Abstract: Aspects of the present disclosure relate to real-time invoice error prevention. Embodiments include receiving a value related to an item or service during creation of an invoice by a user via a user interface, and determining a user-level mean and a user-level standard deviation related to the value based on historical invoices of the user. Embodiments include determining a global mean and a global standard deviation related to the value based on historical invoices of a plurality of users. Embodiments include selecting weights for the user-level mean, the user-level standard deviation, the global mean, and the global standard deviation based on a total number of the historical invoices of the user. Embodiments include determining an expected range for the value based on the user-level mean, the user-level standard deviation, the global mean, the global standard deviation, and the weights. Embodiments include determining that the value is outside the expected range.

Abstract: Aspects of the present disclosure provide techniques for predicting content relevant to questions based on reference links. Embodiments include receiving a set of question and answer (Q/A) pairs and identifying a set of references in the set of Q/A pairs that link pairs of Q/A pairs of the set of Q/A pairs. Embodiments include identifying popular Q/A pairs of the set of Q/A pairs based on the set of references. The popular Q/A pairs may be referenced by a subset of the set of Q/A pairs and each respective Q/A pair of the subset of the set of Q/A pairs may comprise a respective question of a plurality of questions. Embodiments include training a model based on the plurality of questions, the popular Q/A pairs, and the set of references, to predict Q/A pairs of the set of Q/A pairs that are relevant to a given question.

Abstract: A transcript of an audio conversation between multiple users (e.g., two users) is generated. The transcript is displayed in real time within a VR environment as the conversation takes place. A virtual selection tool is displayed within the VR environment to allow for a selection of different portions of the transcript. In addition, a virtual keyboard and or virtual panels with characters may be displayed and the virtual selection tool may be used to make selections from these displays as well. These selections are used to generate new text. The new text may form part of a user's notes of the conversation or an entry for a text field within the VR environment.

Abstract: A method including receiving a user input from a user device. The method also includes generating test inputs including the user input and modified inputs. The user input is processed with a rephrasing model to form the modified inputs. The method also includes executing a test model to generate test outputs, including an original test output and modified test outputs, from processing the test inputs. The method also includes generating similarity scores by performing similarity comparisons among the test outputs. The method also includes determining a model confidence from the similarity scores. The method also includes routing the user input responsive to the model confidence satisfying or failing to satisfy a confidence threshold.

Abstract: A method is provided for authenticating a user. A request to access a resource is received from a user agent. A cookie associated with the request is identified. The cookie includes a first subset of data that was previously used to authenticate the user. The cookie is validated based on the first subset of the data. Responsive to validating the cookie, a second subset of the data is retrieved from server-side storage. A risk decision is generated based on the first subset and the second subset. When the risk decision meets a threshold, the user is authenticated without presenting an authentication challenge, and access to the resources permitted.

Abstract: A method in a client backoff filter. The method includes receiving, from a server, a backoff data packet having backoff metadata. The method also includes saving the backoff metadata in a cache local to the client backoff filter. The method also includes receiving a subsequent request for service. The method also includes checking whether an attribute of the subsequent request for service matches the backoff metadata in the cache. The method also includes performing, responsive to checking, an action including at least one of the group including: blocking, responsive to the attribute matching the backoff metadata in the cache, transmission of the subsequent request for service to a server, and transmitting, responsive to the attribute failing to match the backoff metadata in the cache, the subsequent request for service to the server.

Abstract: A processor may receive user information for a request payload from an external device and data describing a plurality of user interface (UI) elements configured to be presented in a UI of the external device. The processor may select a machine learning (ML) model from a plurality of ML models using a contextual bandit ML model that is trained based on the user information. The processor determines at least one recommended user interface (UI) element with a selected ML model, based on the user information and the data describing the plurality of UI elements. The at least one recommended UI element may be presented in the UI of the external device. The processor may receive event data indicating a user interaction with the at least one recommended UI element in the UI of the external device. The contextual bandit ML model may be re-trained based on the event data.

Abstract: A method may include obtaining, for each period within a time interval, raw performance metric values for a performance metric of a service, encoding the raw performance metric values to generate inferred performance metric values for the performance metric, and selecting a portion of the inferred performance metric values. The portion corresponds to a representative period. The may method further include obtaining a service request for the service, generating a simulated response to the service request, and transmitting the simulated response using the portion of the inferred performance metric values.

