Abstract: Artificial Intelligence (“AI”) apparatus and method are provided that correlate and consolidate operation of discrete vendor tools for detecting cyberthreats on a network. An AI engine may filter false positives and eliminate duplicates within cyberthreats detected by multiple vendor tools. The AI engine provides machine learning solutions to complexities associated with translating vendor-specific cyberthreats to known cyberthreats. The AI engine may ingest data generated by the multiple vendor tools. The AI engine may classify hardware devices or software applications scanned by each vendor tool. The AI engine may decommission vendor tools that provide redundant cyberthreat detection. The AI engine may display operational results on a dashboard directing cyberthreat defense teams to corroborated cyberthreats and away from false positives.

Abstract: A method of implementing a scenario library includes generating a first sourcing event scenario using a scenario library and based on a first request received from a first client device. The method also includes storing the first sourcing event scenario as one of the plurality of sourcing event scenarios at the scenario library. The method further includes modifying a sourcing event template for generating the sourcing event by at least adding a reference to the first sourcing event scenario to the sourcing event template. The method further includes generating the sourcing event using the modified sourcing event template and based on a second request from one of the plurality of client devices. The method further includes displaying, in response to the second request, the generated possible combination of suppliers for the sourcing event. Related systems and articles of manufacture are provided.

Abstract: Artificial Intelligence (“AI”) apparatus and method are provided that correlate and consolidate operation of discrete vendor tools for detecting cyberthreats on a network. An AI engine may filter false positives and eliminate duplicates within cyberthreats detected by multiple vendor tools. The AI engine provides machine learning solutions to complexities associated with translating vendor-specific cyberthreats to known cyberthreats. The AI engine may ingest data generated by the multiple vendor tools. The AI engine may classify hardware devices or software applications scanned by each vendor tool. The AI engine may decommission vendor tools that provide redundant cyberthreat detection. The AI engine may display operational results on a dashboard directing cyberthreat defense teams to corroborated cyberthreats and away from false positives.

Abstract: Artificial Intelligence (“AI”) apparatus and method are provided that correlate and consolidate operation of discrete vendor tools for detecting cyberthreats on a network. An AI engine may filter false positives and eliminate duplicates within cyberthreats detected by multiple vendor tools. The AI engine provides machine learning solutions to complexities associated with translating vendor-specific cyberthreats to known cyberthreats. The AI engine may ingest data generated by the multiple vendor tools. The AI engine may classify hardware devices or software applications scanned by each vendor tool. The AI engine may decommission vendor tools that provide redundant cyberthreat detection. The AI engine may display operational results on a dashboard directing cyberthreat defense teams to corroborated cyberthreats and away from false positives.

Abstract: Artificial Intelligence (“AI”) apparatus and method are provided that correlate and consolidate operation of discrete vendor tools for detecting cyberthreats on a network. An AI engine may filter false positives and eliminate duplicates within cyberthreats detected by multiple vendor tools. The AI engine provides machine learning solutions to complexities associated with translating vendor-specific cyberthreats to known cyberthreats. The AI engine may ingest data generated by the multiple vendor tools. The AI engine may classify hardware devices or software applications scanned by each vendor tool. The AI engine may decommission vendor tools that provide redundant cyberthreat detection. The AI engine may display operational results on a dashboard directing cyberthreat defense teams to corroborated cyberthreats and away from false positives.

Abstract: Artificial Intelligence (“AI”) apparatus and method are provided that correlate and consolidate operation of discrete vendor tools for detecting cyberthreats on a network. An AI engine may filter false positives and eliminate duplicates within cyberthreats detected by multiple vendor tools. The AI engine provides machine learning solutions to complexities associated with translating vendor-specific cyberthreats to known cyberthreats. The AI engine may ingest data generated by the multiple vendor tools. The AI engine may classify hardware devices or software applications scanned by each vendor tool. The AI engine may decommission vendor tools that provide redundant cyberthreat detection. The AI engine may display operational results on a dashboard directing cyberthreat defense teams to corroborated cyberthreats and away from false positives.

Abstract: Artificial Intelligence (“AI”) apparatus and method are provided that correlate and consolidate operation of discrete vendor tools for detecting cyberthreats on a network. An AI engine may filter false positives and eliminate duplicates within cyberthreats detected by multiple vendor tools. The AI engine provides machine learning solutions to complexities associated with translating vendor-specific cyberthreats to known cyberthreats. The AI engine may ingest data generated by the multiple vendor tools. The AI engine may classify hardware devices or software applications scanned by each vendor tool. The AI engine may decommission vendor tools that provide redundant cyberthreat detection. The AI engine may display operational results on a dashboard directing cyberthreat defense teams to corroborated cyberthreats and away from false positives.

