430 results about "Crowd sourcing" patented technology

Methods, systems and processor-readable media for extracting and classifying location information utilizing social media messages and/or data thereof. The social media messages can be sampled from a social media database and the messages filtered based on a heuristic rule. A geolocation entity from the unstructured social media messages can be extracted utilizing a geolocation entity extracting module. The messages with the geoentities can be uploaded onto a crowd sourcing platform to manually annotate the messages with a label. A text classification model can be built and learned from the label utilizing a machine learning algorithm and the messages can be classified by a location classifier in order to extract the user location. The user location can then be transformed into a geocode so that a spatial search can be enabled and the distance between the locations can be easily calculated.

A method and system for real-time analytics of sensor-based data is disclosed. Also disclosed is a Cloud-based Paltform-as-a-Service (PaaS) offering for sensor driven applications with services and features for their complete life-cycle management including prompt development, testing, deployment and so forth. The method of the present disclosure enables real-time tracking of various physical parameters and attributes related to smart-spaces using sensor devices implemented in the premises of the smart-space environment and using crowd-sourced user input data. Further, the parameters obtained are sent to the cloud-computing server, wherein the analytics are performed in real-time based on the obtained parameters.

A mobile computing implemented platform is provided that enables the generation, and delivery of highly relevant content to the mobile device of a driver. The content is filtered based on the current location and direction of the driver, and content is generated and filtered based a crowd sourced model where a plurality of users who are driving within a similar locations are linked to one another via the platform for improved discovery of traffic conditions, and improved determination of user interest based on collective interest of the plurality of users. Various intelligent features are enabled by the platform.

A method and system for crowdsourcing and managing contact and profile information of a user's contacts, and exchanging business and personal contact information through a mobile device, personal computer, or a web application. The system comprises a crowdsourcing intelligence module that provides the software, analysis, and algorithms for automatically populating and updating an individual's contact information in a user's address book based on contributed information and changes made to the individual's profile by a large community of users. The module also automatically populates and updates business profile, captures business's external social and business profiles, and analyzes demographic information which can then be transmitted to users. Users may also search for job opportunities, review and purchase products and services, review the location of contacts in proximity to the user, and manage sales and account activities including lead generation, lead qualification, and better understanding their customer base.

Systems and methods for monitoring parking spots are disclosed. A system includes at least one vehicle and a remote computer that are in communication with each other. A vehicle includes a camera that generates image data, a location device that generates geographic coordinates of the vehicle, a computing device that receives the image data from the camera and the geographic coordinates of the vehicle and optionally data from a laser scanner that is calibrated with the camera and is connected to a smartphone that transmits data to the remote computer. Image data generated by the camera is processed with a reference image and data from the laser scanner to determine an occupation status of the parking spot. The occupation status is transmitted by the remote computer to a second vehicle.

Metadata determines treatment of content by automated systems, including “user generated content” web sites. The metadata may be conveyed with the content or may be determined by other techniques, including systems based on digital watermarking or content fingerprinting. In some arrangements, treatment depends on the temporal length of a content excerpt that matches a pre-existing work. In others, treatment depends on popularity—either of the content, or a pre-existing work from which it was derived. A great variety of other factors and contexts can also be considered. Automated tools to preliminarily identify possible “fair use” can be realized; further determination may be made by human evaluators (including crowd-source approaches).

Systems and methods of processing crowdsourced navigation information for use in autonomous vehicle navigation are disclosed. A method may include processing, by a mapping server, crowdsourced navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.

A DeepQA engine is enhanced to provide a digital medical investigation tool which assists a medical professional in researching potential causes of a set of patient conditions, including clues, facts and factoids about the patient. The DeepQA engine provides one or more answers to a natural language question with confidence levels for each answer. If a confidence level falls below a threshold, the enhanced DeepQA engine performs a crowd sourcing operation to gather additional information from one or more domain experts. The domain expert responses are provided to the medical professional, and are learned by the enhanced DeepQA system to provide for better research of similar patient conditions in future queries.

Systems, apparatuses, methods, and software for collecting and disseminating crowd-sourced information relating to one or more shared vehicles, such as buses, passenger trains, subway vehicles, streetcars, etc. The crowd-sourced information is collected via mobile client devices carried by users, such a riders of the shared vehicle at issue. Information collected includes tracing data for tracing the route and timing of each shared vehicles. The tracing data is used to update a computer model that helps predict arrival/departure times. The predicted arrival times can be conveyed to users and to allow people to arrange rendezvous events. Other information collected includes user-report information on items such as condition of the shared vehicle, fullness of the vehicle, and the user's experience with the vehicle and/or corresponding infrastructure. Collected user-report information can be shared with other users and/or a customer service system affiliated with the shared vehicle.

Systems and methods are provided for filtering and/or creating Points-of-Interest (POIs). In one embodiment, a list of POIs is obtained and then filtered based on crowd data related to the list of POIs to provide a filtered list of POIs. In another embodiment, one or more crowd-sourced POIs are created based on one or more crowds relevant to a corresponding bounding region for POI creation.

