Abstract: A method, computer program product, and a system where a processor(s) generates a digital wardrobe for each user of a set of users. The processor(s) obtains a prospective theme(s) for a given event and a list of participants comprising a portion of the set of users. The processor(s) identify preferences, in the digital wardrobes of the portion of the set of users, relevant to each of the one or more prospective themes for the given event. The processor(s) determine, based on analyzing the relevant preferences in the digital wardrobes of the portion of the set of users, if a consensus exists in the relevant preferences of the portion of the set of users, where the consensus represents a given prospective theme, where the respective relevant preferences for the given prospective theme are aligned across the portion of the set of users. Based on determining that the consensus exists, the processor(s) generates an event theme for the given event, wherein the event theme is the consensus.

Abstract: Personnel evaluation based on crowdsourcing concepts. An approach to personnel evaluation is described that uses crowd-sourced data and an iterative approach. Individual personnel evaluators are themselves evaluated and assigned weighting factors based on their acumen and accuracy with regard to personnel evaluation. Personnel evaluations are provided by each personnel evaluator, who collectively form a crowdsourcing crowd, and weighted according to each evaluator's assigned weighting factor. The crowd-sourced weighted-averages from all evaluators are combined and used periodically and iteratively to evaluate the individual crowdsourcing participants and to re-calculate their individual weighting factors. The approach provides more accurate and more directed personnel evaluations as compared to simple arithmetic averaging of crowd-sourced data, and is improved with each iteration over time.

Abstract: A device may receive historical risk data identifying historical risks associated with entities, and historical compliance data identifying historical compliance actions performed by the entities. The device may train a machine learning model with the historical risk data and the historical compliance data to generate a structured semantic model, and may receive entity risk data identifying new and existing risks associated with an entity. The device may receive entity compliance data identifying new and existing compliance actions performed by the entity, and may process the entity risk data and the entity compliance data, with the structured semantic model, to determine risk and compliance insights for the entity. The risk and compliance insights may include insights associated with a key performance indicator, a compliance issue, a regulatory issue, an operational risk, a compliance risk, or a qualification of controls. The device may perform actions based on the risk and compliance insights.

Abstract: Techniques are described for determining that a property is vacant based on monitoring data collected by connected sensors and appliances near or within the property. In response to determining that a property is vacant, the condition of the vacant property can be monitored for various risks such as fire safety and security breaches. Monitoring techniques may also be used to determine when a new customer (e.g., a new tenant or property owner) has moved into the property. In response to detecting a move-in, one or more notifications may be transmitted to assist the new customer to customize a security system associated with the property. In this regard, techniques described herein may be used by service providers to improve the transition associated with move-in and move-out processes, which may also improve retention of monitoring services.

Abstract: Methods and systems for buyback provisioning. A risk level for each of a plurality of items at risk of losing market value is computed and the plurality of items at risk of losing market value are differentiated based on the corresponding risk levels. An identity of candidate substitute items is learned by communicating with a distribution system and one or more of the candidate substitute items to exchange for one of the plurality of items are identified based on one or more contextual factors. An exchange of the item and at least one of the candidate substitute items is facilitated, the facilitating comprising highlighting the item on a graphical user interface to indicate that the item is losing value or is at risk of losing value.

Abstract: Techniques for risk evaluation include receiving, from a requesting entity, a request for monitoring target entities specifying a first identifier associated with each target entity and target entity information. The system generates a second identifier and a third identifier for each target entity and stores a mapping of the second identifiers to the first identifiers and the third identifiers, preventing the second identifiers from being provided to the requesting entity. The system monitors a periodically updated data set and determines risk metrics for the target entities, comparing each risk metric to a threshold value to identify target entities whose risk data indicates an insider threat. The system generates a third identifier for the identified target entities and provides the third identifiers to the requesting entity. Responsive to a request for a corresponding first identifier, the system identifies and provides the first and third identifiers to the requesting entity.

Abstract: A method for immutability of review verifications includes: storing a blockchain; receiving a review submission from a reviewer about an entity comprising feedback; identifying a review identifier unique to the review submission; generating a new review block for the blockchain including the review submission including the review identifier; receiving verification submissions from other reviewers about the review submission indicating positive or negative verification of the review submission; generating verification blocks for the blockchain that include the verification submissions; adding a trustworthiness block to the blockchain for the review once enough positive verifications are received; and adding a trustworthiness block to the blockchain for the reviewer once they have enough trustworthy reviews.

Abstract: Methods and systems described in this disclosure describe generating a travel smartlist. In some implementations, the system can receive a location of travel and a date associated with the travel. The system can generate objects based on the location of the travel and the date and can organize the objects by categories and timeframes, the timeframes being in relation to the date. The system can cause a graphical user interface to display objects associated with a first category horizontally and display objects associated with a second category horizontally below the objects associated with the first category when a first timeframe is selected.