Abstract: In some embodiments, the instant invention provides for specifically programming a computer machine to perform at lease: receiving, from a trader, a passive indication of interest (IOI) for a financial instrument, where the passive IOI is a bid or an offer and a resting liquidity; classifying the trader as at least: a trading type that is subject to a decline ratio calculation for crossing the passive IOIs, where the decline ratio calculation identifies how many eligible aggressive IOIs have been previously declined by the trader out of a total number of all eligible aggressive IOIs that were offered to such trader; receiving, from another trader, another IOI that is an aggressive IOI, where the aggressive IOI is available to be immediately crossed at the price; and determining, in real-time, an allocation of the aggressive IOI to the trader submitted the passive IOI based on the decline ratio of the trader.

Abstract: In some embodiments, the present invention provides for a computer-implemented method, including: causing, by a specifically programmed computer call management communication system, to transform, over a computer network, computing devices of users, into corresponding specialized call management devices, by having each computing device to execute a specialized call management client software application being in electronic communication with the specifically programmed computer call management communication system over the computer network by utilizing SIP; where the specialized call management client software application generates specialized graphical user interfaces configured to allow each user to concurrently initiate and maintain, over the computer network, a plurality of voice communications of distinct types with other users, by, for example, allowing each user to independently and dynamically divert, in real-time, any voice communication of any type to any audio device associated with a corresponding

Abstract: In some embodiments, the instant invention provides for specifically programming a computer machine to perform at lease: receiving, from a trader, a passive indication of interest (IOI) for a financial instrument, where the passive IOI is a bid or an offer and a resting liquidity; classifying the trader as at least: a trading type that is subject to a decline ratio calculation for crossing the passive IOIs, where the decline ratio calculation identifies how many eligible aggressive IOIs have been previously declined by the trader out of a total number of all eligible aggressive IOIs that were offered to such trader; receiving, from another trader, another IOI that is an aggressive IOI, where the aggressive IOI is available to be immediately crossed at the price; and determining, in real-time, an allocation of the aggressive IOI to the trader submitted the passive IOI based on the decline ratio of the trader.

Abstract: In some embodiments, the present invention provides for an exemplary computer system which includes at least the following components: an adaptive self-trained computer engine programmed, during a training stage, to electronically receive an initial speech audio data generated by a microphone of a computing device; dynamically segment the initial speech audio data and the corresponding initial text into a plurality of user phonemes; dynamically associate a plurality of first timestamps with the plurality of user-specific subject-specific phonemes; and, during a transcription stage, electronically receive to-be-transcribed speech audio data of at least one user; dynamically split the to-be transcribed speech audio data into a plurality of to-be-transcribed speech audio segments; dynamically assigning each timestamped to-be-transcribed speech audio segment to a particular core of the multi-core processor; and dynamically transcribing, in parallel, the plurality of timestamped to-be-transcribed speech audio segm

Abstract: In some embodiments, the present invention provides for a computer-implemented method, including: causing, by a specifically programmed computer call management communication system, to transform, over a computer network, computing devices of users, into corresponding specialized call management devices, by having each computing device to execute a specialized call management client software application being in electronic communication with the specifically programmed computer call management communication system over the computer network by utilizing SIP; where the specialized call management client software application generates specialized graphical user interfaces configured to allow each user to concurrently initiate and maintain, over the computer network, a plurality of voice communications of distinct types with other users, by, for example, allowing each user to independently and dynamically divert, in real-time, any voice communication of any type to any audio device associated with a corresponding

Abstract: Systems and methods herein provide for establishing a subjective viewpoint in text. In one embodiment, a method includes identifying intents and metrics in each of a plurality of texts, calculating a sentiment score for each text based on the identified intents and metrics of each text, and calculating a disfluency score for each text to weight the sentiment score of each text. The method also includes training the machine learning model with the texts, and processing a subsequent text through the trained machine learning model to determine a sentiment score of the subsequent text.

Abstract: Systems and methods herein provide for establishing a subjective viewpoint in text. In one embodiment, a method includes identifying intents and metrics in each of a plurality of texts, calculating a sentiment score for each text based on the identified intents and metrics of each text, and calculating a disfluency score for each text to weight the sentiment score of each text. The method also includes training the machine learning model with the texts, and processing a subsequent text through the trained machine learning model to determine a sentiment score of the subsequent text.

