Abstract: The present disclosure describes method and system for predicting the actual liability that is related to a certain event. An example embodiment of the disclosed technique may obtain a form of a demand for loss (DFL). Some example embodiments of the disclosed technique may predict the extend of loss (EoL) of a certain DFL that is related to event. Based on the predicted EoL and information retrieved from to the relevant bill-of-lading (BoL) an embodiment of the disclosed technique is configured to predict the actual liability that is related to that event. An example event can be a ship that was drowned.

Abstract: Examples are described of smart capacity workload routing. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is process and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model, and identifying an agent with capacity that is available to handle the requested user communication. A communication slot for the identified agent is activated and defined by the identified workload measurement. The request is then routed to the identified agent and updating available workload capacity in the system.

Abstract: The present disclosure describes method and system for determining the extent of loss (EoL) that is related to a certain shipment of a certain type of goods along a certain path of a journey. An example embodiment of the disclosed technique may obtain a form that describes a loss (FTDL). The FTDL comprises a plurality of parameters that are related to that shipment, parameters such as but not limited to type of the cargo, temperature, relative humidity, acceleration, vibration, season of the year, etc. Some example embodiments of the disclosed technique may express the EoL by two or more ranks.

Abstract: The present disclosure describes method and system for recommending on a certain alternative for transporting a certain type of goods from an origin to a destination, wherein the recommendation is based on the predicted extent of loss (EoL) that is related to each alternative. Some example embodiments of the disclosed technique may express the EoL by two or more ranks.

Abstract: Examples are described of smart capacity workload routing. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is process and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model, and identifying an agent with capacity that is available to handle the requested user communication. A communication slot for the identified agent is activated and defined by the identified workload measurement. The request is then routed to the identified agent and updating available workload capacity in the system.

Abstract: A cargo management system for handling cargo transportation is disclosed. The management system can be configured to obtain information regarding one or more alternative routes of a journey of a cargo-shipping-unit (CSU). The system comprises an operational recommending unit (ORU), which is configured to obtain one or more define targets that are related to the type of cargo that is carried by the CSU and to present the impact of each alternative route on each of the define targets.

Abstract: A cargo management system for handling cargo transportation is disclosed. The management system can be configured to determine the risk that is involved in a journey of a cargo-shipping-unit (CSU). The system comprises a Risk-Analyzer-Unit (RAU). The RAU can be configured to obtain one or more features of the journey of that CSU. Next the RAU can fetch a predictive model that can predict the likelihood that a demand for loss (DFL) will be filed. In addition the RAU can be configured to present the likelihood that a demand DFL will be filed as the risk that is associated with that journey of that CSU.

Abstract: Cargo management system for handling demand for loss (DFL) is disclosed. The management system is configured to obtain a DFL that is related to a certain journey, wherein the journey can comprise one or more segments. Based on analyzing the obtained DFL the system can recommend whether a filed DFL is for real occurred damages. In addition, the system can point on a suspected cause for the damage as well as the suspected segment of the journey in which the damage occurred.

Abstract: Systems and methods for dynamic communication routing based on consistency weighting and routing rules are disclosed. A computing device can receive a communication including content data. The communication can be stored in a queue position of a primary queue. For example, the primary queue can include a plurality of queue positions for storing communications. The communication can be retrieved from the queue position of the primary queue and analyzed. In some instances, analyzing can include parsing the content data for a keyword. A keyword can correspond to a secondary queue. When the keyword is identified in the communication, the communication can be stored in the secondary queue that corresponds to the keyword. A terminal device associated with the secondary queue can be identified. A retrieval request to access the communication from the secondary queue can be received, and the communication can be routed to the terminal device.

Abstract: Systems and methods for dynamic communication routing based on consistency weighting and routing rules are disclosed. A computing device can receive a communication including content data. The communication can be stored in a queue position of a primary queue. For example, the primary queue can include a plurality of queue positions for storing communications. The communication can be retrieved from the queue position of the primary queue and analyzed. In some instances, analyzing can include parsing the content data for a keyword. A keyword can correspond to a secondary queue. When the keyword is identified in the communication, the communication can be stored in the secondary queue that corresponds to the keyword. A terminal device associated with the secondary queue can be identified. A retrieval request to access the communication from the secondary queue can be received, and the communication can be routed to the terminal device.

Abstract: Systems and methods for determining predictive model types are provided. A method may include generating a predictive model for a web page of a web site, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein each object is associated with a predictive model. The method may include determining one or more predictive model types that are associated with the predictive model, determining one or more performance indicators that correspond to each determined predictive model type, wherein performance indicators represent one or more benefits to a website, selecting a predictive model type of the predictive model out of the one or more predictive model types, wherein the predictive model type is selected based on a performance indicator corresponding to the selected predictive model type, and determining a configuration of the web page using the selected predictive model type of the predictive model.

