Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to manage network slices. An example apparatus includes interface circuitry to acquire network information, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to reserve first network slices to satisfy service level objectives (SLOs) corresponding to first nodes, reserve second network slices to satisfy SLOs corresponding to second nodes, and reconfigure the first network slices to accept network communications from the second nodes when the network communications from the second nodes exceed a performance metric threshold.

Abstract: This application relates to systems and methods for providing search results based on a primary intent. In some examples, a disclosed system includes a memory resource storing instructions; and one or more processors coupled to the memory resource. The one or more processors are configured to execute the instructions to: receive, from a user, a search query including a plurality of words, identify a plurality of intention terms from the words of the search query, compute, for each of the plurality of intention terms, a compatibility score between the intention term and a query context associated with the intention term, determine, from the plurality of intention terms, a primary intention term having a maximum compatibility score among the plurality of intention terms, and generate, based on the primary intention term, a set of intent-based search results in response to the search query, the set of intent-based search results identifying a set of items associated with the primary intention term.

Abstract: In some examples, a system to may be configured to, for at least a first user of the plurality of users, implement a first set of operations that generate, for each of a first set of item types, attribute value data. Additionally, the system may implement a second set of operations that generate, for each of a second set of item types identified in catalogue data, clique data. Moreover, the system may, for the at least first user, implement a third set of operations that generate preference dependency data . Further, the system may, for the at least first user, based on the preference dependency data, the clique data, the attribute value data, generate, for each item type of a set of item types, output data including an affinity value for each item type of the first set of item types.

Abstract: In some examples, a system can include at least one processor and a memory storing instructions. In various examples, the at least one processor executes the instructions to, during a chat session, obtain, from a computing device of a first user, session data associated with the chat session and a first user. Additionally, the at least one processor executes the instructions to obtain a first set of data identifying a set of price drop items associated with the chat session, and obtain a second set of data identifying a set of attribute values associated with one or more attribute features of each item type. Based on the first set of data and the second set of data, the at least one processor may execute the instructions to implement a first set of operations that generate a third set of data. In some examples, the third set of data including affinity value data for each item of the set of price drop items.

Abstract: A session-specific conversion determination system can include a computing device configured to receive real-time signals of an event occurring from a user device. The real-time signals include interaction parameters. The computing device is also configured to obtain a set of historical data based on the interaction parameters and a set of facets and generate a probability affinity for each facet of the set of facets by implementing a machine learning model using the interaction parameters and the set of historical data as features. The computing device is also configured to adjust a display of a set of recommended items based on the probability affinity for each facet of the set of facets and transmit the display of the set of recommended items to a user interface of the user device.

Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction data of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of conversion scores, based on the set of features. In some examples, each conversion score of the plurality of conversion scores are associated with a particular user of the set of users and characterize a likelihood of a conversion event of the corresponding user changing from a trial-member status to a full-member status prior to a predetermined future time.

Abstract: This application relates to systems and methods for providing search results based on a primary intent. In some examples, a disclosed system includes a memory resource storing instructions; and one or more processors coupled to the memory resource. The one or more processors are configured to execute the instructions to: receive, from a user, a search query including a plurality of words, identify a plurality of intention terms from the words of the search query, compute, for each of the plurality of intention terms, a compatibility score between the intention term and a query context associated with the intention term, determine, from the plurality of intention terms, a primary intention term having a maximum compatibility score among the plurality of intention terms, and generate, based on the primary intention term, a set of intent-based search results in response to the search query, the set of intent-based search results identifying a set of items associated with the primary intention term.

Abstract: In some examples, a system tomay be configured to, for at least a first user of the plurality of users, implement a first set of operations that generate, for each of a first set of item types, attribute value data Additionally, the system may implement a second set of operations that generate, for each of a second set of item types identified incatalogue data, clique data . Moreover, the system may, for the at least first user, implement a third set of operations that generate preference dependency data . Further, the system may , for the at least first user, based on the preference dependency data, the clique data, the attribute value data, generate, for each item type of a set of item types, output data including an affinity value for each item type of the first set of item types.

Abstract: Disclosed herein is a system. The system includes a memory and a processor. The memory stores processor executable instructions for a recognition engine. The processor is coupled to the memory. The processor executes the processor executable to cause the system to define a plurality of baseline entities to be identified from documents in a workflow and digitize the one or documents to generate corresponding document object models. The recognition engine further causes the system to train a model by using as inputs the corresponding document object models and tagged files and determine, using the model, plurality of target entities from target documents.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving a user identifier, receiving an item identifier, determining user item quantity information related to quantities of the item previously selected by the user, determining a respective household size for each user, and determining aggregate household item quantity information related to quantities of the item previously selected by an aggregate of users of the same household size. If a first threshold level of the quantity of transactions is met, a recommended quantity is based on the user item quantity information, and if not, the recommended quantity is based on the aggregate household item quantity information. The user interface of the electronic device is updated to notify the user of the recommended quantity. Other embodiments are disclosed herein.

