The Multidimensional Machine Learning Data and User Interface Segment Tagging Engine Apparatuses, Methods and Systems (“MLUI”) transforms ambient condition data, sales data, user interface selections, cognitive intelligence question input inputs via MLUI components into project projections, campaigns, user interface visualizations, cognitive intelligence question output outputs. A category identifier selection is obtained via a category selection interaction interface mechanism. Entity segment identifier selections are obtained via entity segment selection interaction interface mechanisms. A set of visualization cognitive intelligence (CI) datapoint identifiers is determined as CI datapoint identifiers associated with each combination of a selected entity segment identifier and the selected category identifier. CI datapoint values corresponding to the set of visualization CI datapoint identifiers are retrieved from a NoSQL database configured to act as cache for generating visualizations based on metrics calculated using survey data. A visualization is generated using the retrieved CI datapoint values.

Some embodiments of the present disclosure relate to systems, devices, and methods for making recommendations to users for, individually or together, goods, services, and information/media (e.g., audio, video, publications), as well as verifying the veracity of data/information, based upon an emotional impact of such goods/services and/or media/information on the user, other users, and/or other individuals, as well some embodiments directed to systems, devices, and methods for treating one or more brain functions, including neurological conditions and episodes associated therewith by determining (and optionally providing) media (e.g., audio, but in some embodiments, any of audio, textual information, and video), which may be referred to as optimal medical media (“OMM”).

Some embodiments of the present disclosure relate to systems, devices, and methods for making recommendations to users for, individually or together, goods, services, and information/media (e.g., audio, video, publications), as well as verifying the veracity of data/information, based upon an emotional impact of such goods/services and/or media/information on the user, other users, and/or other individuals, as well some embodiments directed to systems, devices, and methods for treating one or more brain functions, including neurological conditions and episodes associated therewith by determining (and optionally providing) media (e.g., audio, but in some embodiments, any of audio, textual information, and video), which may be referred to as optimal medical media (“OMM”).

Methods, apparatus, systems and articles of manufacture are disclosed to estimate an audience population. An apparatus includes a characteristic identifier to determine whether respective ones of respondents are associated with a characteristic, a respondent identifier to determine whether the respective ones of the respondents are recaptured based on a comparison of a person identifier corresponding to respondents and a database of previously identified person identifiers, a count determiner to, in response to the respective ones of the respondents exhibiting the characteristic, increase a total capture count by one and, in response to detecting unique instances of the respective ones of the respondents exhibiting the characteristic, increase a unique capture count by one, and a population estimator to, in response to a recapture probability satisfying a threshold, determine a population estimate having the characteristic based on the total capture count, the unique capture count, and a number of available samples.

Systems and methods for Artificial Intelligence (AI)-based analysis of oral reviews are provided. An example method includes prompting a user to provide an oral review concerning a subject; providing the user with an interface configured to receive the oral review; receiving, via the interface, the oral review concerning the subject in a free format; generating, based on the oral review, a text for review and presenting the text for review to the user; and providing, to the user, an option to publish the text for review via at least one social media. Generating the text for review may include removing filler words from the oral review and converting the oral review from the free format to a format according to a grammar rule of at least one human language.

A system and method for a podcast community that creates an avenue for listeners to provide feedback to podcasters. These solutions include websites, web applications, mobile applications and algorithms that connect people listening to podcasts around the world. The mobile application creates a forum where podcast listeners can listen and comment on podcast episodes. Listeners can interact with each other, podcasters and featured guests on the applications. Users and podcasters can also attach media files to comments and podcasts respectively.

An apparatus (10) for navigating a hierarchical electronic questionnaire (12) includes at least one electronic processor (20) programmed to: provide (102) a user interface (UI) (27) via which: at least a portion of the hierarchical electronic questionnaire is presented on a display device (24) represented as a tree-based questionnaire map (30) with a plurality of nodes (32) connected by a plurality of branches (34), the nodes of the plurality of nodes corresponding to questions; a user input is received, via at least one user input device (22), indicative of a selection of at least one of the nodes; and the question corresponding to the selected node is displayed and a user input indicative of an answer to the question corresponding to the selected node is received via the at least one user input device; and populate (104) one or more nodes other than the selected node based on the selected node and/or the answer to the question corresponding to the selected node.

Methods, apparatus, systems and articles of manufacture for near real time out of home audience measurement are disclosed. An example apparatus includes at least one memory; instructions; and processor circuitry to execute the instructions to at least: receive a first data transmission request at a first portable meter; send a second data transmission request from the first portable meter to a second portable meter; determine whether the first portable meter is capable of transmitting at least one data packet, based at least in part on an indication the second portable meter is capable of transmitting the at least one data packet; and in response to determining the first portable meter is capable of transmitting the at least one data packet, transmit the at least one data packet.

Aspects of the disclosure relate to training a machine learning model to continuously sustain security assessment protocols on vendor computing devices. In some embodiments, a machine learning engine may analyze vendor compliance data from previous security assessments, compliance requirements for security gaps that are commonly reported during security assessments, and enterprise security requirements. A security assessment platform may generate and transmit, to a vendor computing device, a plurality of security assessment surveys and instructions for completing a security assessment survey. The machine learning engine may analyze the responses provided on the completed security assessment survey. The security assessment platform may transmit either a notification of compliance or a notification of non-compliance to the enterprise organization. The security assessment platform may continuously perform security assessments, depending on the security risk level of the vendor, to ensure the vendor complies with the prescribed security requirements.

A method, apparatus and system for creating an asset reliability model for at least one physical asset includes receiving data related to the at least one physical asset from at least a selection of at least one data source from which to receive the data related to the at least one physical asset, receiving a selection of at least one type of model to be created, receiving a respective response to at least one guided task or choice configured to prepare the received data, and creating an asset reliability model for the at least one physical asset using a machine learning process based on the received data related to the at least one physical asset, the selection of the model to be created, and the respective response to the at least one guided task or choice.