Abstract: Methods and systems provide for dynamically generated topic segments for a communication session. In one embodiment, the system connects to a communication session with a number of participants; receives a list of topics; receives a transcript of a conversation between the participants produced during the communication session, the transcript including timestamps for a number of utterances associated with speaking participants; for each topic in the list of topics, segments the utterances into one or more topic segments based on the topic; for each of the segments, classifies whether the topic segment is related to the topic, and transmits, to one or more client devices, a list of the topic segments for the communication session.

Abstract: Methods and systems provide for dynamically generated topic segments for a communication session. In one embodiment, the system connects to a communication session with a number of participants; receives a list of topics; receives a transcript of a conversation between the participants produced during the communication session, the transcript including timestamps for a number of utterances associated with speaking participants; for each topic in the list of topics, segments the utterances into one or more topic segments based on the topic; for each of the segments, classifies whether the topic segment is related to the topic, and transmits, to one or more client devices, a list of the topic segments for the communication session.

Abstract: Methods and systems provide for dynamically generated topic segments for a communication session. In one embodiment, the system connects to a communication session with a number of participants; receives a list of topics; receives a transcript of a conversation between the participants produced during the communication session, the transcript including timestamps for a number of utterances associated with speaking participants; for each topic in the list of topics, segments the utterances into one or more topic segments based on the topic; for each of the segments, classifies whether the topic segment is related to the topic, and transmits, to one or more client devices, a list of the topic segments for the communication session.

Abstract: A computer-implemented method comprises: storing a trained machine learning model, the machine learning model comprising a predictor sub-model and a smoothing sub-model, the machine learning model being trained based on segments of training genomic sequences that have known ancestral origins; receiving data representing an input genomic sequence of the subject, the input genomic sequence covering a plurality of segments including a plurality of single nucleotide polymorplasms (SNP) sites of the genome of the subject, wherein each segment comprises a sequence of SNP values at the SNP sites, each SNP value specifying a variant at the SNP site; determining, using the predictor sub-model and based on the data, an initial ancestral origin estimate of each segment of SNP values; and performing, by the smoothing sub-model for each segment, a smoothing operation over the initial ancestral origin estimates to obtain a final prediction result for the ancestral origin of the segment.