Abstract: A system and a computer-implemented method for analyzing workflow of test automation associated with a robotic process automation (RPA) application are disclosed herein. The computer-implemented method includes receiving the workflow of the test automation associated with the RPA application and analyzing, via an Artificial Intelligence (AI) model associated with a workflow analyzer module, the workflow of the test automation based on a set of pre-defined test automation rules. The computer-implemented method further includes determining one or more metrics associated with the analyzed workflow of the test automation and generating, via the AI model, corrective activity data based on the determined one or more metrics.

Abstract: A robotic process automation (RPA) workflow may be automatically created from text, an image, and/or a media file. A workflow sequence may be converted into a digital format using optical character recognition (OCR), and this information may then be analyzed by an artificial intelligence (AI) model and converted into a predicted RPA workflow. The predicted RPA workflow may be presented to a developer for approval, denial, or modification. Information pertaining to the selection by the developer may then be used for subsequent retraining of the AI model to improve prediction accuracy.

Abstract: Systems and methods for splitting an electronic file into sub-documents are provided. The electronic file is received. Portions of the electronic file are classified using a trained machine learning based model. The classifications represent relative positions of the portions within sub-documents of the electronic file. The electronic file is split into the sub-documents based on the relative positions of the portions. The sub-documents are output.

Abstract: Training and using artificial intelligence (AI)/machine learning (ML) models to automatically supplement and/or complete code of RPA workflows is disclosed. A trained AI/ML model may intelligently and automatically predict and complete the next series of activities in RPA workflows (e.g., one, a few, many, the remainder of the workflow, etc.). Actions users take while creating workflows over a time period may be captured and stored. The AI/ML model may then be trained and used to match the stored actions with stored workflow sequences of actions in order to predict and complete the workflow. As more and more workflow sequences are captured and stored over time, the AI/ML model may be retrained to predict a larger number of sequences and/or to more accurately make predictions. Auto-completion may occur in real-time in some embodiments to save time and effort by the user.

Abstract: Automatic completion of robotic process automation (RPA) workflows using machine learning (ML) is disclosed. A trained ML model may intelligently and automatically predict and complete the next series of activities in RPA workflows (e.g., one, a few, many, the remainder of the workflow, etc.). Actions users take while creating workflows over a time period may be captured and stored. The ML model may then be trained and used to match the stored actions with stored workflow sequences of actions in order to predict and complete the workflow. As more and more workflow sequences are captured and stored over time, the ML model may be retrained to predict a larger number of sequences and/or to more accurately make predictions. Auto-completion may occur in real-time in some embodiments to save time and effort by the user.

Abstract: A method including building a recommendation triggering model. The method can include receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item for the user. The method further can include determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items of the anchor item for the user. The method also can include determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation. The method additionally can include after determining the recommendation confidence, when the recommendation confidence is positive, transmitting, in real-time through the network, the one or more complementary items to be presented to the user via the user device. The method likewise can include after determining the recommendation confidence, when the recommendation confidence is not positive, refraining from transmitting the one or more complementary items to the user.

Abstract: A method including building a recommendation triggering model. The method can include receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item in a session by the user. The method further can include determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items based at least in part on: (a) the anchor item; and (b) a user profile of the user. The method also can include determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation based at least in part on one or more of: (a) the user profile; (b) the anchor item; (c) the one or more complementary items; or (d) one or more feedbacks from the user associated with one or more prior recommendations in the session.

Abstract: A method including building a recommendation triggering model. The method can include receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item in a session by the user. The method further can include determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items based at least in part on: (a) the anchor item; and (b) a user profile of the user. The method also can include determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation based at least in part on one or more of: (a) the user profile; (b) the anchor item; (c) the one or more complementary items; or (d) one or more feedbacks from the user associated with one or more prior recommendations in the session.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving a user identifier, receiving an item identifier, determining user item quantity information related to quantities of the item previously selected by the user, determining a respective household size for each user, and determining aggregate household item quantity information related to quantities of the item previously selected by an aggregate of users of the same household size. If a first threshold level of the quantity of transactions is met, a recommended quantity is based on the user item quantity information, and if not, the recommended quantity is based on the aggregate household item quantity information. The user interface of the electronic device is updated to notify the user of the recommended quantity. Other embodiments are disclosed herein.

Abstract: A system and a computer-implemented method for analyzing workflow of test automation associated with a robotic process automation (RPA) application are disclosed herein. The computer-implemented method includes receiving the workflow of the test automation associated with the RPA application and analyzing, via an Artificial Intelligence (AI) model associated with a workflow analyzer module, the workflow of the test automation based on a set of pre-defined test automation rules. The computer-implemented method further includes determining one or more metrics associated with the analyzed workflow of the test automation and generating, via the AI model, corrective activity data based on the determined one or more metrics.