Methods, systems, computer-readable media, and apparatuses for using a small cell as a reliable crowd-sourcing agent are presented. In some embodiments, a small cell installed at a known location may observe one or more wireless signals at the known location, wherein the small cell comprises a built-in network listen receiver for observing cellular downlink signals. Subsequently, the small cell may provide, to at least one crowdsourcing server, information that identifies the location and describes one or more detected properties of the one or more observed wireless signals. In at least one arrangement, the information provided to at least one crowdsourcing server is a Positioning Reference Signal (PRS) configuration based on an observed LTE downlink signals. In at least one arrangement, the information provided to at least one crowdsourcing server is configured to be used by the at least one crowdsourcing server in providing position assistance information.

Techniques and systems for determining locations of devices using location data sources are provided. For example, a network device, method, and computer-program product may be provided. In one example, a method may include receiving, on a computing device, a request to locate a device, wherein the request includes an identifier of the device. The method may further include receiving a communication from the device, wherein the communication includes the identifier of the device, and obtaining a location of the device. The method may further include transmitting the location of the device and the identifier of the device to a server, wherein the server is configured to use the location of the device and the identifier of the device to send a response to the requestor of the request.

The invention relates to a tag noise correction based crowd-sourced tagging data quality improvement method. The method comprises the following steps: running a tag integration algorithm in an initial crowd-sourced tagging data set to form a data set after tag integration, and estimating tagger quality and integrated tag quality information of samples in the process; performing multi-round K-fold cross validation by utilizing the data set after tag integration, and constructing a high-quality data set; determining a tag noise set in combination with the tagger quality and the tag quality of the samples by utilizing a prediction probability of a class tag of each sample in the multi-round K-fold cross validation process; and training a classification model by utilizing the high-quality data set generated in the multi-round K-fold cross validation process, and performing prediction and replacement on the class tag of each sample in the tag noise data set by using the model. With the tag noise correction method, the quantity of potential noise tag samples in the data set after original tag integration is reduced, so that the data quality is improved.

A method of processing vehicle navigation information for use in autonomous vehicle navigation is provided. The method includes receiving, by a server, navigation information from a plurality of vehicles. The navigation information from the plurality of vehicles is associated with a common road segment. The method also includes storing, by the server, the navigation information associated with the common road segment. The method also includes generating, by the server, at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information from the plurality of vehicles. The method further includes distributing, by the server, the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment.

Current and predicted traffic information is provided from incident data, traffic flow data, and media related to traffic received from multiple sources. The crowd sourced data may be provided passively by applications on remote mobile devices or actively by users operating the remote mobile devices. An application on a mobile device may receive the multiple data types, aggregate and validate the data, and provides traffic information for a user. The traffic information may relate to the current position and route of the user or a future route. The present technology may also provide driving efficiency information such as fuel consumption data, carbon footprint data, and a driving rating for a user associated with a vehicle.

In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include an expert system configured to identify clusters of inquiries that can be responded to using predetermined response content. The expert system is optionally trained based on scored responses to prior customer service inquiries.

Business profiles are created using information from third parties. A user is enabled to indicate a business type for one or more merchants, and a system prompts the user to input information relating to the one or more merchants. The system determines the validity of the input information, and when the validity of the input information is verified, the input information is transformed into directory information. Official merchant pages can thus be created by crowd-sourcing important and pertinent information, which will lead to a more extensive directory of businesses and allow users a more complete and enjoyable experience. The system ranks users on their knowledge and input versus other users, and the system maintains a leaderboard that may be tied to user awards as an incentive to input information.

The invention provides a Wi-Fi location fingerprint map building method and a system based on crowd-sourcing. The method comprises the steps as follows: acquiring relevant crowd-sourcing data; using a dead reckoning approach to calculate the relative motion trajectory of a user based on the crowd-sourcing data; identifying specific behaviors of the user including a plurality of behavior types through machine learning; building a behavior sequence model formed by the types of the specific behaviors in the relative motion trajectory and the relative spatial relationship between the specific behaviors; generating a point-line model of an indoor map, and matching the point-line model with the behavior sequence model through a hidden Markov model to obtain the indoor map coordinate information of the relative motion trajectory; and finally, building a Wi-Fi location fingerprint map based on the indoor location coordinate information and the crowd-sourcing data. The Wi-Fi location fingerprint map is built and updated automatically based on the crowd-sourcing data.

An apparatus and method for crowd sourcing current traffic conditions using an image sensor on a vehicle to record traffic conditions at the vehicle location, date and time stamp the images, and transmit the images along with selected vehicle operating conditions and vehicle coordinates through a network to a central crowd sourcing traffic condition control. The control stores the images and, upon receiving an image request from a vehicle, transmits traffic condition images to the requesting vehicle through the network for a specified roadway segment. A vehicle opted-in to the control can select another opted-in vehicle to have an image taken by the any selected vehicle of a desired road location. The control handles the exchange of a request and an image between the requesting vehicle and the selecting vehicle to enable an instantaneous image of a desired road location to be displayed in the requesting vehicle.