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Statistical Methods For Environmental Pollution Monitoring

Techniques for providing friction-free transactions using geolocation and user identifiers are described herein. These techniques may ascertain a user's location based on a location of a mobile device. A transaction between the user and a merchant may be completed with zero or minimal input from the user based on the geolocation of the mobile device and the user identifiers. In some implementations, a transaction initiated earlier is completed when the mobile device arrives at the merchant. Additionally, a parent-child or similar relationship may be established between multiple devices. Security on the mobile device based may be provided by biometric identification and calculation of variance from regular movement patterns. Advertisements may be sent to the mobile device based on bids from merchants near to the mobile device. Promotions may be sent to the mobile device when more than a threshold number of mobile devices are located at the same merchant.

User profile and geolocation for efficient transactions

A system, apparatus, and method for determining a consumer's location code/zip code based on data from payment transactions in which the consumer has participated. Payment transaction data for a consumer is processed to determine the location code/zip code corresponding to the locations in which the majority of transactions occurred. From this information, the most likely location code/zip code for the residence of the consumer is inferred. Based on the inferred location code/zip code for the consumer, demographic data may be accessed and used to infer characteristics of the financial situation or status of the consumer. Such financial situation or status information may include the average net worth, range of income, spending habits, etc. for a consumer residing in the identified location code/zip code. Based on the demographic data, products or services that may be of most interest to a person in the consumer's presumed financial situation may be marketed to the consumer.

System and method for determining a consumer's location code from payment transaction data

Techniques for providing information to a user of a mobile device based on an online or web-identity of the user and a geolocation of the mobile device are described herein. The user may be notified when a nearby merchant has a good or service for sale that matches a good or service in a list, such as a wish list, associated with the web-identity of the user. The users may also be provided access to a coupon within an electronic document when a mobile device storing the electronic document is located at a particular merchant. This convergence of geographical location of the user, as determined by the geolocation of his or her mobile device, with his or her web-identity can bring the online and off-line worlds closer together to provide relevant information for the user and improved marketing opportunities for merchants.

Converged Web-identity and Mobile Device Based Shopping

Systems and methods are provided for improved web searching. In one implementation, suggested search queries are provided based on previous search queries and click data. A weighted bi-partite graph or index may be used to identify related search queries based on overlapping clicked URLs. According to a method, query-click log data of a search engine is processed to generate sets of suggested search queries, data corresponding to each suggested search query, and a set of clicked URLs related to each suggested search query. Additionally, or independently, methods may be provided for contextually correcting spelling errors within sets of suggested search queries using a contextual algorithm, and/or identifying and discarding sets of suggested search queries and URLs that lead to restricted material, such as restricted content and related URLs.

Systems and methods for improved web searching

A method for supplementing structured information within a data system for entities based on unstructured data analyzes a document with unstructured data and extracts attribute values from the unstructured data for one or more entities of the data system. Entity records with structured information are retrieved from the data system based on the extracted attribute values. Entity references for corresponding entities of the data system are constructed based on a comparison of the retrieved entity records and the extracted attribute values. The entity references are linked to the corresponding entities within the data system, with the entity references including extracted attributes from the unstructured data for corresponding linked entities.

Supplementing structured information about entities with information from unstructured data sources

Systems and methods of marketing to mobile devices are provided. Other methods, apparatus, and computer readable media are also provided.

Systems and methods for marketing to mobile devices

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called "check-in" profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

Inferring and Exploiting Categories for Next Location Prediction

A method is provided for forming discrete segment clusters of one or more sequential sentences from a corpus of communication transcripts of transactional communications that comprises dividing the communication transcripts of the corpus into a first set of sentences spoken by a caller and a second set of sentences spoken by a responder; generating a set of sentence clusters by grouping the first and second sets of sentences according to a measure of lexical similarity using an unsupervised partitional clustering method; generating a collection of sequences of sentence types by assigning a distinct sentence type to each sentence cluster and representing each sentence of each communication transcript of the corpus with the sentence type assigned to the sentence cluster into which the sentence is grouped; and generating a specified number of discrete segment clusters by successively merging sentence clusters according to a proximity-based measure between the sentence types assigned to the sentence clusters within sequences of the collection.

Method for segmenting communication transcripts using unsupervised and semi-supervised techniques

Many organizations provide dialog-based support through contact centers to sell their products, handle customer issues, and address product-and service-related issues. This is usually provided through voice calls--of late, web-chat based support is gaining prominence. In this paper, we consider any conversational text derived from web-chat systems, voice recognition systems etc., and propose a method to identify procedures that are embedded in the text. We discuss here how to use the identified procedures in knowledge authoring and agent prompting. In our experiments, we evaluate the utility of the proposed method for agent prompting. We first cluster the call transcripts to find groups of conversations that deal with a single topic. Then, we find possible procedure-steps within each topic-cluster by clustering the sentences within each of the calls in the topic-cluster. We propose a measure for differentiating between clusters that are procedure-steps and those that are topical sentence collections. Once we identify procedure-steps, we represent the calls as sequences of procedure-steps and perform mining to extract distinct and long frequent sequences which represent the procedures that are typically followed in calls. We show that the extracted procedures are comprehensive enough. We outline an approach for retrieving relevant procedures for a partially completed call and illustrate the utility of distinct collections of sequences in the real-world scenario of agent prompting using the retrieval mechanism.

Deepak S. Padmanabhan

Papers

Supplementing structured information about entities with information from unstructured data sources

Systems and methods for marketing to mobile devices

Inferring and Exploiting Categories for Next Location Prediction

Method for segmenting communication transcripts using unsupervised and semi-supervised techniques

Mining conversational text for procedures with applications in contact centers