Abstract: A system queries for entity data of a user from a first data source. The system displays a display panel in a user interface (UI), the display panel including measurement metrics for an account entity of the user from the entity data of the first data source. The system determines mode chaining information that specifies a chain of entity relationships between a number of entities in the entity data, where the relationships include one-to-one or one-to-many relationships. The system determines forecast information corresponding to the measurement metrics for the entities of the user. The system displays the forecast information as entity-based forecast values in the UI based on the mode chaining information, where the entity-based forecast values include a drill down of forecast values for the entities of the user.

Abstract: A system to validate behavior of a replacement component is disclosed. The system obtains first result data, the first result data being generated by performing a first request at a first component of a production environment. The system performs second request at a second component of the production environment to generate second result data. The system performs a parity check between the first result data and the second result data to determine an equivalence in behavior between the first request at the first component and the second request at the second component. The system generates discrepancy information indicating the equivalence in behavior between the first request at the first component and the second request at the second component based on the parity check. The system performs a third action based on the discrepancy information including storing the discrepancy information.

Abstract: The disclosure describes a method of generating a target profile including the target's sequence of events (SOE) for a task. Such target profile sequence of events is derived from several source group's transactions, where any source group's transactions cannot be shared with other source groups but the derived target group's profile is the only information that is shared. Source-side information is periodically extracted for a plurality of sources that each interact with a plurality of targets. The information includes source stages, resources, and stage transition events for a task with a target. Source information is used to generate a set of normalized stages, and a set of normalized events for transitioning between the stages of the set of normalized stages. An artificial intelligence (AI) model is trained using the source information. The AI model can generate a target profile with target process information inferred using the trained model.

Abstract: A processing device may obtain, from a remote data source, a first database formula having a first syntax. The processing device may translate the first database formula to a normalized database formula having a normalized syntax and obtain, from the remote data source, data that is associated with the first database formula. The processing device may update a target database, based on the obtained data and the normalized database formula.

Abstract: A server receives an indication of a start of a call to a conversation for a first user at a first terminal device. The server receives a connection request from the first user at a second terminal device. The server receives one or more requests to add one or more bookmarks to the conversation from first user at the second terminal device while the conversation for the first user is ongoing at the first terminal device, wherein the one or more bookmarks correlate to one or more conversation utterances within a timeline of the conversation. The server determines audio transcripts for the one or more voice utterances within the timeline of the conversation. The server tags and stores the bookmarks with the conversation based on the timeline.

Abstract: The disclosure describes systems, methods, and media for generating real-time insights in a voice over internet protocol (VoIP) conversation. According to the methods, an application server receives a transcript of one or more voice utterances of a participant in the VoIP conversation, and identifies a context of the VoIP conversation and a first state of the context based on the transcript. The application server further receives an intent of the participant from a conversation artificial intelligence (AI) engine based on the transcript provided to the conversation AI engine. The application server further formulates one or more queries based on the intent, the context, and the first state of the context to retrieve one or more insights from one or more backend databases, and transmits the one or more insights to a terminal of at least one participants of the VoIP conversation for display.

Abstract: Techniques for identifying similar companies based on profile data sets of the companies are provided. In one embodiment, a method comprises using a processing device to obtain a benchmark profile data set for a benchmark company and obtain a plurality of profile data sets, each of the plurality of profile data sets corresponding to a candidate company. The processing device may utilize a machine learning algorithm to determine the distance between each of the plurality of profile data sets and the benchmark profile data set and build profile graphs indicating the distance. The processing device may determine one or more of the plurality of profile data sets that are most similar to the benchmark profile data set based on the determined distance and identify the one or more candidate companies corresponding to the one or more profile data sets as companies most similar to the benchmark company.

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: Described herein are systems and methods for facilitating the information entry and task updates to a task database in a cloud server. The task database is in synchronization with a customer relationship management (CRM) system. The systems and methods described herein enable users to update the task database and enter information into the task database in a timely manner such that the task database can stay updated. The updated database can be used to construct a suggested task set at the beginning of a period of time to meet a preset target sales value for the end of the period of time. In one embodiment, a system includes a mobile application to capture contact information from a physical medium as an image, and to send the image to a cloud server, where a trained neural network model is to extract contact details and send the contact details back to the mobile application for editing and confirmation by a user.

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: 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: 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 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.