Gm Global Technolgoy Operations, Inc.

Samsung Electronics Co., Ltd.

Ca, Inc.

Acalvio Technologies, Inc.

Satnam Singh

Pulak Bandyopadhyay

Steven W Holland

Rajendra Arcot Gopalakrishna

Nirmesh Neema

Sailesh Kumar Sathish

Venkata Babji Sama

Suril Rajul Desai

Rahul G Chougule

Ravitheja Tetali

Bhavani Shankar Yeleswarapu

Fan Bai

Xinyu Du

Yilu Zhang

Rammohan Varadarajan

Mark N Howell

Xidong Tang

Vineet Khare

Gaurav Jain

Venkatababji Sama

Sanjay Vyas

John V Bowman

Pattada A Kallappa

Frederick J Vondrak

Halasya Siva Subramania

Calvin E Wolf

Keith D Armitage

Sachin Raviram

David N Nowak

David B Miller

Balamurali Andiyakkal Rajendran

Clifton L Pinion

Aurobinda Routray

Mohammad Waseem

Aparna Rajaguru

Santosh Kosgi

Robert J Casper

Jeremy Krall

Joseph M Martinez

Brian W Quinn

Gaurav Kumar Jain

A method and a system for dynamically modifying at least one element of a User Interface (UI) of a first electronic device are provided. The method includes collating usage information of at least one data source in the first electronic device, categorizing the collated usage information into one or more knowledge clusters, forming a knowledge graph using the one or more knowledge clusters, and dynamically modifying the at least one element of the UI based on the knowledge graph.

Dynamically modifying elements of user interface based on knowledge graph

A method by a network node includes generating logically associated clusters of reviews submitted by users regarding an application program executed on user equipments operated by the users, generating a separate summary for each of the clusters of the reviews, and communicating the summaries to a network node.

Analysis of mobile application reviews based on content, reviewer credibility, and temporal and geographic clustering

An application analysis computer obtains reports from user terminals identifying operational states of instances of an application being processed by the user terminals. Sequences of the operational states that the instances of the application have transitioned through while being processed by the user terminals are identified. Common operational states that occur in a plurality of the sequences are identified. For each of the common operational states, a frequency of occurrence of the common operational state is determined. For each state transition between the common operational states in the sequences, a frequency of occurrence of the state transition is determined. State predictive metrics are generated based on the frequencies of occurrence of the common operational states and the frequencies of occurrence of the state transitions. The state predictive metrics are communicated, such as to an application server to control access to the application by user terminals.

Generating state predictive metrics based on Markov chain model from application operational state sequences

A social community management method includes: setting a group including a plurality of mobile devices, based on a location; determining common interest information shared by users of the plurality of mobile devices included in the group; creating a social community for communication of the users based on the determined common interest information; and transmitting information on the created social community to the plurality of mobile devices.

Location-based social community management apparatus and method

This disclosure is related to using network flow information of a network to determine the trajectory of an attack. In some examples, an adjacency data structure is generated for a network. The adjacency data structure can include a machine of the network that has interacted with another machine of the network. The network can further include one or more deception mechanisms. The deception mechanisms can indicate that an attack is occurring when a machine interacts with one of the deception mechanisms. When the attack is occurring, attack trajectory information can be generated by locating in the adjacency data structure the machine that interacted with the deception mechanism. The attack trajectory information can correlate the information from the interaction with the deception mechanism, the interaction information of the network, and machine information for each machine to determine a possible trajectory of an adversary.

Systems and methods for detecting and tracking adversary trajectory

Methods, systems, and computer-readable mediums are described herein to provide context-aware knowledge systems and methods for deploying deception mechanisms. In some examples, a deception profiler can be used to intelligently deploy the deception mechanisms for a network. For example, a method can include identifying a network for which to deploy one or more deception mechanisms. In such an example, a deception mechanism can emulate one or more characteristics of a machine on the network. The method can further include determining one or more asset densities and a summary statistic. An asset density can be associated with a number of assets connected to the network. The summary statistic can be associated with a number of historical attacks on the network. Using at least one or more of the one or more asset densities, the summary statistic, other information associated with the network, or a combination thereof, the method can further include determining a number of deception mechanisms to deploy, and deploying the number of deception mechanisms.

Context-aware knowledge system and methods for deploying deception mechanisms

Systems and methods for identifying potentially compromised devices using attributes of a known compromised device may be provided. In one embodiment, an attribute set can be constructed for the compromised hosts using data from these logs. Weights can be assigned to each attribute in the attribute set initially, and further weights can be learned using audits by a user. This attribute set can be used in the disclosed systems and methods for identifying hosts that are similar to compromised hosts. The similar items can be used as hosts for deception mechanisms, can be taken off the network as being likely compromised or likely to become compromised, or quarantined.

