Abstract: A data aggregator includes a memory, a network interface, a vectorization engine, and an aggregator. The memory stores a plurality of aggregation rules. The network interface retrieves a plurality of transactions that includes a first transaction and a second transaction. The vectorization engine determines a first transaction vector for the first transaction and a second transaction vector for the second transaction. The aggregator determines that the first transaction occurred during a first life event and that the second transaction occurred during a second life event. The aggregator also determines that the first life event and the second life event are the same and in response to that determination, assigns the first transaction and the second transaction to a cluster for the first life event. The aggregator further stores the first life event, the second life event, and an identifier for the cluster to the database.

Abstract: A method comprises creating a word vector from a message, wherein the word vector comprises creating a word vector from a message stored in a database, wherein the word vector comprises an entry for each word of the plurality of words and each entry indicating a number of occurrences in the message of the word for that entry. The method further comprising calculating a cosine difference between the word vector and each group vector of a plurality of group vectors and assigning the word vector to a group for a group vector of the plurality of group vectors based on the calculated cosine difference between the word vector and the group vector for the group. The method may also comprise reformatting the message to include a phrase and assigning the phrase to the same group that the word vector is assigned to. The method may also comprise notifying the user.

Abstract: A computing platform may receive video, audio, and/or biometric information of one or more people. The computing platform may identify the people based on a comparison of the video, audio, and/or biometric information to stored information in one or more user profiles each associated with the people. For example, the computing platform may compare multiple types of biometric information, including fingerprint, retina scan, facial features, and the like, as part of a process for identifying the people. The computing platform may further determine one or more interactions between the people, and, based on the interactions, determine and/or identify a relationship between the people. The identified relationships may further be used for confirming identifies of the people. Based on the identifications (e.g., biometric, video, audio, relationships), the computing platform may further provide access for the people to one or more services.

Abstract: Aspects of the disclosure relate to using natural language processing to identify a context of failure associated with a channel error and analyzing the identified context of failure in relation to historic data by machine learning algorithms to identify one or more of a severity ranking, alternate channel, and solution for the channel error. In some instances, a computing platform may receive data corresponding to a system event associated with a channel of server infrastructure, identify a technical issue, customer intent, and customer sentiment of the system event, determine a context of failure of the system event, generate a mapping of the context of failure in relation to historic data, and identify a suggested solution, severity assignment, and alternate channel for the system event based on the mapping of the context of failure in relation to the historic data.

Abstract: A method comprises creating a word vector from a message, wherein the word vector comprises creating a word vector from a message stored in a database, wherein the word vector comprises an entry for each word of the plurality of words and each entry indicating a number of occurrences in the message of the word for that entry. The method further comprising calculating a cosine difference between the word vector and each group vector of a plurality of group vectors and assigning the word vector to a group for a group vector of the plurality of group vectors based on the calculated cosine difference between the word vector and the group vector for the group. The method may also comprise reformatting the message to include a phrase and assigning the phrase to the same group that the word vector is assigned to. The method may also comprise notifying the user.

Abstract: A method comprises creating a word vector from a message, wherein the word vector comprises creating a word vector from a message stored in a database, wherein the word vector comprises an entry for each word of the plurality of words and each entry indicating a number of occurrences in the message of the word for that entry. The method further comprising calculating a cosine difference between the word vector and each group vector of a plurality of group vectors and assigning the word vector to a group for a group vector of the plurality of group vectors based on the calculated cosine difference between the word vector and the group vector for the group. The method may also comprise reformatting the message to include a phrase and assigning the phrase to the same group that the word vector is assigned to. The method may also comprise notifying the user.

Abstract: A computing platform may receive video, audio, and/or biometric information of one or more people. The computing platform may identify the people based on a comparison of the video, audio, and/or biometric information to stored information in one or more user profiles each associated with the people. For example, the computing platform may compare multiple types of biometric information, including fingerprint, retina scan, facial features, and the like, as part of a process for identifying the people. The computing platform may further determine one or more interactions between the people, and, based on the interactions, determine and/or identify a relationship between the people. The identified relationships may further be used for confirming identifies of the people. Based on the identifications (e.g., biometric, video, audio, relationships), the computing platform may further provide access for the people to one or more services.

