Abstract: Methods, systems, and computer programs are presented for abstractive summarization of text by viewing sequence transduction as a language modeling problem. One method comprises an operation for training a machine-learning program to create a machine-learning model that estimates a word to be added to a running summary for the text being summarized. The method further comprises operations for detecting the text to be summarized, initializing the running summary, and performing a plurality of iterations. Each iteration comprises providing, to the machine-learning model, the source text and the running summary, and adding, using the machine-learning model, a new word to the running summary. Further, the method comprises an operation for storing, on a memory, the running summary as the summary of the text.

Abstract: Methods, systems, and computer programs are presented for searching the content of voice conversations. The conversations are translated into text and analysis of the conversation is performed to identify information in the conversation. The information identified includes turns taking data in the conversation and states identified within each state. A powerful user interface (UI) is provided to review the conversations and add annotations that tag the different turns. Additionally, parameter values are extracted from the text. A powerful search engine is provided with multiple search options, such as searching for text, searching by state within the conversation, searching by parameters extracted from the conversation, or a combination thereof.

Abstract: Methods, systems, and computer programs are presented for formatting raw text. One method includes an operation for accessing raw text comprising words corresponding to one or more sentences. The raw text is lowercase text without any punctuation. Further, the method includes operations for creating a plurality of sub-words corresponding to the raw text, and for generating, by a machine-learning (ML) model, an output for each sub-word based on the created sub-words. The output for each sub-word indicates a formatting operation for the corresponding sub-word. The method further includes an operation for generating, based on the formatting operations in the outputs for the sub-words, formatted text corresponding to the raw text. The formatted text is text with correct grammar, proper punctuation, and proper capitalization according to a meaning of words spoken by a speaker associated with the raw text.

Abstract: A computer-implemented method for generating optimized user journeys leveraging artificial intelligence is disclosed. The method includes receiving a user text prompt describing a desired journey objective and extracting context data for multiple users from a customer data platform. This context data encompasses attributes, events, predicted traits and audience memberships. A journey generation prompt is constructed by combining the received user prompt and extracted context data. This prompt is input into a machine learning model which processes the prompt to produce a user journey definition comprising interconnected nodes representing journey phases like audiences, waits and messages. The journey system validates the definition, modifying nodes to conform to predefined schema rules. The validated journey is output to campaign orchestration systems for execution across customer touchpoints. As journeys run, engagement data is collected for retraining models to improve journey performance over time.

Abstract: Methods, systems, and computer programs are presented for searching the content of voice conversations. The conversations are translated into text and analysis of the conversation is performed to identify information in the conversation. The information identified includes turns taking data in the conversation and states identified within each state. A powerful user interface (UI) is provided to review the conversations and add annotations that tag the different turns. Additionally, parameter values are extracted from the text. A powerful search engine is provided with multiple search options, such as searching for text, searching by state within the conversation, searching by parameters extracted from the conversation, or a combination thereof.

Abstract: Methods, systems, and computer programs are presented for formatting raw text. One method includes an operation for accessing raw text comprising words corresponding to one or more sentences. The raw text is lowercase text without any punctuation. Further, the method includes operations for creating a plurality of sub-words corresponding to the raw text, and for generating, by a machine-learning (ML) model, an output for each sub-word based on the created sub-words. The output for each sub-word indicates a formatting operation for the corresponding sub-word. The method further includes an operation for generating, based on the formatting operations in the outputs for the sub-words, formatted text corresponding to the raw text. The formatted text is text with correct grammar, proper punctuation, and proper capitalization according to a meaning of words spoken by a speaker associated with the raw text.

Abstract: Methods, systems, and computer programs are presented for searching the content of voice conversations. The conversations are translated into text and analysis of the conversation is performed to identify information in the conversation. The information identified includes turns taking data in the conversation and states identified within each state. A powerful user interface (UI) is provided to review the conversations and add annotations that tag the different turns. Additionally, parameter values are extracted from the text. A powerful search engine is provided with multiple search options, such as searching for text, searching by state within the conversation, searching by parameters extracted from the conversation, or a combination thereof.