Abstract: Artificial Intelligence (“AI”) apparatus and method are provided that correlate and consolidate operation of discrete vendor tools for detecting cyberthreats on a network. An AI engine may filter false positives and eliminate duplicates within cyberthreats detected by multiple vendor tools. The AI engine provides machine learning solutions to complexities associated with translating vendor-specific cyberthreats to known cyberthreats. The AI engine may ingest data generated by the multiple vendor tools. The AI engine may classify hardware devices or software applications scanned by each vendor tool. The AI engine may decommission vendor tools that provide redundant cyberthreat detection. The AI engine may display operational results on a dashboard directing cyberthreat defense teams to corroborated cyberthreats and away from false positives.

Abstract: Artificial Intelligence (“AI”) apparatus and method are provided that correlate and consolidate operation of discrete vendor tools for detecting cyberthreats on a network. An AI engine may filter false positives and eliminate duplicates within cyberthreats detected by multiple vendor tools. The AI engine provides machine learning solutions to complexities associated with translating vendor-specific cyberthreats to known cyberthreats. The AI engine may ingest data generated by the multiple vendor tools. The AI engine may classify hardware devices or software applications scanned by each vendor tool. The AI engine may decommission vendor tools that provide redundant cyberthreat detection. The AI engine may display operational results on a dashboard directing cyberthreat defense teams to corroborated cyberthreats and away from false positives.

Abstract: Some embodiments provide a program. The program receives from a client device a sourcing request specifying a plurality of variables comprising set of sources, a set of items, and a set of quantities associated with the items. The program further receives a set of offers from the set of sources. Each offer in the set of offers specifies an item in set of items, a price associated with the item, and a quantity associated with the item. The program also receives a selection of a set of defined scenarios for the sourcing event and the set of offers. The program further generates a set of linear programming models based on the set of defined scenarios the sourcing and the set of offers. The program also instructs solvers to solve the liner programming models.

Abstract: A system and method for just-in-time embedded watermarking of streaming digital content. The system employs sequential processing to transcode streaming content to embed a user-specific watermark into the streaming content. Additionally, the just-in-time embedded watermarking system transcodes short segments of content on an as-needed basis, in response to user requests, instead of performing bulk transcoding of large amounts of content at once. Accordingly, the just-in-time embedded watermarking system provides consistent and predictable user playback experience. Further, the system automatically supports adaptive bit rate optimization by providing interoperability with multiple potential adaptive bit rates requestable by the content player device. The system watermarks the requested content segment in real-time during the streaming of the content by causing burn-in of the user-specific watermark into the streaming content.

Abstract: Some embodiments provide a program. The program receives from a client device a sourcing request specifying a plurality of variables comprising set of sources, a set of items, and a set of quantities associated with the items. The program further receives a set of offers from the set of sources. Each offer in the set of offers specifies an item in set of items, a price associated with the item, and a quantity associated with the item. The program also receives a selection of a set of defined scenarios for the sourcing event and the set of offers. The program further generates a set of linear programming models based on the set of defined scenarios the sourcing and the set of offers. The program also instructs solvers to solve the liner programming models.

Abstract: Some embodiments provide a program. The program receives from a client device a request specifying a plurality of variables comprising set of sources and a set of objects. The program further receives a set of values from the set of sources. Each value in the set of values specifies an object in set of objects. The program also receives a selection of a set of defined scenarios for the request and the set of values. The program further generates a set of linear programming models based on the set of defined scenarios, the request, and the set of values.

Abstract: A system and method for just-in-time embedded watermarking of streaming digital content. The system employs sequential processing to transcode streaming content to embed a user-specific watermark into the streaming content. Additionally, the just-in-time embedded watermarking system transcodes short segments of content on an as-needed basis, in response to user requests, instead of performing bulk transcoding of large amounts of content at once. Accordingly, the just-in-time embedded watermarking system provides consistent and predictable user playback experience. Further, the system automatically supports adaptive bit rate optimization by providing interoperability with multiple potential adaptive bit rates requestable by the content player device. The system watermarks the requested content segment in real-time during the streaming of the content by causing burn-in of the user-specific watermark into the streaming content.

Abstract: Some embodiments provide a program. The program receives from a client device a request specifying a plurality of variables comprising set of sources and a set of objects. The program further receives a set of values from the set of sources. Each value in the set of values specifies an object in set of objects. The program also receives a selection of a set of defined scenarios for the request and the set of values. The program further generates a set of linear programming models based on the set of defined scenarios, the request, and the set of values.