The present disclosure provides a method for providing cost efficient probable accommodation facilities in each of one or more area clusters. The method includes a first step to receive a first set of data associated with a plurality of organizations at a cluster identification system. The method includes another step to fetch a second set of data associated with a plurality of competitors. The method includes yet another step to obtain a set of rules associated with a plurality of parameters. The method includes yet another step to perform analysis of the first set of data, the second set of data, and the set of rules in real-time to identify the one or more area clusters in the geographical region based on the analysis of the first set of data and the second set of data with facilitation of the set of rules in real-time.

A computer-implemented method includes determining whether historical profile data structures are stored in a database with corresponding structured supplemental data and selected health care plan option identifiers of a set of health care plan option identifiers. The method includes generating historical feature vectors using the historical data structures stored in the database or generating the historical feature vectors using created sample profile data structures. The method includes training machine learning models using the generated historical feature vectors, selecting one of the machine learning models for use in generating recommendation outputs, presenting an interactive voice interface to an entity to generate audio questions and prompts for obtaining response data from the entity, classifying voice survey responses of the entity, and generating feature vectors. The method includes processing, using the selected machine learning model, the feature vectors to generate the recommendation outputs, and transforming a user interface to display the recommendation output.

A method for tracking receipt of a gift includes creating an online message, generating a link associated with a landing page, a reveal code to unlock the online message at the landing page. An anonymous sender creates an off-line gift and sends the gift, a link to the landing page, and the reveal code to one or more recipients, either together or in various combinations. When a recipient follows the link and enters the reveal code, the online message is displayed. The message may include the identify of the sender. The online message detects when the one or more recipients land on the page and collects data on the one or more recipients.

Systems and methods for on demand local commerce are described. One example embodiment includes a device gathering location information and product interest associated with clients and client devices. The system may use location information in determining that the first plurality of client devices are within a first geographic area during a first time period, and may further use the interest information in calculating an interest level for a first product. A threshold may be identified and used in determining that the interest level for the first product exceeds the threshold. When the calculated interest level exceeds the threshold, a local commerce action is initiated. In various embodiments, the local commerce action may be a live on demand auction at a particular location, an offer associated with a geofenced area, a sales location recommendation to a merchant, or any other such local commerce action.

The present disclosure relates to systems and methods for planning transportation routes. In one implementation, a method for simulating vehicle ridesharing is provided. The method may include receiving a first input of a geographical area and accessing map information of roadways in the geographical area. The method may also include receiving a second input indicative of at least one scenario of ridesharing demand in the geographical area and receiving a third input indicative of virtual vehicles designated to transport virtual passengers associated with the scenario. The method may further include initiating a transportation simulation of scenario to simulate rides of the virtual vehicles transporting the virtual passengers along the roadways. The method may also include determining, based on the transportation simulation, a performance level associated with the virtual vehicles and providing an output representative of the determined performance level.

A method for forecasting part sales, including collecting sales data for a part over a series of sales time periods and collecting activity data for a plurality of activity types over a series of activity time periods for a plurality of machines including the part. A mean activity time can be calculated for each activity type for each time period in the series of activity time periods based on the collected activity data. An activity probability density function of the mean activity times for each activity type is created and a machine learning model is trained using an expectation of activity derived from the probability density functions for each activity type and the collected sales data. Machine activity data for a set of machines can be fed into the trained model to derive a part sales probability density function for the set of machines.

To provide an information processing apparatus, an information processing method, and an information processing program capable of analyzing the motion of a person in more detail. An information processing apparatus includes an acquisition unit (200) that acquires at least a position and an orientation of a moving body, and an analysis unit (202) that generates a label indicating a motion of the moving body on the basis of the position and the orientation acquired by the acquisition unit and time when the acquisition unit acquired the position and the orientation.

Exemplary embodiments include a feedback-based system and methodology for suggesting or making special transfer recommendations in dynamic lightweight personalized analytics (DLPA). It focuses on when and how to make such transfers, and the actual transfer amounts, based on remittance customer behavior, local and global events, geolocation data and other relevant information. Exemplary embodiments include a process for identifying, minimizing, and leveraging the behavioral information that optimize customer satisfaction in conjunction with the key performance indicators (KPIs) used in quantifying success. Furthermore, it facilitates a small memory footprint and optimal computation in selecting such donations.

Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.

The present disclosure herein provides methods and systems for optimizing personalized hospitality offerings to suit based on the customer requirement. The present disclosure employs a bucket of prediction models, namely (i) pre-trained hotel prediction model for predicting one or more hotels present in a destination city, (ii) the pre-trained room prediction model for predicting the one or more vacant rooms from the one or more hotels, and (iii) the pre-trained ancillary services prediction model for the predicting the one or more ancillary services available for the one or more vacant rooms. Each prediction model is separately trained on the features obtained from the unstructured historical training data, using a feature extraction technique. The fluidic pricing mechanism is used to provide personalized hospitality offerings by determining the fluidic pricing and offers to multiple relevant ancillary service bundles which may suit mostly to the diverse customers.