Abstract: Certain aspects of the present disclosure provide techniques for detecting sentences that are utterances by an agent indicating an intent to poach a customer. According to certain embodiments, a language model is trained using query sentences that are confirmed to be sentences used in poaching a customer, to identify semantically similar sentences in a corpus. These semantically similar sentences are then used as base sentences for comparison to sentences in a transcript. Sentences of the transcript that are found to be semantically similar to one or more base sentences are provided to a user device for review and confirmation that the similar sentence was generated by an agent in an attempt to poach a customer.

Abstract: A method including identifying first and second training data having first and second subsets of click-through information for a dataset. Identifying includes associating the first and second subsets first and second applications executing on first and second domains having divergent first and second ontologically defined groupings of entities. The method also includes storing, as first and second vector data structures, the first and second training data. The method also includes training, on the first and second vector data structures, first and second ARIMA machine learning models. The first trained ARIMA machine learning model is trained on the first domain and the second trained ARIMA machine learning model is trained on the second domain. The method also includes deploying the first and second trained ARIMA machine learning models.

Abstract: A method including receiving an input and embedding the input into a first data structure that defines first relationships among images and texts. The method also includes comparing the first data structure to an index including a second data structure that defines second relationships among pre-determined texts and pre-determined images. The method also includes returning a subset of images from the pre-determined images. The subset includes those images in the pre-determined images for which matches exist between the first relationships and the second relationships.

Abstract: A visual-based classification model influenced by text features as a result of the outputs of a text-based classification model is disclosed. A system receives one or more documents to be classified based on one or more visual features and provides the one or more documents to a student classification model, which is a visual-based classification model. The system also classifies, by the student classification model, the one or more documents into one or more document types based on one or more visual features. The one or more visual features are generated by the student classification model that is trained based on important text identified by a teacher classification model for the one or more document types, with the teacher classification model being a text-based classification model. Generating training data and training the student classification model based on the training data are also described.

Abstract: In a pipeline, data events generated by a producer application are temporally grouped by using a group identification tag. For each data event, data points are generated and uploaded to a storage and cache at each point of production and consumption. The storage allows a matching of data events between the production point and the consumption point, thereby ensuring that streaming parity is maintained. In cases of mismatch, the cache allows for detecting missing data events, i.e., identifying data events that were generated by an upstream producer application, but not consumed by a downstream consumer. While being agnostic to the transformations applied by the various applications in the pipeline, the embodiments disclosed herein keep track of the output data events and input data events and precisely identify the missing data events.

Abstract: A method converts a request received from a client application to a query by a serverless function operating on a virtual machine instance provisioned responsive to the request. The query is transmitted to an application database corresponding to the client application. A result is received from the application database. The result is converted into a response and the response is transmitted to the client application in response to the request. Display of the client application is updated based on response with the result.

Abstract: Systems and methods for assessment of user price sensitivity using a predictive model are disclosed.

Abstract: A method and system for generating anomaly-detection rules for communication protocols are provided. The method includes receiving communication data; constructing at least one N-gram from the received communication data; analyzing the at least one N-gram by comparing the constructed at least one N-gram with a repository of N-gram analyses to identify conditional probabilities of certain characteristics; and generating anomaly-detection rules based on the N-gram analysis.

Abstract: A method includes receiving a user input including natural language text. The method also includes generating modified inputs from the user input. The method also includes executing a machine learning model on the modified inputs to generate model outputs. The method also includes sampling the model outputs using a statistical sampling strategy to generate sampled model outputs. The method also includes clustering the sampled model outputs into clusters. The method also includes generating a confidence metric of the clusters. The method also includes routing, automatically in a computing system, the user input based on whether the confidence metric satisfies a threshold value.

Abstract: Customizable template-based user interfaces for digital CRM tools. For instance, the CRM tools are pre-loaded with customizable templates, which are rendered as interactive user interfaces that allow customization of the templates based on user needs. This customization therefore allows a user to rapidly, visually, and easily define a customer engagement pipeline and customer segments without any specialized programming knowledge. The user interfaces may dynamically show the flow of customers between different phases and provide recommended actions to further drive customer engagement. Additionally, the CRM tools are integrated with other on-line platforms such as social media sites and e-commerce sites.