Abstract: Arrangements for providing software vulnerability analysis and monitoring are provided. In some aspects, software bill of materials (SBOM) data may be received and software attributes may be extracted from the SBOM data. Author data may be received and analyzed using natural language processing and/or machine learning to identify author attributes. Current event or vulnerability data may be received. In some examples, one or more machine learning models may be executed to determine a confidence score associated with the software being analyzed. For instance, software attributes, author attributes, and current event data may be used as inputs in the machine learning model and a confidence score may be output. Based on the confidence score, one or more alerts may be generated and transmitted to one or more enterprise organization computing devices.

Abstract: Arrangements for providing software vulnerability analysis and monitoring are provided. In some aspects, software bill of materials (SBOM) data may be received and software attributes may be extracted from the SBOM data. Author data may be received and analyzed using natural language processing and/or machine learning to identify author attributes. Current event or vulnerability data may be received. In some examples, one or more machine learning models may be executed to determine a confidence score associated with the software being analyzed. For instance, software attributes, author attributes, and current event data may be used as inputs in the machine learning model and a confidence score may be output. Based on the confidence score, one or more alerts may be generated and transmitted to one or more enterprise organization computing devices.

Abstract: Arrangements for providing software vulnerability analysis and monitoring are provided. In some aspects, software bill of materials (SBOM) data may be received and software attributes may be extracted from the SBOM data. Author data may be received and analyzed using natural language processing and/or machine learning to identify author attributes. Current event or vulnerability data may be received. In some examples, one or more machine learning models may be executed to determine a confidence score associated with the software being analyzed. For instance, software attributes, author attributes, and current event data may be used as inputs in the machine learning model and a confidence score may be output. Based on the confidence score, one or more alerts may be generated and transmitted to one or more enterprise organization computing devices.

Abstract: A method and apparatus maintains, during non-session conditions and on a per user basis, concurrent multimodal session status information of user agent programs configured for different concurrent modality communication during the same session, and re-establish a concurrent multimodal session in response to accessing the concurrent multimodal session status information.

Abstract: A multimodal network element facilitates concurrent multimodal communication sessions through differing user agent programs on one or more devices. For example, a user agent program communicating in a voice mode, such as a voice browser in a voice gateway that includes a speech engine and call/session termination, is synchronized with another user agent program operating in a different modality, such as a graphical browser on a mobile device. The plurality of user agent programs are operatively coupled with a content server during a session to enable concurrent multimodal interaction.

Abstract: A method and apparatus, during a session, analyze fetched modality specific instructions for at least one modality associated with a first user agent program to determine if the modality specific instructions include a concurrent multimodal tag (CMMT); and if detected, provide modality specific instructions for at least a second user agent program operating in a different modality, based on the concurrent multimodal tag. Synchronization of output from the first and second user agent programs is carried out based on the modality specific instructions.

Abstract: A multimodal network element facilitates concurrent multimodal communication sessions through differing user agent programs on one or more devices. For example, a user agent program communicating in a voice mode, such as a voice browser in a voice gateway that includes a speech engine and call/session termination, is synchronized with another user agent program operating in a different modality, such as a graphical browser on a mobile device. The plurality of user agent programs are operatively coupled with a content server during a session to enable concurrent multimodal interaction.

Abstract: A method and apparatus maintains, during non-session conditions and on a per user basis, concurrent multimodal session status information of user agent programs configured for different concurrent modality communication during the same session, and re-establish a concurrent multimodal session in response to accessing the concurrent multimodal session status information.