voice instructions are to update states of one or more tasks displayed in a voice interface of the IVR application; recognizing the voice instructions and constructing appropriate texts using the one or more trained neutral network models; presenting the texts to one or more voice interfaces provided by the IVR application for confirmation by a user; and storing the user confirmed texts to a repository on the cloud server.

Abstract: Described herein are systems and methods for facilitating the information entry and task updates to a task database in a cloud server. In one embodiment, a method includes the operations of receiving, by one or more neutral network models on a cloud server, voice instructions captured by an interactive voice response (IVR) application on a mobile device, wherein the voice instructions are to update states of one or more tasks displayed in a voice interface of the IVR application; recognizing the voice instructions and constructing appropriate texts using the one or more trained neutral network models; presenting the texts to one or more voice interfaces provided by the IVR application for confirmation by a user; and storing the user confirmed texts to a repository on the cloud server.

Abstract: Through a first processing thread, a first database is accessed via a first API to retrieve a list of event objects of the first database. Through a second processing thread, for each of the event objects, participant identifiers (IDs) are determined from the event object. For each of the participant IDs, a domain ID is extracted from the participant ID. A list of one or more entity objects are identified based on the domain ID, where the entity objects are stored in a second database such as a task database storing and managing many tasks. At least one attribute of at least one of the entity objects is modified based of the participant ID and the domain ID, which generates a modified entity object. Through a third processing thread, any event objects that have been modified are transmitted to the second database via a second API over the network.

Abstract: According to various embodiments, described herein are systems and methods for training machine learning (ML) models to generate real-time scores to predict the probabilities of task completion. In one embodiment, an exemplary method includes the operations of receiving, from a data store, a set of features and a workflow for training a first type of ML models, the workflow specifying a data source, a number of stages and associated parameters for training the ML models; retrieving, from the data source, training data for the set of features; and segmenting the training data into different segments. The method further includes the operations of training a separate first type of ML model using each of the different segment of the training data in accordance with the workflow; and persisting the first type of trained ML models into the data storage. The method also includes using a trained ML model to generate probability scores and displaying the scores to users in real-time.

Abstract: According to some embodiments, described herein is a system and method for classifying and grouping users of a task database system based on their activities in completing tasks defined in the task database system. A cloud server communicatively coupled to the task database system receives task data from the task database system, receives a request for ranking users under a particular level in a user hierarchy, gathers user activity data from an activity data server, uses a predetermined algorithm calculate a raw score for each user based on the user's activity, normalizes the raw score, and generates a ranking of the users based on their normalized scores. The ranking can be used as an indicator for determining which users need additional training in communicating with outside parties in completing assigned tasks.

Abstract: A system performs a first query to retrieve a commit lag timestamp, where the commit lag timestamp specifies an earliest time instance when a record of an entity is inserted or updated, but is yet to be committed, to a data source. The system determines an inline crawl interval based on the commit lag timestamp. The system performs a second query based on the inline crawl interval to retrieve a number of record identifiers and/or modification dates. The system performs a third query based on the inline crawl interval, where the third query corresponds to records that exist in a data store. The system determines at least one identifier that is missing from the third query due to commit lag based on a difference between data corresponding to the second and third queries. The system persists data corresponding to the second query and the at least one missing identifier.

Abstract: For a given user, a task database system associated with the user is queried to identify a list of tasks associated with the user. For each of the tasks, an email system associated with the user is queried to obtain one or more emails and calendar events associated with the task. An IM system associated with the user is queried to obtain IM messages of an IM channel of which the user is a member. The IM channel has been specifically created to exchange messages concerning the task amongst the members of the IM channel. The task is then determined whether it satisfies a predetermined state (e.g., inactive, lack of future meetings) in view of emails, calendar events, and IM messages associated with the task based on a set of rules. If the task is determined to satisfy the predetermined state, a notification message is transmitted to a preconfigured destination.

Abstract: Activity data of a set of tasks as a training set is obtained from a list of communication platforms associated with the tasks. For each of the tasks in the training set, a set of activity metrics is compiled according to a set of predetermined activity categories based on the activity data of each task. The activity metrics of all of the tasks in the training set are aggregated based on the activity categories to generate a data matrix. A principal component analysis is performed on the metrics of its covariance matrix to derive an activity dimension vector, where the activity dimension vector represents a distribution pattern of the activity metrics of the tasks. The activity dimension vector can be utilized to determine an activity score of a particular task, where the activity score of a task can be utilized to estimate a probability of completeness of the task.

