Abstract: Logic may receive one or more questions from a customer in a chat. The logic may associate the one or more questions with one or more categories of subjects. The logic may store indications of the one or more categories of the subjects with a customer profile. The logic may select questions for a quiz for the customer based on the one or more categories of the subjects in the customer profile from a set of questions. The logic may present the one or more questions to the customer. And, in some embodiments, the logic may receive answers to the one or more questions from the customer.

Abstract: Disclosed embodiments may include a system providing for currency transfer using virtual or augmented reality environments. The system may receive, from a user device, a first input indicating a first selection of a first virtual reality item associated with a user. The system may receive, from the user device, a second input indicating a first movement of the first virtual reality item. The system may determine whether the first virtual reality item is in proximity to a purchasable item. In response to determining that the first virtual reality item is in proximity to the purchasable item, the system may initiate a transfer by retrieving, from a store, transfer information regarding the purchasable item and store account information, retrieving user account information, and transmitting, to a payment processor, the user account information, the store account information, and the transfer information.

Abstract: Disclosed embodiments may include a system providing for currency transfer using augmented reality environments. The system may receive, at a user device associated with a user, a first input indicating a first selection of a first augmented reality item. The system may receive a second input indicating a first movement of the first augmented reality item. The system may determine whether the first augmented reality item is in proximity to a purchasable item. In response to determining that the first augmented reality item is in proximity to the purchasable item, the system may initiate a transfer by receiving image data containing a computer readable code associated with the purchasable item, processing the image data, retrieving store account information and transfer information regarding the purchasable item, retrieving user account information, and transmitting, to an account processor, the user account information, the store account information, and the transfer information.

Abstract: A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to correct ASR errors. The system may receive a transcription comprising transcribed word(s) and may determine whether the transcribed word(s) exceed associated predefined confidence level(s). Responsive to determining a transcribed word does not exceed a predefined confidence level, the system may generate a predicted word. The system may calculate a distance between numerical representations of the transcribed word and the predicted word and may determine whether the distance exceeds a predefined threshold. Responsive to determining the distance exceeds the predefined threshold, the system may determine whether at least one red flag word of a list of red flag words corresponds to a context of the transcription, and, responsive to making that determination, may classify the transcription as associated with a first category.

Abstract: A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to correct ASR errors. The system may receive a transcription comprising transcribed word(s) and may determine whether the transcribed word(s) exceed associated predefined confidence level(s). Responsive to determining a transcribed word does not exceed a predefined confidence level, the system may generate a predicted word. The system may calculate a distance between numerical representations of the transcribed word and the predicted word and may determine whether the distance exceeds a predefined threshold. Responsive to determining the distance exceeds the predefined threshold, the system may determine whether at least one red flag word of a list of red flag words corresponds to a context of the transcription, and, responsive to making that determination, may classify the transcription as associated with a first category.

Abstract: Systems and methods for active medical planning and customized budgeting may include a database that stores medical data, an interface, and a processor. The processor may be configured to receive and parse a medical document for medical procedure and related information, implement a learning algorithm to predict, based on the parsed medical and related information as well as the medical data, one or more medical procedures that the user is likely to undergo subsequent to any medical procedures parsed from the medical document, a cost associated with each predicted medical procedure, and a time horizon for each of the one or more predicted procedures, and train the predictive model with feedback from the user.

Abstract: Disclosed embodiments pertain to protecting sensitive information. A browser extension associated with a web browser can detect a user entering information associated with the user into an electronic form. The browser extension can monitor the user entering sensitive information into the electronic form and detect that the user has entered sensitive information incorrectly. In response, the browser extension can provide a warning to the user that sensitive information has been incorrectly entered. Instructions can be displayed to a user on how incorrectly entered sensitive information is to be corrected. The incorrectly entered sensitive information is corrected based on a response from the user before the sensitive information propagates beyond the electronic form.

Abstract: Disclosed aspects pertain to predicting a likelihood that a user enters sensitive data inappropriately and mitigating such a risk. An electronic form is monitored for user interaction. Non-biometric behavior data can be received or otherwise acquired for the user. A machine learning model can be invoked to determine a likelihood that the user will input sensitive data inappropriately into a form field based on the similarity of user non-biometric behavior data with historic non-biometric behavior data. User assistance, such as a message or warning, can be initiated when the likelihood satisfies a predetermined threshold to mitigate the risk of input of sensitive data inappropriately. The field can also be monitored to detect the presence of inappropriate sensitive data. The user can be prompted to redact or remove the sensitive data if detected.

Abstract: Disclosed embodiments pertain to identifying sensitive data using redacted data. Data entry into electronic form fields can be monitored and analyzed to detect improperly entered sensitive data. The type of sensitive data can be determined, and the sensitive data can be removed or redacted from the electronic form field. Surrounding context data, including text associated with the sensitive data, can be identified and captured. The context data and type of sensitive data can be utilized to train or update a machine learning model configured to identify sensitive data. In one instance, the machine learning model can be employed to detect improperly entered sensitive data, and context and type can be utilized to improve the performance and predictive power of the machine learning model.

Abstract: Methods and systems disclosed herein describe customizing searching. Search queries may be customized according to a user's preferences. A user may emphasize or indicate that additional weight should be given to one or more terms in a search query. Terms that are weighted higher may have a larger impact on the results that are returned in response to the search query. In addition to changing the terms in a search query, a user may provide a weight for each term. Each term in a search query may be weighted to varying degrees, giving a user more control over the results that are returned. The weights may be used with machine learning techniques to generate a vector representation of a search query. The vector representation of the search query may be compared with vector representations of search objects to determine results that match the search query.

