Abstract: Methods to provide selective supporting evidence processing by applying a first machine learning (ML) model to a first candidate answer to generate a first confidence score that does not consider supporting evidence for the first candidate answer, determining, from a second ML model, an expected contribution of processing supporting evidence for the first candidate answer, and upon determining that the expected contribution does not exceed a specified threshold, skipping supporting evidence processing for the first candidate answer.

Abstract: Systems and computer program products to provide selective supporting evidence processing by applying a first machine learning (ML) model to a first candidate answer to generate a first confidence score that does not consider supporting evidence for the first candidate answer, determining, from a second ML model, an expected contribution of processing supporting evidence for the first candidate answer, and upon determining that the expected contribution does not exceed a specified threshold, skipping supporting evidence processing for the first candidate answer.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving a case, generating a set of candidate answers for the case, excluding a first candidate answer from the set of candidate answers upon determining that a first attribute in the case precludes returning the first candidate answer as a valid response to the case, and processing supporting evidence for the remaining candidate answers in the set of candidate answers.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving a case from a user, determining a level of sophistication of the user, determining a level of sophistication associated with each of a plurality of items of supporting evidence in a corpus of information, selecting a subset of the plurality of items of supporting evidence based on the determined levels of sophistication, and returning the selected subset to the user as part of a response to the case.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving a case, generating a set of candidate answers for the case, excluding a first candidate answer from the set of candidate answers upon determining that a first attribute in the case precludes returning the first candidate answer as a valid response to the case, and processing supporting evidence for the remaining candidate answers in the set of candidate answers.

Abstract: Systems and computer program products to provide selective supporting evidence processing by applying a first machine learning (ML) model to a first candidate answer to generate a first confidence score that does not consider supporting evidence for the first candidate answer, determining, from a second ML model, an expected contribution of processing supporting evidence for the first candidate answer, and upon determining that the expected contribution does not exceed a specified threshold, skipping supporting evidence processing for the first candidate answer.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving a case from a user, determining a level of sophistication of the user, determining a level of sophistication associated with each of a plurality of items of supporting evidence in a corpus of information, selecting a subset of the plurality of items of supporting evidence based on the determined levels of sophistication, and returning the selected subset to the user as part of a response to the case.

Abstract: Methods to provide selective supporting evidence processing by applying a first machine learning (ML) model to a first candidate answer to generate a first confidence score that does not consider supporting evidence for the first candidate answer, determining, from a second ML model, an expected contribution of processing supporting evidence for the first candidate answer, and upon determining that the expected contribution does not exceed a specified threshold, skipping supporting evidence processing for the first candidate answer.

Abstract: In an embodiment, a guest operating system receives first host network information that identifies a host virtual network adapter. The first host network information uniquely identifies the host virtual network adapter. The guest operating system sends a first frame that comprises the first host network information to a guest virtual network adapter. The guest virtual network adapter sends the first frame to the host virtual network adapter. The guest operating system receives second host network information that identifies the host virtual network adapter. The second host network information uniquely identifies the host virtual network adapter. The guest operating system sends a second frame that comprises the second host network information to the guest virtual network adapter. The guest virtual network adapter sends the second frame to the host virtual network adapter identified by the second host network information in the second frame.

Abstract: In an embodiment, a guest operating system receives first host network information that identifies a host virtual network adapter. The first host network information uniquely identifies the host virtual network adapter. The guest operating system sends a first frame that comprises the first host network information to a guest virtual network adapter. The guest virtual network adapter sends the first frame to the host virtual network adapter. The guest operating system receives second host network information that identifies the host virtual network adapter. The second host network information uniquely identifies the host virtual network adapter. The guest operating system sends a second frame that comprises the second host network information to the guest virtual network adapter. The guest virtual network adapter sends the second frame to the host virtual network adapter identified by the second host network information in the second frame.

Abstract: Techniques are disclosed for designing information technology products by influencing design of a product using a set of criteria. Each of the criteria may have one or more attributes, and may be different in priority from one another. The criteria are preferably directed toward ensuring, and/or improving, the product's acceptance by its target marketplace or market segment. In preferred embodiments, information that defines “best practices” for various ones of the criteria is gathered during a product assessment, and this information provided to product teams to be used in product design decisions.

Abstract: Techniques are disclosed for assessing information technology products by comparing a product (including a product still under development) to a set of criteria. Each of the criteria may have one or more attributes, and may be different in priority from one another. The comparison is preferably directed toward ensuring, and/or improving, the product's acceptance by its target marketplace or market segment. In preferred embodiments, a product assessment score is created as a result of the comparison. When necessary, a set of recommendations for product changes is also created. The criteria/attributes may be prioritized in view of their importance to the target marketplace or market segment, and the assessment results are preferably provided to product teams to influence the importance of product planning and/or development efforts.