Abstract: There is thus provided a computerized-method for calculating a Repeated-Information-Request (RIR) score of an interaction in a contact center, by which a related interaction-recording is filtered for evaluation. The computerized-method is operating in a computerized-system which includes a processor, a data-storage and a memory to store the data-storage. The processor is operating a RIR score calculation module. The operating of the RIR score calculation module includes: (i) retrieving from the data-storage an interaction-recording and an interaction-recording-length thereof; (ii) operating a module on the retrieved interaction-recording to count a number of requests to repeat information; (iii) calculating a RIR score according to the number of requests to repeat information and the interaction-recording-length; and (iv) storing the RIR score of the retrieved interaction-recording in the data-storage.

Abstract: A computerized-method for calculating an After-Call-Work (ACW) factor of an interaction in a contact center, by which a related recording may be filtered for evaluation is provided herein. The method includes an After-Call-Work (ACW) factor calculation module. The operating of the ACW factor calculation module includes: (i) receiving agent recording of the interaction. (ii) aggregating data fields associated with: (a) the interaction; and (b) the customer; (iii) retrieving ACW time of the interaction; (iv) forwarding the aggregated data fields to a machine learning model; (v) operating the machine learning model to calculate a predicted ACW time, based on the aggregated data fields; (vi) calculating an ACW factor based on the received time of ACW and the calculated predicted ACW time; and (vii) sending the calculated ACW factor to a platform by which the platform is preconfigured to distribute the interaction for evaluation, based on the ACW factor.

Abstract: The present disclosure discloses a method and system for dynamically processing financial transactions. The method includes receiving a transaction request including at least one unique identifier field to transfer payment into an account associated with a payment instrument and authorizing the transaction request based on one or more predetermined rules. The payment instrument is used at an electronic device by a merchant for transfer of the payment from a merchant account. Thereafter, the method includes generating a direct fund transfer message based on the at least one unique identifier field upon authorization of the transaction request and transmitting the direct fund transfer message to an issuer associated with the merchant account for authentication of the direct fund transfer message. Lastly, the method includes transferring dynamically the payment from the merchant account to the account associated with the payment instrument when the direct fund transfer message is authenticated by the issuer.

Abstract: Embodiments described herein provide an optimal communication channel recommendation engine by assessing whether the environment the customer is situated in is conducive to the channel selected by the customer. Specifically, the optimal channel recommendation engine obtains data artifacts indicative of ambient noise, motion, customer sentiment, network quality, customer focus, and/or the like to assess quality of the environment and recommend an optimal channel for the communication between the customer and the call agent. With the recommendation to switch to a different channel, the client component resumes communication with the new channel and retains the context of the interaction.

Abstract: A computerized-method for calculating an After-Call-Work (ACW) factor of an interaction in a contact center, by which a related recording may be filtered for evaluation is provided herein. The method includes an After-Call-Work (ACW) factor calculation module. The operating of the ACW factor calculation module includes: (i) receiving agent recording of the interaction; (ii) aggregating data fields associated with: (a) the interaction; and (b) the customer; (iii) retrieving ACW time of the interaction; (iv) forwarding the aggregated data fields to a machine learning model; (v) operating the machine learning model to calculate a predicted ACW time, based on the aggregated data fields; (vi) calculating an ACW factor based on the received time of ACW and the calculated predicted ACW time; and (vii) sending the calculated ACW factor to a platform by which the platform is preconfigured to distribute the interaction for evaluation, based on the ACW factor.

Abstract: A computerized-method for gauging agent's self-assessment effectiveness, is provided herein. The computerized-method includes for each interaction (i) operating a Self-assessment Consolidation module to calculate a confidence-interval for each data-point of one or more preconfigured data-points, and (ii) operating a Self-assessment Divergence Determinant (SDD) module. The operating of the SDD includes: retrieving one or more data-points of the interaction; for each data-point retrieving the confidence interval; setting a divergence-indicator as zero, when the data point is within the confidence-interval; setting the divergence-indicator as a subtraction of the data point from the calculated lower-bound, when the data-point is lower than the lower-bound of the confidence-interval; and setting the divergence-indicator as a subtraction of the calculated upper-bound from the data-point, when the data-point is greater than the upper-bound of the confidence-interval.