Abstract: A computing platform may receive video, audio, and/or biometric information of one or more people. The computing platform may identify the people based on a comparison of the video, audio, and/or biometric information to stored information in one or more user profiles each associated with the people. For example, the computing platform may compare multiple types of biometric information, including fingerprint, retina scan, facial features, and the like, as part of a process for identifying the people. The computing platform may further determine one or more interactions between the people, and, based on the interactions, determine and/or identify a relationship between the people. The identified relationships may further be used for confirming identifies of the people. Based on the identifications (e.g., biometric, video, audio, relationships), the computing platform may further provide access for the people to one or more services.

Abstract: A method comprises creating a word vector from a message, wherein the word vector comprises an entry for each word of a plurality of words, and wherein each word of the plurality of words is assigned a weight. The method further comprises calculating a value for the word vector based on each entry of the word vector and the weights assigned to the plurality of words, and identifying that the message belongs to a first group by comparing the value for the word vector to a threshold. The word vector comprises an entry for each word of a plurality of words, and wherein each word of the plurality of words is assigned a weight.

Abstract: A computing platform having at least one processor, a memory, and a communication interface may receive, via the communication interface, a first content stream containing status information associated with a customer session. Responsive to receiving the first content stream, the computing platform may generate a feedback request, which is transmitted via the communication interface to a remote client device, causing the feedback request to be displayed on the remote client device. The computing platform may receive, via the communication interface, a second content stream containing information received from the remote client device in response to the feedback request. Responsive to receiving the second content stream, the computing platform may generate and transmit advice via the communication interface to the remote client device, causing the advice to be displayed on the remote client device.

Abstract: A computing platform may receive video, audio, and/or biometric information of one or more people. The computing platform may identify the people based on a comparison of the video, audio, and/or biometric information to stored information in one or more user profiles each associated with the people. For example, the computing platform may compare multiple types of biometric information, including fingerprint, retina scan, facial features, and the like, as part of a process for identifying the people. The computing platform may further determine one or more interactions between the people, and, based on the interactions, determine and/or identify a relationship between the people. The identified relationships may further be used for confirming identifies of the people. Based on the identifications (e.g., biometric, video, audio, relationships), the computing platform may further provide access for the people to one or more services.

Abstract: A data aggregator includes a memory, a network interface, a vectorization engine, and an aggregator. The memory stores a plurality of aggregation rules. The network interface retrieves a plurality of transactions that includes a first transaction and a second transaction. The vectorization engine determines a first transaction vector for the first transaction and a second transaction vector for the second transaction. The aggregator determines that the first transaction occurred during a first life event and that the second transaction occurred during a second life event. The aggregator also determines that the first life event and the second life event are the same and in response to that determination, assigns the first transaction and the second transaction to a cluster for the first life event. The aggregator further stores the first life event, the second life event, and an identifier for the cluster to the database.

Abstract: Aspects of the disclosure relate to using natural language processing to identify a context of failure associated with a channel error and analyzing the identified context of failure in relation to historic data by machine learning algorithms to identify one or more of a severity ranking, alternate channel, and solution for the channel error. In some instances, a computing platform may receive data corresponding to a system event associated with a channel of server infrastructure, identify a technical issue, customer intent, and customer sentiment of the system event, determine a context of failure of the system event, generate a mapping of the context of failure in relation to historic data, and identify a suggested solution, severity assignment, and alternate channel for the system event based on the mapping of the context of failure in relation to the historic data.

Abstract: A method comprises creating a word vector from a message, wherein the word vector comprises an entry for each word of a plurality of words, and wherein each word of the plurality of words is assigned a weight. The method further comprises calculating a value for the word vector based on each entry of the word vector and the weights assigned to the plurality of words, and identifying that the message belongs to a first group by comparing the value for the word vector to a threshold. The word vector comprises an entry for each word of a plurality of words, and wherein each word of the plurality of words is assigned a weight.

Abstract: A method comprises creating a word vector from a message, wherein the word vector comprises creating a word vector from a message stored in a database, wherein the word vector comprises an entry for each word of the plurality of words and each entry indicating a number of occurrences in the message of the word for that entry. The method further comprising calculating a cosine difference between the word vector and each group vector of a plurality of group vectors and assigning the word vector to a group for a group vector of the plurality of group vectors based on the calculated cosine difference between the word vector and the group vector for the group. The method may also comprise reformatting the message to include a phrase and assigning the phrase to the same group that the word vector is assigned to. The method may also comprise notifying the user.