Abstract: Activity data of a set of tasks as a training set is obtained from a list of communication platforms associated with the tasks. For each of the tasks in the training set, a set of activity metrics is compiled according to a set of predetermined activity categories based on the activity data of each task. The activity metrics of all of the tasks in the training set are aggregated based on the activity categories to generate a data matrix. A principal component analysis is performed on the metrics of its covariance matrix to derive an activity dimension vector, where the activity dimension vector represents a distribution pattern of the activity metrics of the tasks. The activity dimension vector can be utilized to determine an activity score of a particular task, where the activity score of a task can be utilized to estimate a probability of completeness of the task.

Abstract: According to one embodiment, a customer relationship management (CRM) system is queried to obtain a list of projects that are associated with a particular user. A subset of the projects is selected from the list. Each of the projects in the subset includes an attribute that has been set to a predetermined attribute value or attribute state by the user (e.g., flagged, starred, a checkbox). For each of the projects in the subset, a communication channel (e.g., an instant messaging or IM system, an email group, a group chat, a social network community) associated with the project is identified. It is determined whether the user is already a member of the communication channel. If the user is not a member of the communication channel, the user is automatically added to the communication channel without user intervention from the user.

Abstract: According to one embodiment, a customer relationship management (CRM) system is queried to obtain a project that is associated with an entity (e.g., a user, a group of users, a client). A first list of one or more contacts (e.g., contact persons, referred to herein target contacts) associated with the project from the CRM system. For each of the contacts in the first list, a domain name is determined based on contact information of the contacts (e.g., emails, Web addresses, name of an account associated with the contacts). A first set of email addresses, referred to as target email addresses, is determined based on the domain name and contacts using a set of email identification rules. An email server is queried to retrieve a list of one or more emails and/or calendar events based on the first set of email addresses.

Abstract: The long-lived and recently modified (LLRM) partitioning is a system and method for partitioning slowly changing aged data for a given year into one long-lived partition (LLP) and multiple recently modified partitions (RMP) where the RMP corresponds to a time period of a first time granularity. Each record has a start and end time to indicate its lifetime. A record in a RMP is modified at a timepoint corresponding to the time period of a RMP. RMP records can be sampled every second, minute, hour or day. The LLP holds records whose lifetime is greater than the time period of a first time granularity. Queries pertaining to a given timepoint is performed on data that is a union of the LLP and the RMP pertaining to that timepoint instead of the unpartitioned table thus leading to shorter query processing time and better utilization of memory storage.

Abstract: According to one embodiment, a customer relationship management (CRM) system is queried to obtain a project that is associated with an entity (e.g., a user, a group of users, a client). A first list of one or more contacts (e.g., contact persons, referred to herein target contacts) associated with the project from the CRM system. For each of the contacts in the first list, a domain name is determined based on contact information of the contacts (e.g., emails, Web addresses, name of an account associated with the contacts). A first set of email addresses, referred to as target email addresses, is determined based on the domain name and contacts using a set of email identification rules. An email server is queried to retrieve a list of one or more emails and/or calendar events based on the first set of email addresses.

Abstract: In an embodiment, described herein is a system and method for creating a suggested task set to meet a target value. A cloud server, in response to receiving a request specifying a target value, retrieves completed task sets from a database. Each completed task set includes a same set of task categories. The cloud server derives a number of ratios from the retrieved completed task sets, including a composition ratio and a conversion rate for each task category, and an addition ratio for the number of completed task sets. Based on the derived ratios and the specified target value, the cloud server constructs the suggested task set, and displays in real-time the suggested task set together with current values for the task categories. The cloud server alerts users of a discrepancy between a current value and the corresponding suggested value for a task category when the discrepancy reaches a predetermined level.

Abstract: In an embodiment, a cloud server determines a point in time in a current task, and identifies a corresponding point in time in each of a number of completed tasks retrieved from a first database. User activity data from a second database is used to calculate time spent on the current task up to the identified point in time, a ratio between time spent on each completed task after the corresponding point in time and time spent on the completed task before the corresponding point in time. The cloud server also calculates a similarity score between each completed task and the current task based on a number of parameters and using a predetermined algorithm. A weighted arithmetic mean of the time ratios can be calculated using the similarity scores as weights, and can be used in conjunction with the time spent on the current task up to the identified point in time, to calculate time required to complete the current task.