Systems and methods for identifying similar hosts

A method of detecting anomalies in the service repairs data of equipment. A failure mode-symptom correlation matrix correlates failure modes to symptoms. Diagnostic trouble codes are collected for an actual repair for the equipment. The diagnostic trouble codes are provided to a diagnostic reasoner for identifying failure modes. Diagnostic assessment is applied by the diagnostic reasoner for determining the recommended repairs to perform on the equipment in response to identifying the failure modes. Each of the recommended repairs is compared with the actual repair used to repair the equipment. A mismatch is identified in response to any recommended repair not matching the actual repair. Reports are generated for displaying all of the identified mismatches. The reports are analyzed for determining repair codes having an increase in a number of anomalies. Service centers are alerted of a correct repair for the identified failure mode.

Detecting anomalies in field failure data

A method of performing operations on a processor of an application analysis computer includes obtaining reports from electronic terminals. The reports contain metrics for operation of instances of an application processed by the electronic terminals and attributes of the electronic terminals characterizing their application processing platforms. Metrics clusters of the reports are generated based on the metrics. Attribute clusters of the reports are generated based on the attributes contained in the reports of one of the metrics clusters which satisfy a defined metric rule. One of the attribute clusters, which contains reports obtained from less than a threshold number of distinct electronic terminals, is identified. Identifiers of the electronic terminals are determined based on the attributes contained in the reports in the one of the attribute clusters. Information is communicated based on the identifiers of the electronic terminals.

Hierarchical temporal clustering, metric clustering and attribute clustering of electronic terminal reports to identify electronic terminals for analysis

A method includes collecting vehicle health data from a plurality of vehicles. A peer group is identified among the plurality of vehicles. The collected vehicle health data from the peer group into a collaborative vehicle health model, the collaborative vehicle health model being applicable to a current vehicle to predict a state of at least a component of the current vehicle.

Method and system for predicting vehicle battery health using a collaborative vehicle battery health model

A vehicle repair assist system for repairing a vehicle fault in a vehicle. A symptom input module is provided for entering vehicle symptom information relating to the fault. A diagnostic code module retrieves diagnostic trouble codes from the vehicle. The diagnostic trouble codes are generated in response to a fault occurrence. A knowledge-based fault module identifies potential causes of the vehicle fault based on at least one of the symptom information and diagnostic trouble codes. A repair assistant module identifies recommended repair parts and repair procedures for repairing the cause of the vehicle fault. The repair assistant module communicates with the knowledge-based fault module for obtaining a prioritized listing of the recommended repair parts and repair procedures for repairing the vehicle fault.

Repair assist system for vehicle servicing

A method for correcting service manual textual inconsistencies. Extracting textual procedures from service documents stored in a memory of a service document storage device. Each term of an extracted textual procedure terminology is compared to a correlating target name terminology for identifying any matching terms by a processor. An overlap similarity is computed as a function of the identified matching terms from the extracted textual procedure terminology and the correlating target name terminology. A determination is made whether the overlap similarity is greater than a predetermined similarity threshold. The service documents are modified to change the extracted textual procedure terminology to the correlating target name terminology in response to the overlap similarity being greater than the predetermined similarity threshold and the extracted textual procedure terminology not exactly matching the correlating target name terminology.

Semantics mismatch in service information

System fault codes, component fault codes, and failure modes may be received at a system. A first level model comprising first level causal relationships and first level causal weights between the system fault codes and the component fault codes may be generated. A second level model comprising second level causal relationships and second level causal weights between the component fault codes and the failure modes may be generated. The first level model and the second level model may be combined to generate a model such as graphical model. One or more causal probabilities may be determined using the graphical model.

Method and system for root cause analysis and quality monitoring of system-level faults

A method and system for comparing and merging fault models which are derived from different data sources. Two or more fault models are first represented as bipartite weighted graphs, which define correlations between failure modes and symptoms. The nodes of the graphs are compared to find failure modes and symptoms which are the same even though the specific terminology may be different. A graph matching method is then used to compare the graphs and determine which failure mode and symptom correlations are common between them. Finally, smoothing techniques and domain expert knowledge are used to merge and update the fault models, producing an integrated fault model which can be used by onboard vehicle systems, service facilities, and others.

Graph matching system for comparing and merging fault models

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Bengaluru, India

Satnam Singh