Abstract: Methods, systems, and computer programs are presented for classifying information in conversations and extracting information from the conversations. An Engagement Intelligence Platform (EIP) analyzes transcripts of conversations to find different states and information associated with each of the states (e.g., identification that the interest rate was quoted, and the quoted value of the interest rate). The EIP analyzes the conversation and labels (e.g., “tags”) the text where the conversation associated with the label took place, such as, “An interest rate was provided.” The labels are customizable, so each client can define its own labels based on business needs. Further, the EIP extracts data from the conversation (e.g., the interest rate is “3%”).

Abstract: System and method including accessing a feature associated with a plurality of user identities (IDs); accessing a structure specifying mappings between the plurality of user IDs and a plurality of user canonical IDs; generating groups of feature values of the feature based on the mappings, each group of feature values being associated with a corresponding group of user IDs and with a corresponding user canonical ID; aggregating each group of feature values to calculate an aggregate feature value of the feature, each aggregate feature value associated with the corresponding user canonical ID; computing predictive traits associated with the plurality of user canonical IDs, the predictive traits including likelihoods of events or trait values, the computation of the predictive traits using the aggregate feature values associated with the corresponding user canonical IDs; and causing display, at a user interface (UI) of a computing device, of the computed predictive traits.

Abstract: Methods, systems, and computer programs are presented for classifying information in conversations and extracting information from the conversations. An Engagement Intelligence Platform (EIP) analyzes transcripts of conversations to find different states and information associated with each of the states (e.g., identification that the interest rate was quoted, and the quoted value of the interest rate). The EIP analyzes the conversation and labels (e.g., “tags”) the text where the conversation associated with the label took place, such as, “An interest rate was provided.” The labels are customizable, so each client can define its own labels based on business needs. Further, the EIP extracts data from the conversation (e.g., the interest rate is “3%”).

Abstract: Methods, systems, and computer programs are presented for abstractive summarization of text by viewing sequence transduction as a language modeling problem. One method comprises an operation for training a machine-learning program to create a machine-learning model that estimates a word to be added to a running summary for the text being summarized. The method further comprises operations for detecting the text to be summarized, initializing the running summary, and performing a plurality of iterations. Each iteration comprises providing, to the machine-learning model, the source text and the running summary, and adding, using the machine-learning model, a new word to the running summary. Further, the method comprises an operation for storing, on a memory, the running summary as the summary of the text.

Abstract: Methods, systems, and computer programs are presented for abstractive summarization of text by viewing sequence transduction as a language modeling problem. One method comprises an operation for training a machine-learning program to create a machine-learning model that estimates a word to be added to a running summary for the text being summarized. The method further comprises operations for detecting the text to be summarized, initializing the running summary, and performing a plurality of iterations. Each iteration comprises providing, to the machine-learning model, the source text and the running summary, and adding, using the machine-learning model, a new word to the running summary. Further, the method comprises an operation for storing, on a memory, the running summary as the summary of the text.

Abstract: Methods, systems, and computer programs are presented for formatting raw text. One method includes an operation for accessing raw text comprising words corresponding to one or more sentences. The raw text is lowercase text without any punctuation. Further, the method includes operations for creating a plurality of sub-words corresponding to the raw text, and for generating, by a machine-learning (ML) model, an output for each sub-word based on the created sub-words. The output for each sub-word indicates a formatting operation for the corresponding sub-word. The method further includes an operation for generating, based on the formatting operations in the outputs for the sub-words, formatted text corresponding to the raw text. The formatted text is text with correct grammar, proper punctuation, and proper capitalization according to a meaning of words spoken by a speaker associated with the raw text.