Abstract: Systems and methods for reducing network traffic associated with a service. In some aspects, the systems and methods provide for using a first machine learning model to process a data stream for a communication with a user and generate a confidence score regarding whether to assign a communication suppression flag to the user account. Based on the confidence score not exceeding a first threshold, a communication suppression flag is not assigned to the user account. Based on the confidence score being between first and second thresholds, at least a portion of the data stream is extracted based on temporal proximity to a time stamp of an intent of the user to not receive further communications. Using a second machine learning model, the extracted portion of the data stream is processed to generate a prediction regarding whether to assign a communication suppression flag to the user account.

Abstract: Disclosed embodiments relate to addition of tags or keywords to metadata associated with sensitive data to aid in subsequent root cause analysis regarding incorrect entry of sensitive data. Sensitive data entered into an electronic form be identified. Next, context information can be collected regarding a user that entered the data. A machine learning model can be invoked that is trained to automatically determine a tag based on the context information and a confidence score associated with the tag. The tag can be added to metadata of a data string that includes the sensitive data. A data steward can be prompted to evaluate and correct the tag when the confidence score satisfies a predetermined threshold.

Abstract: Disclosed embodiments can pertain to utilizing biometric behavior to prevent inappropriate entry of sensitive information. Current biometric behavior of a user, specific to the user's interaction with an electronic form, can be acquired and compared to benchmark biometric behavior of the user associated with past interaction with the electronic form to create a comparison result. A similarity score based on the comparison result can then be established. It can be inferred that sensitive information was incorrectly entered into the electronic form when the similarity score satisfies a predetermined similarity threshold. The sensitive information can be identified, and a machine learning model can be triggered to generate a risk score based on the similarity score and the identified sensitive information. A user can be prompted to redact incorrectly entered sensitive information when the risk score satisfies a predetermined risk threshold.

Abstract: Systems and computer-implemented methods disclosed herein relate to detecting errors in manually entered data. In one embodiment, the system can identify a named entity automatically from a conversation between a customer and service agent with a named entity recognition model that employs natural language processing and machine learning to detect a word or string of words in the conversation that corresponds to a named entity category. In another embodiment, the system can determine whether data entered into a field on a service platform by the service agent includes an error by comparing the data entered with the named entity. In another embodiment, the system can transmit an alert to the service agent through the service platform when there is a mismatch between the named entity and the data entered.

Abstract: Methods and systems disclosed herein describe customizing searching. Search queries may be customized according to a user's preferences. A user may emphasize or indicate that additional weight should be given to one or more terms in a search query. Terms that are weighted higher may have a larger impact on the results that are returned in response to the search query. In addition to changing the terms in a search query, a user may provide a weight for each term. Each term in a search query may be weighted to varying degrees, giving a user more control over the results that are returned. The weights may be used with machine learning techniques to generate a vector representation of a search query. The vector representation of the search query may be compared with vector representations of search objects to determine results that match the search query.

Abstract: A system and method for allowing a single live customer service agent to simultaneously serve multiple customers. According to various embodiments, a virtual agent operates at the front end to receive and attempt to handle customer issues. The virtual agent employs speech recognition and intent mapping in order to generate a proposed response that attempts to identify and resolve customer issues. In some scenarios, the proposed response includes both a response message and a response action to be taken. A chat history and the proposed response is then provided to the live agent. The live agent reviews the information provided, and determines whether the proposed response is appropriate. The live agent then approves the response or revises or replaces the response. The final response is sent back to the virtual agent for processing and providing to the customer.

Abstract: In some implementations, a system may capture audio from a call between a calling device and a called device. The system may filter the captured audio to generate a background audio layer. The system may generate an audio footprint that is a representation of sound in the background audio layer. The system may determine that the audio footprint includes a triggering sound footprint based on one or more audio characteristics of the audio footprint. The system may detect synthetic sound based on the audio footprint and after determining that the audio footprint includes the triggering sound footprint, wherein the synthetic sound is indicative of a sound recording. The system may transmit a notification to one or more devices associated with the call based on detecting the synthetic sound.

Abstract: A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to correct ASR errors. The system may receive a transcription comprising transcribed word(s) and may determine whether the transcribed word(s) exceed associated predefined confidence level(s). Responsive to determining a transcribed word does not exceed a predefined confidence level, the system may generate a predicted word. The system may calculate a distance between numerical representations of the transcribed word and the predicted word and may determine whether the distance exceeds a predefined threshold. Responsive to determining the distance exceeds the predefined threshold, the system may determine whether at least one red flag word of a list of red flag words corresponds to a context of the transcription, and, responsive to making that determination, may classify the transcription as associated with a first category.

Abstract: Methods and systems disclosed herein describe customizing searching. Search queries may be customized according to a user's preferences. A user may emphasize or indicate that additional weight should be given to one or more terms in a search query. Terms that are weighted higher may have a larger impact on the results that are returned in response to the search query. In addition to changing the terms in a search query, a user may provide a weight for each term. Each term in a search query may be weighted to varying degrees, giving a user more control over the results that are returned. The weights may be used with machine learning techniques to generate a vector representation of a search query. The vector representation of the search query may be compared with vector representations of search objects to determine results that match the search query.