A quitting-intention estimating system facilitates detecting an entity's intention to quit an organization. During operation, the system obtains recent electronic-communication information associated with the entity under investigation, such as an employee of a corporation, or a volunteer of a non-profit organization. The system can determine the entity's quitting intention by determining a plurality of recent communication features from the electronic-communication information, for example, by using a variety of feature-extracting modules that extract the features from the entity's electronic communications. The system then computes a quitting-intention value for the entity under investigation, using a quitting-intention estimating function that takes the recent communication features as input. The quitting-intention estimating function detects a change in the entity's behavior from previous communication features, and generates a quitting-intention value which indicates a likelihood that the change in the entity's behavior corresponds to an intention to quit the organization.

A method, computer program product, and computing system for the sharing of experiences is provided. The method may include receiving content associated with a uniform resource locator and determining two or more content items based on the content associated with the uniform resource locator. The method may further include generating, based on the two or more content items, an experience data card and storing the generated experience data card in an experience data store, the experience data store being communicatively coupled to the one or more computing devices. The method may further include receiving a request to view the experience data card and providing for display, using the one or more computing devices, a view of the experience data card based at least in part on the request to view the experience data card.

A system includes: a processor; and a memory, wherein the memory stores instructions that, when executed by the processor, cause the processor to: retrieve a first log over a network from a first server of a plurality of servers for at least one contact center; generate an index of values in the first log; update an index database based on the index; and save the first log on a storage device.

Techniques for product recommendation involve receiving, from a user, a request for recommendation of a product matching one or more characteristics specified in the request. In response to the request, a product may be identified for recommendation, at least in part by searching an ontology in which the identified product is represented as matching at least one of the characteristics specified in the request. The ontology may further include at least one link to at least one natural language product review providing supporting evidence that the identified product matches the at least one characteristic. At least a portion of the at least one natural language product review may be retrieved using the at least one link in the ontology, and presented to the user in support of a recommendation of the identified product, in response to the request.

A system and method for determining whether a textual work submitted for publishing is machine generated or non-machine generated by identifying and quantifying various aspects of the textual work and comparing those aspects to known works. For example, the system and method may identify aspects of a textual work, including, a relationship between the sentences within the textual work, a writing style of the author of the textual work, a grammatical structure of the sentences within the textual work, a quality of the textual work, and other aspects of the textual work. Upon determining that the textual work is machine generated the textual work may be rejected for publishing.

A system, computer program product, and process are provided for an automated escalation agent. A receiver receives a request for language interpretation from a first language to a second language. Further, a database stores data associated with a plurality of language interpreters associated with a computer implemented language interpretation platform. A processor searches the database and determines that no online language interpreter is available to perform language interpretation at a time of the request and that escalates the request to determine if an offline language interpreter is available to initiate the language interpretation within a predetermined time period measured from the time of the request. An automated escalation agent module searches the database according to notification criteria, and sends at least one notification including the request to a plurality of offline language interpreters that meet the notification criteria.

The subject disclosure is directed towards processing evidence, which may include high-dimensional streaming evidence, into a future belief state. The existing evidence is used to project a belief about a future state. The future belief state may be used to determine whether to wait for additional evidence, or to act now without waiting for additional evidence, e.g., based on a cost of the delay. For example, an autonomous assistant may decide based upon the belief whether to engage a person or not, or to wait for more information before the engagement decision is made.

Examples of the disclosure describe user environment aware single channel acoustic noise reduction. A noisy signal received by a computing device is transformed and feature vectors of the received noisy signal are determined. The computing device accesses classification data corresponding to a plurality of user environments. The classification data for each user environment has associated therewith a noise model. A comparison is performed between the determined feature vectors and the accessed classification data to identify a current user environment. A noise level, a speech level, and a speech presence probability from the transformed noisy signal are estimated and the noise signal is reduced based on the estimates. The resulting signal is outputted as an enhanced signal with a reduced or eliminated noise signal.

In providing a help service, a server: receives a help request from a help seeker; matches the help request to potential helpers using a scoring model; sends a list of potential helpers to the help seeker; receives selections of potential helpers; sends the help request to each selected potential helper; receives from potential helper indication of willingness to respond to the help request; and sends to the help seeker information on the potential helpers. The server may further: search an external platform for posts seeking help; import the post to the help service; and send a response from potential helpers to post on the platform. The server may further: analyze keywords in the help request; determine it a candidate for sponsored answers; routes the help request to specialists; receives answers from specialists; and sends the answers to the help seeker. The server may further provide a safety net strength score.

Systems, computer-implemented methods, and media for updating content of a page composed of markup language in response to a triggering event method include: receiving a request that indicates a triggering event; determining the type of triggering event based on the request; determining at least one page element from amongst plural page elements of the page to be updated based on the request; generating a response to the request, the response including metadata indicating the at least one page element and the content of the at least one page element, the content of the at least one page element being composed of the markup language; and transmitting the response.