Fmr Corp.

Tredence Inc.

Ankush Chopra

Aravind Chandramouli

Abhishek Desai

Debasis Bal

Shubham Sharma

Shruti Agrawal

Shubham Pandey

Siddharth Shukla

Shiven Purohit

Ashutosh Rajesh Kothiwala

Paarth Ajay Shrivastava

Sohom Ghosh

Soumendra Mohanty

Mohammed Sameer Khan

Embodiments include a method. The method includes receiving, by the computer system, a first input. The method includes generating, by at least using a language detection model, a set of predicted languages. The method includes generating, using different translation models, a different language translation of the first input for each language in the set of predicted languages. The method includes generating, using a first question-answer model, a set of answers. The method includes performing a distribution-based correction, by at least using a distribution source and at least one answer included in the set of answers, to determine the attribute represented by the arbitrary values included in the first input. The method includes causing a device to present a recommendation based at least in part on the attribute represented by the arbitrary values included in the first input.

Attribute extraction from text sources

An analytics system receives, from a client computing device, a request to generate a presentation. The analytics system accesses one or more feedback datasets of feedback data. The feedback data comprises unstructured data available from multiple data stores. The analytics system generates, for each feedback dataset, a respective feedback text and a respective sentiment score indicating a degree of negativity associated with the respective feedback text. For a combination of a plurality of generated feedback texts, the analytics system selects a set of themes based at least on a plurality of generated sentiment scores. Each sentiment score of the plurality of generated sentiment scores is associated with one of the plurality of generated feedback texts. The analytics system generates a presentation file that indicates the set of themes. The analytics system causes the presentation file to be transmitted to the client computing device.

Multi-channel feedback analytics for presentation generation

Methods and apparatuses are described for automated analysis of customer interaction text to generate customer intent information and a hierarchy of customer issues. A server captures computer text segments including a first portion comprising a transcript of an interaction and a second portion comprising notes about the interaction. The server generates interaction embeddings corresponding to the first portion of the computer text segment for a trained neural network. The server executes the neural network using the interaction embeddings to generate an interaction summary for each computer text segment. The server converts each interaction summary into a multidimensional vector and aggregates the multidimensional vectors into clusters based upon a similarity measure. The server aligns the clusters of vectors with attributes of the interaction summaries to generate a hierarchical mapping of customer issues.

Automated analysis of customer interaction text to generate customer intent information and hierarchy of customer issues

A computerized method of representing customer interactions with an organization includes: receiving, by a computing device, customer web interaction data segments and customer conversation data segments; pre-processing the customer conversation data segments to remove specified types of information; scoring each of the pre-processed customer conversation data segments; pre-processing the customer web data interaction segments; extracting from the pre-processed customer web interaction data segments tokens; combining the pre-processed customer conversation data segments and the pre-processed customer web interaction data segments into a customer data set; parsing the customer data set into one or more windows; assigning, for each window, pre-trained weights to each of the tokens in each window; assigning a transaction theme to each window based on the tokens in each window; and generating, based on the transaction themes, a ranked list of topic keywords reflecting the customer web interaction data segments and customer conversation data segments.

Computer systems and methods for efficient query resolution by customer representatives

A computerized method is provided for automatically determining answers to a plurality of questions. The method includes automatically discovering a plurality of questions by processing historical data related to prior customer interactions. The automatically discovering includes applying a linguistic analytical model on the data related to historical customer interactions to detect the plurality of questions, vectoring the plurality of questions to generate mathematical representations of the questions, and grouping the plurality of questions into one or more clusters in accordance with similarities of the questions as measured based on their mathematical representations. The method also includes identifying the questions that do not have an existing answer. The method further includes determining at least one probable answer to each of the representative questions using a content mining technique that mines pertinent data from one or more identified content sources.

Content creation and prioritization

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Bengaluru, India

Ankush Chopra