CONDUENT BUSINESS SERVICES, LLC

A method and system provide for a hands-free access mechanism. The method includes locating a mobile device using a first radio access technology. The method further includes measuring a signal strength using the first technology and determining that the signal strength meets a predetermined signal strength threshold in order to trigger a location process using a second technology having a more precise location capability than the first technology. The method may further assess environmental conditions upon determining a particular precise location and trigger an access sequence upon detecting the mobile device in a selected position. The access sequence triggered may be impacted by the environmental conditions.

A method and apparatus for automatically accessing a barrier or an electronic system can involve reading from a memory of a contactless device, a received signal strength information (RSSI) threshold associated with a user of the contactless device. An RSSI can be compared with the RSSI threshold to determine when the contactless device is within an acceptable distance from a barrier. The barrier can be opened for passage beyond the barrier and/or the electronic system can be accessed when the RSSI exceeds the RSSI threshold.

A system includes an input interface receiving conversation input from at least one system participant. A natural language processor operates on the conversational input. A sentiment processor operates on the pre-processed input using a sentiment classification model to predict sentiment, the sentiment processor predicting positive, neutral, and negative sentiment for the pre-processed input. An emotion processor receiving the pre-processed input from the natural language processor and a corresponding sentiment from the sentiment processor and operates to predict an emotion class and a strength of the emotion based on the corresponding sentiment. An output mechanism generates sentiment strength for the conversation input based on the emotion class and strength of the emotion.

Methods and systems of license plate recognition can involve subjecting an image captured by an image capturing device to image-processing by a group of different license plate recognition engines including a license plate recognition engine and a license plate reidentification engine, and using a decision tree to combine data from the license plate recognition engine and the license plate reidentification engine and generate a license plate identifier based on the data processed by the decision tree.

Methods and systems for implementing a tariff within a Mobility as a Service (MaaS) platform, can involve assembling a plurality of factors for tariff determination, the plurality of factors comprising tariff data that is true, reliable, optimized, and performative; invoking an algorithm of choice embedded within the MaaS platform to process the plurality of factors; and generating a tailored tariff for a user based on the processed plurality of factors by the algorithm of choice embedded within the MaaS platform, wherein the tailored tariff is customized to suit the individual needs and usage patterns of the user within the MaaS platform.

A method and system provide for a hands-free access mechanism. The method includes locating a mobile device using a first radio access technology. The method further includes measuring a signal strength using the first technology and determining that the signal strength meets a predetermined signal strength threshold in order to trigger a location process using a second technology having a more precise location capability than the first technology. The method may further assess environmental conditions upon determining a particular precise location and trigger an access sequence upon detecting the mobile device in a selected position. The access sequence triggered may be impacted by the environmental conditions.

Automated systems and methods are provided for processing speech, comprising obtaining a digitally-encoded speech representation corresponding to a telecommunication interaction, wherein the digitally-encoded speech representation includes at least one of a voice recording or a transcript derived from audio of the telecommunication interaction; obtaining a digitally-encoded data set corresponding to at least one structured feature of the telecommunication interaction; obtaining a reference set, wherein the reference set includes a set of binary-classified existing satisfaction classifications; obtaining a trained machine learning algorithm, wherein the machine learning algorithm has been trained using a first plurality of reference telecommunication interactions which include user-provided satisfaction scores; extracting a feature set from the digitally-encoded speech representation; and by the machine learning algorithm and based on at least one structured feature and the feature set, generating a predicted satisfaction classification for the telecommunication interaction.

Methods and systems for manual-based image review can involve associating an image with a group of keyword utterances, the image displayable in a display screen of a computing device, the group of keyword utterances including different keyword utterances. A prompt for the user to utter a keyword utterance can be displayed in a first area of the image in the display screen and another prompt for the user to utter another keyword utterance can be displayed in another area of the image in the display screen. Audio of the keyword utterances displayed in the display screen can be captured and processed by natural language processing (NLP), when uttered by the user. The utterances can be displayed respectively as text in the first area and the other area of the image in response to processing by NLP of the audio. Thus, instead of users typing in the results and changing their focus between screen and keyboard, for example, the user can speak to the results, which increases the throughput of the results.

Automated systems and methods are provided for processing speech, comprising obtaining a trained machine learning model that has been trained using a cumulative historical data structure corresponding to at least one digitally-encoded speech representation for a plurality of telecommunications interactions conducted by a plurality of agent-side participants, which includes a first data corresponding to a score variable and a second data corresponding to a plurality of driver variables; applying the trained machine learning model: to a subset of data in the cumulative historical data structure that corresponds to a first agent-side participant of the plurality of agent-side participants, to generate a performance classification score and/or a performance direction classification score, to identify an intervention-target agent-side participant from among the plurality of agent-side participants, and to the cumulative historical data structure to identify an intervention training plan; and conducting at least one training session according to the intervention training plan.

An access control device includes a primary access barrier configured move between a closed position and an open position. The primary access barrier may be configured to extend into an access passage of the access control device to prevent access through the access passage when in the closed position and to move away from the access passage to permit access through the access passage when in an open position. An entry access barrier is positionable in the access passage of the access control device on a first side of the primary access barrier, and an exit barrier is positionable in the access passage of the access control device on a second side of the primary access barrier opposite the first side. Systems and methods relate to access control devices.