Abstract: Methods, systems, and computer programs are presented for formatting raw text. One method includes an operation for accessing raw text comprising words corresponding to one or more sentences. The raw text is lowercase text without any punctuation. Further, the method includes operations for creating a plurality of sub-words corresponding to the raw text, and for generating, by a machine-learning (ML) model, an output for each sub-word based on the created sub-words. The output for each sub-word indicates a formatting operation for the corresponding sub-word. The method further includes an operation for generating, based on the formatting operations in the outputs for the sub-words, formatted text corresponding to the raw text. The formatted text is text with correct grammar, proper punctuation, and proper capitalization according to a meaning of words spoken by a speaker associated with the raw text.

Abstract: Methods, systems, and computer programs are presented for abstractive summarization of text by viewing sequence transduction as a language modeling problem. One method comprises an operation for training a machine-learning program to create a machine-learning model that estimates a word to be added to a running summary for the text being summarized. The method further comprises operations for detecting the text to be summarized, initializing the running summary, and performing a plurality of iterations. Each iteration comprises providing, to the machine-learning model, the source text and the running summary, and adding, using the machine-learning model, a new word to the running summary. Further, the method comprises an operation for storing, on a memory, the running summary as the summary of the text.

Abstract: Methods, systems, and computer programs are presented for formatting raw text. One method includes an operation for accessing raw text comprising words corresponding to one or more sentences. The raw text is lowercase text without any punctuation. Further, the method includes operations for creating a plurality of sub-words corresponding to the raw text, and for generating, by a machine-learning (ML) model, an output for each sub-word based on the created sub-words. The output for each sub-word indicates a formatting operation for the corresponding sub-word. The method further includes an operation for generating, based on the formatting operations in the outputs for the sub-words, formatted text corresponding to the raw text. The formatted text is text with correct grammar, proper punctuation, and proper capitalization according to a meaning of words spoken by a speaker associated with the raw text.

Abstract: Methods, systems, and computer programs are presented for abstractive summarization of text by viewing sequence transduction as a language modeling problem. One method comprises an operation for training a machine-learning program to create a machine-learning model that estimates a word to be added to a running summary for the text being summarized. The method further comprises operations for detecting the text to be summarized, initializing the running summary, and performing a plurality of iterations. Each iteration comprises providing, to the machine-learning model, the source text and the running summary, and adding, using the machine-learning model, a new word to the running summary. Further, the method comprises an operation for storing, on a memory, the running summary as the summary of the text.

Abstract: Methods, systems, and computer programs are presented for classifying information in conversations and extracting information from the conversations. An Engagement Intelligence Platform (EIP) analyzes transcripts of conversations to find different states and information associated with each of the states (e.g., identification that the interest rate was quoted, and the quoted value of the interest rate). The EIP analyzes the conversation and labels (e.g., “tags”) the text where the conversation associated with the label took place, such as, “An interest rate was provided.” The labels are customizable, so each client can define its own labels based on business needs. Further, the EIP extracts data from the conversation (e.g., the interest rate is “3%”).

Abstract: Methods, systems, and computer programs are presented for searching the content of voice conversations. The conversations are translated into text and analysis of the conversation is performed to identify information in the conversation. The information identified includes turns taking data in the conversation and states identified within each state. A powerful user interface (UI) is provided to review the conversations and add annotations that tag the different turns. Additionally, parameter values are extracted from the text. A powerful search engine is provided with multiple search options, such as searching for text, searching by state within the conversation, searching by parameters extracted from the conversation, or a combination thereof.

Abstract: Methods, systems, and computer programs are presented for abstractive summarization of text by viewing sequence transduction as a language modeling problem. One method comprises an operation for training a machine-learning program to create a machine-learning model that estimates a word to be added to a running summary for the text being summarized. The method further comprises operations for detecting the text to be summarized, initializing the running summary, and performing a plurality of iterations. Each iteration comprises providing, to the machine-learning model, the source text and the running summary, and adding, using the machine-learning model, a new word to the running summary. Further, the method comprises an operation for storing, on a memory, the running summary as the summary of the text.