Amdocs Development Limited

Liat Taub Bahar

Shmuel Ur

Eran Yosef Paran

Sarit Chehanowitz

Yosef Asaf Hermush

Tsvi Haim Lev

As described herein, a system, method, and computer program are provided for dynamically generating assistance information for customer service agents. In use, presence of a customer at a physical retail store is identified. Additionally, information describing the customer is processed, using a machine learning model, to determine an expected outcome of an interaction with the customer occurring within the physical retail store. It is then determined that the customer is to be assisted by a customer service agent. Further, assistance information for the customer service agent is dynamically generated, based at least in part on the expected outcome of the interaction with the customer.

System, method, and computer program for dynamically generating assistance information for customer service agents

As described herein, a machine learning system, method, and computer program are provided for evaluation of customer service agents. In use, presence of a customer at retail store is identified. Additionally, information associated with the customer is processed, using a machine learning model, to determine an expected outcome of an interaction with the customer occurring within the retail store. Further, after the interaction by an agent of the retail store with the customer, an actual outcome of the interaction is determined. Still yet, the agent is evaluated by comparing the actual outcome with the expected outcome. A result of the evaluation is then output, such as for use in assigning the agent to future in-store customers.

Machine learning system, method, and computer program for evaluation of customer service agents

As described herein, a system, method, and computer program are provided for using shared customer data and artificial intelligence to predict customer classifications. A first system of a first business entity receives an artificial intelligence model generated using output of a secure multi-party computation applied to: a first schema of first customer data stored by the first system, and a second schema of second customer data stored by a second system of a second business entity. Additionally, the first system executes the artificial intelligence model on the first customer data stored by the first system to generate a predictor, the predictor configured to receive input and process the input to predict a classification for the input. Further, the first system distributes the predictor for use by the second system of the second business entity to predict at least one classification for the second customer data.

System, method, and computer program for using shared customer data and artificial intelligence to predict customer classifications

As described herein, a system, method, and computer program are provided for evaluating third party AI models. A request is received from an interested party to evaluate an AI model created by a third party, where the request includes a portion of private data of the interested party, and an expected output of the AI model when applied to the portion of private data of the interested party. The AI model created by the third party is received. The AI model is evaluated by: causing the AI model to process the portion of private data of the interested party, accessing an actual output of the AI model resulting from the processing, and comparing the actual output with the expected output to generate an evaluation of the AI model. The evaluation of the AI model is then reported to the interested party.

System, method, and computer program for evaluating third party artificial intelligence models

As described herein, a system, method, and computer program are provided for using moving network connected vehicles to deliver high level network connectivity. In use, a request for digital content to be provided to a requesting device is received, the requesting device being a residential network router or a mobile device of a user. Additionally, responsive to the request, one or more moving network connected vehicles available to obtain the digital content when moving in a vicinity of a network and to provide the digital content to the requesting device when moving in a vicinity of the requesting device are determined. Further, the one or more moving network connected vehicles are caused to obtain the digital content when moving in the vicinity of the network and to provide the digital content to the requesting device when moving in the vicinity of the requesting device.

System, method, and computer program for using moving network connected vehicles to deliver high level connectivity

As described herein, a machine learning system, method, and computer program are provided for making payment related customer predictions using remotely sourced data. A system of a communication service provider (CSP) identifies a customer of the CSP. Additionally, the system collects data from a plurality of data sources independent from the CSP, the data including telephone numbers and/or webpage URLs of other services providers that are associated with making payments. Further, the system processes the collected data to form input data indicating which of the telephone numbers were contacted by the customer and/or webpage URLs were accessed by the customer. Still yet, the system processes the input data using at least one machine learning algorithm to make at least one payment related prediction for the customer. Moreover, the system outputs the at least one payment related prediction made for the customer.

Machine learning system, method, and computer program for making payment related customer predictions using remotely sourced data

As described herein, a system, method, and computer program are provided for deriving user attention data. In use, user attention data is collected for a user. The user attention data includes first information describing content being viewed by a user on a first device, and second information describing user activity occurring on the first device and/or one or more second devices while the content is being viewed by the user on the first device. Further, the first information and the second information are processed, using a machine learning model, to predict a degree to which the user likes the content. Still yet, the prediction is output for use in making one or more content recommendations.

System, method, and computer program for using user attention data to make content recommendations

A system, method, and computer program product are provided for determining and marking on a document what may be of interest to a user. In operation, a system identifies at least one document of potential interest to at least one user. The system identifies interest points associated with the at least one document for the at least one user based on one or more of: previously identified interest points in the at least one document, previously identified interest points in other similar documents, and characteristics of the at least one document. Further, the system marks in the at least one document the identified interest points associated with the at least one document for easy viewing by the at least one user.

System, method, and computer program for determining and marking on a document what may be of interest to a user

A system, method, and computer program product are provided for automatic management of intent classification. In operation, an intent classification system identifies a conversation including a plurality of utterances for which to identify intent of a user associated with the plurality of utterances. The intent classification system identifies one or more actions performed in association with the plurality of utterances. Further, the intent classification system correlates at least one of the one or more actions to at least one of the plurality of utterances based on which of the one or more actions were performed as a result of the plurality of utterances. The intent classification system automatically determines an intent of the user based on the correlation of the at least one of the one or more actions to the at least one of the plurality of utterances. Additionally, the intent classification system stores an indication of the intent based on the at least one of the plurality of utterances. Moreover, the intent classification system associates one or more specific actions with the indication of intent such that subsequent utterances are capable of being used by the intent classification system to automatically identify an intent of one or more users and to automatically identify specific actions that should be performed based on the automatically identified intent of the one or more users.

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Kfar Sabba, Israel

Liat Taub Bahar