International Business Machines Corporation

David Amid

David Boaz

Ateret Anaby-Tavor

Ofer Shir

Amir Kantor

Luis Alfonso Lastras-Montano

Tin Kam Ho

Inbal Ronen

Ofer Lavi

Neil Rohit Mallinar

Salil Ahuja

Ella Rabinovich

Segev Shlomov

Mitchell Mason

Ateret Anaby Tavor

Rama K T Akkiraju

Shahar Chen

Rotem Aharonov

Jason M Leonard

Jalal U Mahmud

Vibha Singhal Sinha

Thomas Guthrie Zimmerman

Rama Kalyani T Akkiraju

Oded Margalit

Peter Bak

Jerald Thomas Schoudt

Maria Nichole Schwenger

Collin James Murray

Dmitry Moor

Jose Francisco Martinez-Rivera

Alon Jacovi

Ori Bar El

Ateret Anaby - Tavor

Anna Guri

An example system includes a processor to receive a bot design and escalation logs associated with a chat bot configured based on the bot design. The processor can compute a similarity score between each of a number of bot response nodes in the bot design and the escalation logs. The processor can generate a sorted list of the bot response nodes in the bot design based on the similarity scores.

Adjusting chat bot design based on matched escalation logs

A computer product and methodology is contemplated for adaptively serving hold content during an automated communication session. This includes training a machine learning model with a set of training data that includes a feature of hold duration times during historical automated communication sessions. In response to encountering a computer transaction request during a new automated communication session, a hold time duration is predicted for executing the computer transaction request based on obtaining an inference from the machine learning model. Whether to serve hold content in response to the computer transaction request is determined, based on a comparison of the predicted hold time duration to a predetermined threshold time interval.

Adaptively serving hold content during an automated communication session

Automatic measurement of semantic textual similarity of conversations, by: receiving two conversation texts, each comprising a sequence of utterances; encoding each of the sequences of utterances into a corresponding sequence of semantic representations; computing a minimal edit distance between the sequences of semantic representations; and, based on the computation of the minimal edit distance, performing at least one of: quantifying a semantic similarity between the two conversation texts, and outputting an alignment of the two sequences of utterances with each other.

Automatic measurement of semantic similarity of conversations

The computer receives a group of conversation data associated with the escalation node, identifies agent responses in the conversation data, and clusters them into agent response types. The computer identifies dialog state feature value sets for the conversations. The computer identifies feature value set associations with response types, and generates, Boolean expressions representing the feature value sets associated with each of the response types. The computer makes a recommendation to add to at least one child node for the escalation node, with the child node corresponding to one of the response types. The child node has, as an entry condition, the Boolean expression for the response type to which the child node corresponds. The child node has as an action, which according to some aspects, provides a response representative of the cluster of agent responses for the response type to which the child node corresponds.

Computerized dialog system improvements based on conversation data

A method, apparatus, and system are provided for resolving conflicts between training data conflicts by retrieving independent training data sets, each comprising a plurality of intents and end-user utterances for use in training one or more classifiers to recognize a corresponding intent from one or more of the end-user utterances, providing a first test end-user utterance associated with a first intent from the first independent training data set to the one or more classifiers to select an output intent generated by the one or more classifiers; identifying a first conflict when the first intent does not match the output intent, and automatically generating, by the system, one or more conflict resolution recommendations for display and selection by an end user to resolve the first conflict.

Method, apparatus, and system for conflict detection and resolution for competing intent classifiers in modular conversation system

An intermediary server receives a resource request from a client computer for a requested resource that has a desired resource ability to optimize the client computer. The intermediary server identifies an alternative resource that has the desired resource ability to optimize the client computer, and determines that an ability of the alternative resource to provide the desired resource ability exceeds an ability of the requested resource to provide the desired resource ability. In response to determining that the ability of the alternative resource to provide the desired resource ability exceeds the ability of the requested resource to provide the desired resource ability, the intermediary server instructs a resource server to send the alternative resource instead of the requested resource to the client computer.

Providing an optimal resource to a client computer via interactive dialog

A method comprising operating a computerized chatbot to: calculate first and second scores representing a relevance of input received from a user to functionalities provided by respective first and second modules, respectively, of the chatbot; associate the first and second modules with respective first and second names; introducing the modules to the user using their associated names; selecting a module to interact with the user based on at least one of: a name mentioned by the user and a score and switching between the first and second modules based on the names.

System and method for operating a chatbot

An intermediary server receives a resource request for a requested resource from a client computer. The intermediary server responds to the resource request by initiating an interactive dialog between the intermediary server and the client computer, where the interactive dialog identifies a desired resource ability of the requested resource. The intermediary server identifies, based on responses to the interactive dialog from the client computer, an alternative resource that best meets the desired resource ability, wherein the requested resource and the alternative resource are a same type of resource, and wherein the interactive dialog uses an interactive exchange that interprets interactive dialog responses from the client computer in order to identify the desired resource ability and the alternative resource that best meets the desired resource ability and then instructs a resource server to send the alternative resource to the client computer.

A method of selecting a group from a plurality of multi objective designs which comply with a plurality of objectives. The method comprises providing a plurality of multi objective designs, each the multi objective design having a plurality of multi objective design objective values which comply with at least one constraint of a Pareto Frontier of an objective space of a plurality of objectives, selecting a group from the plurality of multi objective designs, each member of the group is selected according to a match between at least one objective of respective the plurality of objectives and at least one of a respective gain threshold and a respective loss threshold, and outputting the group.

Multi objective design selection

A method, system, and apparatus are provided for resolving conflicts between training data conflicts by retrieving independent training data sets, each comprising a plurality of intents and end-user utterances for use in training one or more classifiers to recognize a corresponding intent from one or more of the end-user utterances, providing a first test end-user utterance associated with a first intent from the first independent training data set to the one or more classifiers to select an output intent generated by the one or more classifiers; identifying a first conflict when the first intent does not match the output intent, and automatically generating, by the system, one or more conflict resolution recommendations for display and selection by an end user to resolve the first conflict.

A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a personalized interactive decision support system. A customer profile generator executing within the personalized interactive decision support system determines a set of traits for at least one customer based on at least one user-generated content source. A personalized product recommendation module executing within the personalized interactive decision support system matches the at least one customer to at least one product within a set of products based on a consumption preferences-to-product attribute mapping data structure to form at least one product recommendation. Each product in the set of products has an associated set of attributes and wherein the consumption preferences-to-product attribute mapping data structure correlates customer traits and consumption preferences and correlates consumption preferences and product attributes. A visual and interactive decision support module executing within the personalized interactive decision support system presents the at least one product recommendation.

Interactive decision support based on preferences derived from user-generated content sources

A method, apparatus and product for automated multi-objective solution selection. The method comprises obtaining a set of solutions to a multi-objective problem, wherein each solution is a non-dominated solution. The method further comprises computing outranking weights for each pair of solutions and selecting a subset of the set of solutions based upon a defensive criteria in with respect to the outranking weights, wherein the defensive criteria relates to a number of solutions that outrank the subset. The method further comprises outputting the subset of the set of solutions.

Automated multi-objective solution selection

A method comprising using at least one hardware processor for: receiving a multi-objective optimization problem; projecting a Pareto frontier of candidate solutions for said multi-objective optimization problem to a hyperplane; decomposing said hyperplane into multiple Voronoi regions each associated with a candidate solution of said candidate solutions; determining a robustness degree for each candidate solution of said candidate solutions, by computing a hypervolume for each region of said multiple Voronoi regions; and ranking said candidate solutions based on the robustness degree.

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Kiryat Ata, Israel

David Amid