Abstract: Systems and methods for using machine learning models to predict an outcome of a product test are described. According to certain aspects, an electronic device may calculate, based on a received set of small-scale results as a first input to a first machine learning model of a plurality of machine learning models, a first result predicting an outcome of the product tested according to the large-scale product test. The electronic device may then calculate, based on the set of small-scale results as a second input to at least one second machine learning model of the plurality of machine learning models, a second result predicting the outcome of the product tested according to the large-scale product test. The electronic device may then predict an outcome of the large-scale product test based at least on the first result and the second result.

Abstract: Systems and methods for using machine learning models to predict an outcome of a product test are described. According to certain aspects, a method may include: obtaining a first set of results of a large-scale cable fire test; obtaining a second set of results of a small-scale cable fire test; cleaning the first set of results and the second set of results; inputting the set of cleaned data into each machine learning model of a plurality of machine learning models to determine a machine learning model that is most accurate; obtaining an additional set of results of the small-scale cable fire test on an additional small-scale cable; inputting the additional set of results into the most accurate machine learning model; and after inputting the additional set of results into the most accurate machine learning model, outputting a result from the most accurate machine learning model.

Abstract: Systems and methods for using machine learning models to predict an outcome of a product test are described. According to certain aspects, an electronic device may generate a machine learning model using training data indicating a set of results of a set of products tested according to a specific product test. The electronic device may access a set of inputs comprising a set of additional results of at least one additional product tested according to a modified product test, and input the set of inputs into the machine learning model. Based on an output of the machine learning model, the electronic device may predict whether the at least one additional product would pass or comply with the original specific product test.

Abstract: According to an embodiment, a method includes receiving audio data and providing the audio data as input to a first machine learning model to produce transcription data. The audio data is provided as input to a second machine learning model to produce speaker separation data, and the transcription data is segmented based on the speaker separation data to produce speaker separated transcription data. A portion of the speaker separated transcription data is provided as input to a third machine learning model to identify personal identifiable information (PII) text in the portion of the speaker separated transcription data, the portion being associated with a speaker from a plurality of speakers. The method also includes replacing the PII text with redaction text in the portion of the speaker separated transcription data and causing display of the transcription data including the redaction text at a user compute device.

Abstract: Systems and methods for using machine learning models to predict an outcome of a product test are described. According to certain aspects, an electronic device may calculate, based on a received set of small-scale results as a first input to a first machine learning model of a plurality of machine learning models, a first result predicting an outcome of the product tested according to the large-scale product test. The electronic device may then calculate, based on the set of small-scale results as a second input to at least one second machine learning model of the plurality of machine learning models, a second result predicting the outcome of the product tested according to the large-scale product test. The electronic device may then predict an outcome of the large-scale product test based at least on the first result and the second result.

Abstract: In some embodiments, the instant invention provides for specifically programming a computer machine to perform at lease: receiving, from a trader, a passive indication of interest (IOI) for a financial instrument, where the passive IOI is a bid or an offer and a resting liquidity; classifying the trader as at least: a trading type that is subject to a decline ratio calculation for crossing the passive IOIs, where the decline ratio calculation identifies how many eligible aggressive IOIs have been previously declined by the trader out of a total number of all eligible aggressive IOIs that were offered to such trader; receiving, from another trader, another IOI that is an aggressive IOI, where the aggressive IOI is available to be immediately crossed at the price; and determining, in real-time, an allocation of the aggressive IOI to the trader submitted the passive IOI based on the decline ratio of the trader.

Abstract: In some embodiments, the instant invention provides for specifically programming a computer machine to perform at lease: receiving, from a trader, a passive indication of interest (IOI) for a financial instrument, where the passive IOI is a bid or an offer and a resting liquidity; classifying the trader as at least: a trading type that is subject to a decline ratio calculation for crossing the passive IOIs, where the decline ratio calculation identifies how many eligible aggressive IOIs have been previously declined by the trader out of a total number of all eligible aggressive IOIs that were offered to such trader; receiving, from another trader, another IOI that is an aggressive IOI, where the aggressive IOI is available to be immediately crossed at the price; and determining, in real-time, an allocation of the aggressive IOI to the trader submitted the passive IOI based on the decline ratio of the trader.