Abstract: Systems and methods for dynamic communication routing based on consistency weighting and routing rules are disclosed. A computing device can receive a communication including content data. The communication can be stored in a queue position of a primary queue. For example, the primary queue can include a plurality of queue positions for storing communications. The communication can be retrieved from the queue position of the primary queue and analyzed. In some instances, analyzing can include parsing the content data for a keyword. A keyword can correspond to a secondary queue. When the keyword is identified in the communication, the communication can be stored in the secondary queue that corresponds to the keyword. A terminal device associated with the secondary queue can be identified. A retrieval request to access the communication from the secondary queue can be received, and the communication can be routed to the terminal device.

Abstract: A system and method for providing targeted content to a surfer is disclosed. Exemplary embodiments of the present invention seek to provide novel solutions for determining which content object, taken from a group of content objects, will be best suited for presentation in association with a link on a web page that has been requested by a certain surfer. Exemplary types of content objects may comprise the text, topic, font, color or other attribute of an external advertisement or internal advertisement. Still other content objects may comprise the specific design of the object, an image, the design of the page in which the object is presented, etc. The decision process for selecting a content object can be based on predictive information that is associated with the request, such as a common HTTP request, or historical information about the surfer.

Abstract: Systems and methods for determining predictive model types are provided. A method may include generating a predictive model for a web page of a website, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein each object is associated with a predictive model. The method may include determining one or more predictive model types that are associated with the predictive model, determining one or more performance indicators that correspond to each determined predictive model type, wherein performance indicators represent one or more benefits to a website, selecting a predictive model type of the predictive model out of the one or more predictive model types, wherein the predictive model type is selected based on a performance indicator corresponding to the selected predictive model type, and determining a configuration of the web page using the selected predictive model type of the predictive model.

Abstract: System, methods, and computer-program products include receiving requests for a web page, retrieving predictive information related to the requests, and determining one or more predictive factors for an object presented with the web page, the one or more predictive factors being determined using the retrieved predictive information. The systems, methods, and computer-program products further include generating a plurality of predictive models for the object using the one or more predictive factors, determining a score for each predictive model, selecting a group of predictive models from the plurality of predictive models using the score of each predictive model in the group, and generating a representative predictive model for the object using the group of predictive models, the representative predictive model being associated with the object.

Abstract: Systems and methods for determining predictive model types are provided. A method may include generating a predictive model for a web page of a website, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein each object is associated with a predictive model. The method may include determining one or more predictive model types that are associated with the predictive model, determining one or more performance indicators that correspond to each determined predictive model type, wherein performance indicators represent one or more benefits to a website, selecting a predictive model type of the predictive model out of the one or more predictive model types, wherein the predictive model type is selected based on a performance indicator corresponding to the selected predictive model type, and determining a configuration of the web page using the selected predictive model type of the predictive model.

Abstract: In some embodiments, a set of user groups can be defined, with each group relating to a different webpage experience, user action, etc. Requests are assigned to one of the groups based on actual webpage presentation features and/or user actions. A group-specific model is generated for each group and translates user information to a preliminary result (e.g., a purchasing probability). A model combination includes a weighted combination of a set of available group-specific models. User information is processed using the model combination to generate a model result. The model result is evaluated to determine whether a requested webpage is to be customized in a particular manner and/or an opportunity is to be offered.

Abstract: Systems and methods for determining predictive model types are provided. A method may include generating a predictive model for a web page of a website, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein each object is associated with a predictive model. The method may include determining one or more predictive model types that are associated with the predictive model, determining one or more performance indicators that correspond to each determined predictive model type, wherein performance indicators represent one or more benefits to a website, selecting a predictive model type of the predictive model out of the one or more predictive model types, wherein the predictive model type is selected based on a performance indicator corresponding to the selected predictive model type, and determining a configuration of the web page using the selected predictive model type of the predictive model.

Abstract: A system and method for providing targeted content to a surfer is disclosed. Exemplary embodiments of the present invention seek to provide novel solutions for determining which content object, taken from a group of content objects, will be best suited for presentation in association with a link on a web page that has been requested by a certain surfer. Exemplary types of content objects may comprise the text, topic, font, color or other attribute of an external advertisement or internal advertisement. Still other content objects may comprise the specific design of the object, an image, the design of the page in which the object is presented, etc. The decision process for selecting a content object can be based on predictive information that is associated with the request, such as a common HTTP request, or historical information about the surfer.

Abstract: Providing targeted content to a surfer by determining which content object of a group of content objects, will be best suited for presentation in association with a link on a requested web page. Content objects may include the text, topic, font, color or other attribute of an external or internal advertisement, as well as the specific design of the object, an image, the design of the page in which the object is presented, etc. Selection of a content object can be based on predictive information that is associated with the request (i.e. day and time of receipt, IP address of request, etc.) or historical information about the surfer.