Abstract: Examples of the disclosure are directed to systems and methods of using natural language processing techniques to automatically assign metadata to articles as they are published. The automatically-assigned metadata can then feed into the algorithms that calculate updated causation scores for agent-outcome hypotheses, powering live visualizations of the data that update automatically as new scientific articles become available.

Abstract: An agent including a human plasma immunoglobulin moiety, an immune cell surface receptor binding moiety capable of modulating the immune cell surface receptor, and optionally, a linker moiety linking the human plasma immunoglobulin moiety and the immune cell surface receptor binding moiety.

Abstract: Examples of the disclosure are directed to systems and methods of using natural language processing techniques to automatically assign metadata to articles as they are published. The automatically-assigned metadata can then feed into the algorithms that calculate updated causation scores for agent-outcome hypotheses, powering live visualizations of the data that update automatically as new scientific articles become available.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed to systems and methods of using natural language processing techniques to automatically assign metadata to articles as they are published. The automatically-assigned metadata can then feed into the algorithms that calculate updated causation scores for agent-outcome hypotheses, powering live visualizations of the data that update automatically as new scientific articles become available.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed to systems and methods of using natural language processing techniques to automatically assign metadata to articles as they are published. The automatically-assigned metadata can then feed into the algorithms that calculate updated causation scores for agent-outcome hypotheses, powering live visualizations of the data that update automatically as new scientific articles become available.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed to systems and methods of using natural language processing techniques to automatically assign metadata to articles as they are published. The automatically-assigned metadata can then feed into the algorithms that calculate updated causation scores for agent-outcome hypotheses, powering live visualizations of the data that update automatically as new scientific articles become available.

Abstract: Examples of the disclosure are directed to systems and methods of using natural language processing techniques to automatically assign metadata to articles as they are published. The automatically-assigned metadata can then feed into the algorithms that calculate updated causation scores for agent-outcome hypotheses, powering live visualizations of the data that update automatically as new scientific articles become available.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed to generating visualizations that illustrate various measures of risk associated with a litagion® agent in the context of entire industries or specific to a single party that produces or uses the litagion® agent. A Quindrex™ visualization can illustrate catastrophic risk presented by a risk agent, such as a litagion® agent. For example, the risk agent may be a chemical such as bisphenol A (BPA) or benzene. The visualization can include a plurality of portions, each corresponding to a metric of catastrophic risk associated with the risk agent visualized. A dartboard visualization can illustrate catastrophic risk presented by risk agents produced or used by a party, such as a company. The visualization can include a plurality of portions, such as wedges in a dartboard, each corresponding to a risk agent produced or used by the party.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

Abstract: The present invention relates to a system for detecting misuse of authorized access to a digital data gathering system by a user. User behavior is monitored as to the search queries, the search results, or both. When a valid record of normal user behavior is obtained, each new query and result for the user can be compared to the record to determine if anomalous activity has occurred.

Abstract: An intranet mediator for obtaining direct answers to natural language questions allowing users to search both a data warehouse of integrated/structured data sources and unstructured data sources. The intranet mediator allows the user to obtain an answer to a natural language question without having to surf the data sources in which the answer might be contained, or without being limited to one specific factual item return. The intranet mediator operates on the supposition that most answers to business queries are contained within structured data sources which have been integrated into the data warehouse thereby having common schema and known contents. Preselection of the most relevant data source(s) is thus possible before query output. Search of unstructured data is also performed for additional context surrounding either the question or the answer. A direct answer is given in response to the question.

Abstract: The present invention relates to a system for detecting misuse of authorized access to a digital data gathering system by a user. User behavior is monitored as to the search queries, the search results, or both. When a valid record of normal user behavior is obtained, each new query and result for the user can be compared to the record to determine if anomalous activity has occurred.

Abstract: An intranet mediator for obtaining direct answers to natural language questions allowing users to search both a data warehouse of integrated/structured data sources and unstructured data sources. The intranet mediator allows the user to obtain an answer to a natural language question without having to surf the data sources in which the answer might be contained, or without being limited to one specific factual item return. The intranet mediator operates on the supposition that most answers to business queries are contained within structured data sources which have been integrated into the data warehouse thereby having common schema and known contents. Preselection of the most relevant data source(s) is thus possible before query output. Search of unstructured data is also performed for additional context surrounding either the question or the answer. A direct answer is given in response to the question.