Abstract: A computerized-method for calculating an After-Call-Work (ACW) factor of an interaction in a contact center, by which a related recording may be filtered for evaluation is provided herein. The method includes an After-Call-Work (ACW) factor calculation module. The operating of the ACW factor calculation module includes: (i) receiving agent recording of the interaction; (ii) aggregating data fields associated with: (a) the interaction; and (b) the customer; (iii) retrieving ACW time of the interaction; (iv) forwarding the aggregated data fields to a machine learning model; (v) operating the machine learning model to calculate a predicted ACW time, based on the aggregated data fields; (vi) calculating an ACW factor based on the received time of ACW and the calculated predicted ACW time; and (vii) sending the calculated ACW factor to a platform by which the platform is preconfigured to distribute the interaction for evaluation, based on the ACW factor.

Abstract: A computerized-method for generating a machine-learning model to determine genuineness of customer feedback to filter-out, non-genuine agent recording segments from evaluation. The computerized-method includes generating a Genuineness Opinion Score (GOS) model. The generating of GOS model includes: (a) a data manipulation phase; (b) a data visualization and analysis phase, and (c) a feature augmentation phase for sorting the variables in a set of unique and relevant variables into two categories: estimated variables and anticipated variables. The estimated variables are used for calculation of a GOS of an interaction that is received in a contact center and the anticipated variables are used for calculation of a threshold of said GOS.

Abstract: A computerized-method using a cloud-based computing environment for improving client service, in a contact center is provided herein. The computerized-method includes retrieving a context of a query and a time-limit from a CTI event and attempting to retrieve data to evaluate average resolution time for the received context. When the data is found, comparing the evaluated average resolution time with the received time-limit and when the received time-limit is below the evaluated average resolution time, sending a delay notice and providing the client a menu of options for querying through other channels. When the data is not found, or when the received time-limit is above the evaluated average resolution time, presenting on an agent dashboard, the time-limit of the client and accordingly updating parameters in the agent dashboard dining the inbound call, thus, improving client service, by considering the time-limit of the client before the agent addresses a query.

Abstract: Embodiments described herein provide an optimal communication channel recommendation engine by assessing whether the environment the customer is situated in is conducive to the channel selected by the customer. Specifically, the optimal channel recommendation engine obtains data artifacts indicative of ambient noise, motion, customer sentiment, network quality, customer focus, and/or the like to assess quality of the environment and recommend an optimal channel for the communication between the customer and the call agent. With the recommendation to switch to a different channel, the client component resumes communication with the new channel and retains the context of the interaction.

Abstract: A computerized-method using a cloud-based computing environment for improving client service, in a contact center is provided herein. The computerized-method includes: retrieving a context of a query and a time-limit from a CTI event and attempting to retrieve data to evaluate average resolution time for the received context. When the data is found, comparing the evaluated average resolution time with the received time-limit and when the received time-limit is below the evaluated average resolution time, sending a delay notice and providing the client a menu of options for querying through other channels. When the data is not found, or when the received time-limit is above the evaluated average resolution time, presenting on an agent dashboard, the time-limit of the client and accordingly updating parameters in the agent dashboard during the inbound call, thus, improving client service, by considering the time-limit of the client before the agent addresses a query.

Abstract: A computerized-method for calculating a hold factor of an interaction in a call center, by which related agent recording segments may be filtered for evaluation is provided herein. The computerized-method include: operating a Hold Factor Calculation (HFC) model for an interaction. The HFC model include receiving agent recording segments of the interaction and then collecting data fields of: (i) skills of agent; and (ii) interaction metadata. Then, checking to determine if hold time has occurred in the received agent recording segments and when it is determined that hold time has occurred the HFC is: (a) calculating a hold-ratio; (b) calculating a conversation score based on the collected data fields; (c) dividing the calculated hold ratio by the calculated conversation score to yield a hold factor; and (d) sending the yielded hold factor to a quality planner microservice by which the quality planner is preconfigured to distribute the interaction for evaluation.