Abstract: Natural language processing can analyze language in apparatus, systems, and methods involving audio, including from body camera video footage, which can be transcribed to extract an audio segment from a video track, to identify starting and ending timestamps of voice, to transcribe the at least one audio segment to identify and separate audio of at least a first speaker, and to score the audio of the first speaker to identify interactions of interest. Audio could be analyzed and scored to record verbal performance, respectfulness, wellness, etc. and speakers from the audio can be detected. Evaluations of officer behavior can be labeled by a machine and confirmed by a human reviewer to identify critical events and justification. Voice printing can be used to identify individual speakers and the audio segments can be weighted to identify key phrases. Positive and negative language in an interaction can be identified using natural language processing.

Abstract: In some embodiments, the present invention provides for a computer-implemented method, including: causing, by a specifically programmed computer call management communication system, to transform, over a computer network, computing devices of users, into corresponding specialized call management devices, by having each computing device to execute a specialized call management client software application being in electronic communication with the specifically programmed computer call management communication system over the computer network by utilizing SIP; where the specialized call management client software application generates specialized graphical user interfaces configured to allow each user to concurrently initiate and maintain, over the computer network, a plurality of voice communications of distinct types with other users, by, for example, allowing each user to independently and dynamically divert, in real-time, any voice communication of any type to any audio device associated with a corresponding

Abstract: In some embodiments, the present invention provides for a computer-implemented method, including: causing, by a specifically programmed computer call management communication system, to transform, over a computer network, computing devices of users, into corresponding specialized call management devices, by having each computing device to execute a specialized call management client software application being in electronic communication with the specifically programmed computer call management communication system over the computer network by utilizing SIP; where the specialized call management client software application generates specialized graphical user interfaces configured to allow each user to concurrently initiate and maintain, over the computer network, a plurality of voice communications of distinct types with other users, by, for example, allowing each user to independently and dynamically divert, in real-time, any voice communication of any type to any audio device associated with a corresponding

Abstract: In some embodiments, the instant invention provides for specifically programming a computer machine to perform at lease: receiving, from a trader, a passive indication of interest (IOI) for a financial instrument, where the passive IOI is a bid or an offer and a resting liquidity; classifying the trader as at least: a trading type that is subject to a decline ratio calculation for crossing the passive IOIs, where the decline ratio calculation identifies how many eligible aggressive IOIs have been previously declined by the trader out of a total number of all eligible aggressive IOIs that were offered to such trader; receiving, from another trader, another IOI that is an aggressive IOI, where the aggressive IOI is available to be immediately crossed at the price; and determining, in real-time, an allocation of the aggressive IOI to the trader submitted the passive IOI based on the decline ratio of the trader.

Abstract: In some embodiments, the instant invention provides for specifically programming a computer machine to perform at lease: receiving, from a trader, a passive indication of interest (IOI) for a financial instrument, where the passive IOI is a bid or an offer and a resting liquidity; classifying the trader as at least: a trading type that is subject to a decline ratio calculation for crossing the passive IOIs, where the decline ratio calculation identifies how many eligible aggressive IOIs have been previously declined by the trader out of a total number of all eligible aggressive IOIs that were offered to such trader; receiving, from another trader, another TOT that is an aggressive IOI where the aggressive IOI is available to be immediately crossed at the price; and determining, in real-time, an allocation of the aggressive IOI to the trader submitted the passive TOT based on the decline ratio of the trader.

Abstract: In some embodiments, the present invention provides for a computer-implemented method, including: causing, by a specifically programmed computer call management communication system, to transform, over a computer network, computing devices of users, into corresponding specialized call management devices, by having each computing device to execute a specialized call management client software application being in electronic communication with the specifically programmed computer call management communication system over the computer network by utilizing SIP; where the specialized call management client software application generates specialized graphical user interfaces configured to allow each user to concurrently initiate and maintain, over the computer network, a plurality of voice communications of distinct types with other users, by, for example, allowing each user to independently and dynamically divert, in real-time, any voice communication of any type to any audio device associated with a corresponding