Accenture Global Services Gmbh

Rayid Ghani

Marko Krema

Andrew E Fano

Chad Michael Cumby

Katharina Probst

Yan Liu

Divna Djordjevic

Yaron Rachlin

Hillery D Simmons

Chad M Cumby

Zhu-Song Mei

Leana Rachelle Wallace

Nicholas Francisco Howell

Ajay Kurian Easo

Michael S Irish

Dmitriy Feferman

Mohit Kumar

Lindsey J Lizardi

Laura J Jantzen

Katherine Probst

Privacy is defined in the context of a guessing game based on the so-called guessing inequality. The privacy of a sanitized record, i.e., guessing anonymity, is defined by the number of guesses an attacker needs to correctly guess an original record used to generate a sanitized record. Using this definition, optimization problems are formulated that optimize a second anonymization parameter (privacy or data distortion) given constraints on a first anonymization parameter (data distortion or privacy, respectively). Optimization is performed across a spectrum of possible values for at least one noise parameter within a noise model. Noise is then generated based on the noise parameter value(s) and applied to the data, which may comprise real and/or categorical data. Prior to anonymization, the data may have identifiers suppressed, whereas outlier data values in the noise perturbed data may be likewise modified to further ensure privacy.

Data anonymization based on guessing anonymity

A method and system for using individualized customer models when operating a retail establishment is provided. The individualized customer models may be generated using statistical analysis of transaction data for the customer, thereby generating sub-models and attributes tailored to customer. The individualized customer models may be used in any aspect of a retail establishment's operations, ranging from supply chain management issues, inventory control, promotion planning (such as selecting parameters for a promotion or simulating results of a promotion), to customer interaction (such as providing a shopping list or providing individualized promotions).

System for individualized customer interaction

A search ranking system may include a context mining module to determine a set of contexts based on profile of information rankable by the system and an access history of users that have accessed at least some of the information. A context detection module may compare an association of a user conducting a search with one or more of the contexts to thereby rank search results based on the comparison.

Context and process based search ranking

A method and apparatus are provided for processing a graphic in a document so that the graphic may be reused in a different application than the one it was originally used in. For a given document, a graphic may be identified from within the document and extracted from the document. The extracted graphic may be stored in a suitable storage medium, such as a reusable graphic repository. A structural feature associated with the extracted graphic may also be extracted. The extracted graphic may then be classified based on the extracted structural feature. Furthermore, a method and apparatus are provided for generating a reusable graphic from a document.

Processing a reusable graphic in a document

A generative model is used to develop at least one topic model and at least one sentiment model for a body of text. The at least one topic model is displayed such that, in response, a user may provide user input indicating modifications to the at least one topic model. Based on the received user input, the generative model is used to provide at least one updated topic model and at least one updated sentiment model based on the user input. Thereafter, the at least one updated topic model may again be displayed in order to solicit further user input, which further input is then used to once again update the models. The at least one updated topic model and the at least one updated sentiment model may be employed to analyze target text in order to identify topics and associated sentiments therein.

User modification of generative model for determining topics and sentiments

When redacting natural language text, a classifier is used to provide a sensitive concept model according to features in natural language text and in which the various classes employed are sensitive concepts reflected in the natural language text. Similarly, the classifier is used to provide an utility concepts model based on utility concepts. Based on these models, and for one or more identified sensitive concept and identified utility concept, at least one feature in the natural language text is identified that implicates the at least one identified sensitive topic more than the at least one identified utility concept. At least some of the features thus identified may be perturbed such that the modified natural language text may be provided as at least one redacted document. In this manner, features are perturbed to maximize classification error for sensitive concepts while simultaneously minimizing classification error in the utility concepts.

Classification-based redaction in natural language text

Methods and systems for processing claims (e.g., healthcare insurance claims) are described. For example, prior to payment of an unpaid claim, a prediction is made as to whether or not an attribute specified in the claim is correct. Depending on the prediction results, the claim can be flagged for an audit. Feedback from the audit can be used to update the prediction models in order to refine the accuracy of those models.

Claims analytics engine

Method and apparatus are provided for providing one or more sentiment classifiers from training data using supervised classification techniques based on features extracted from the training data. Training data includes a plurality of units such as, but not limited to, documents, paragraphs, sentences, and clauses. A feature extraction component extracts a plurality of features from the training data, and a feature value determination component determines a value for each extracted feature based on a frequency at which each feature occurs in the training data. On the other hand, a class labeling component labels each unit of the training data according to a plurality of sentiment classes to provide labeled training data. Thereafter, a sentiment classifier generation component provides a least one sentiment classifier based on the value of each extracted feature and the labeled training data using a supervised classification technique.

Sentiment classifiers based on feature extraction

Promotion planning system

General entity retrieval and ranking is described. A first set of documents is retrieved from one or more document repositories based on a query formed according to the topic. The first set of documents is characterized based on its first set of metadata values. One or more candidate entities are identified based on the first set of documents and the original query is thereafter augmented according to a candidate entity. The second set of documents resulting from the augmented query is then characterized in a similar manner. For each candidate entity, the first and second document set characterizations are compared to determine their degree of similarity. Increasingly similar document set characterizations indicates that the candidate entity is increasingly relevant to the original query. Repeating this process for each of the one or more candidate entities can give rise to rankings according to the respective degrees of similarity.

Entity assessment and ranking

One or more classification algorithms are applied to at least one natural language document in order to extract both attributes and values of a given product. Supervised classification algorithms, semi-supervised classification algorithms, unsupervised classification algorithms or combinations of such classification algorithms may be employed for this purpose. The at least one natural language document may be obtained via a public communication network. Two or more attributes (or two or more values) thus identified may be merged to form one or more attribute phrases or value phrases. Once attributes and values have been extracted in this manner, association or linking operations may be performed to establish attribute-value pairs that are descriptive of the product. In a presently preferred embodiment, an (unsupervised) algorithm is used to generate seed attributes and values which can then support a supervised or semi-supervised classification algorithm.

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Chicago, IL, United States of America

Rayid Ghani