Abstract: A computer-implemented method for determining a hygiene condition of an interior space is described. The method has the steps of: a) obtaining relative abundance of at least one bacterium of human health concern in an interior space; b) generating a Microbial Index of Interior Space (“Microbial Index”) for the interior space based on the relative abundance; and c) displaying an output indicative of the Microbial Index for determining a hygiene condition of the interior space. The Microbial Index is characterized by a function of the relative abundance of the at least one bacterium of human health concern defined by F(?i=1NPi), wherein N: Number of bacteria of human health concern identified in a microbial community in the interior space; P: Relative abundance of i-th bacteria of human health concern in the microbial community.

Abstract: A volatile composition dispenser includes a housing having opposing first and second walls that are joined along their peripheries to one another. The first wall has an aperture and the second wall has a plurality of apertures. Within the housing, rests a volatile composition cartridge and a gap, which exists between the cartridge and a bottom surface of the housing, wherein said bottom surface is formed from a joinder of a base portion of one or both of the first and second walls, and wherein, upon activation, the exposure percentage of the dispenser is from about 40% to about 90%; wherein at least about 60% of the total surface area of the plurality of apertures results from individual apertures of said plurality that each have an area of at least 30 mm2 and an aspect ratio of at least about 1:2.

Abstract: The gel compositions as described herein, are less affected by changes in humidity, are readily formed into complex shapes, and can contain high loadings of the hydrophobic material, even when the material is not highly hydrophobic, and provide a more controlled release of the hydrophobic material.

Abstract: A volatile composition having: (i) greater than 30% of a scented hydrophobic material by weight of the volatile composition, wherein the scented hydrophobic material comprises one or more volatile compounds; and (ii) greater than 20% of a regulating scent intensity (RIS) composition by weight of the volatile composition. The RIS composition has at least two of: a first regulating scent intensity component (RIS component) having a high average vapor pressure (VP); a second RIS component having a medium average VP; and a third RIS component having a low average VP; The average VP of each of said one or more volatile compounds is lower than at least one of the high, medium and low average VPs of the first, second and third RIS components at the same temperature.

Abstract: A scent intensity regulating composition (RIS composition). The RIS composition includes at least two of: a first scent intensity regulating component (RIS component) having an average vapor pressure (VP) greater than 0.3 Torr at 25° C.; a second RIS component having an average VP from 0.07 to 0.3 Torr at 25° C.; and a third RIS component having an average VP from 0.0099 to 0.07 Torr at 25° C. Each of the first, second, and third RIS components is characterized by an Odor Detection Threshold (ODT) of greater than 20 ppb.

Abstract: A volatile composition dispenser includes a housing having opposing first and second walls that are joined along their peripheries to one another. The first wall has an aperture and the second wall has a plurality of apertures. Within the housing, rests a volatile composition cartridge and a gap, which exists between the cartridge and a bottom surface of the housing, wherein said bottom surface is formed from a joinder of a base portion of one or both of the first and second walls, and wherein, upon activation, the exposure percentage of the dispenser is from about 40% to about 90%; wherein at least about 60% of the total surface area of the plurality of apertures results from individual apertures of said plurality that each have an area of at least 30 mm2 and an aspect ratio of at least about 1:2.

Abstract: A computerized-method using a cloud-based computing environment for improving client service, in a contact center is provided herein. The computerized-method includes: retrieving a context of a query and a time-limit from a CTI event and attempting to retrieve data to evaluate average resolution time for the received context. When the data is found, comparing the evaluated average resolution time with the received time-limit and when the received time-limit is below the evaluated average resolution time, sending a delay notice and providing the client a menu of options for querying through other channels. When the data is not found, or when the received time-limit is above the evaluated average resolution time, presenting on an agent dashboard, the time-limit of the client and accordingly updating parameters in the agent dashboard during the inbound call, thus, improving client service, by considering the time-limit of the client before the agent addresses a query.

Abstract: The gel compositions as described herein, are less affected by changes in humidity, are readily formed into complex shapes, and can contain high loadings of the hydrophobic material, even when the material is not highly hydrophobic, and provide a more controlled release of the hydrophobic material. It is believed that the slow release is achieved by forming the gel to have a tight cross-linked network with small pore-size. The pore size is correlated to the average correlation length as measured using Small Angle X-Ray Scattering (SAXS). By limiting the average correlation length to less than 8 nm, preferably from 0.3 nm to 8 nm, more preferably from 0.3 nm to 4 nm, a long lasting release of the hydrophobic material is achieved, rather than a blooming effect, whereby the hydrophobic material is released in a short burst.