Wells Fargo Bank, N.a.

Hewlett Packard Enterprise Development LP

Servicenow, Inc.

Juniper Networks, Inc.

Yahoo! Inc.

Texas Instruments Corporation

Sonicwall Inc.

Cloudera, Inc.

Honeywell Internatoinal Inc.

Abhishek Kumar

Amit Agarwal

Jyoti Ranjan

Dipanjan Deb

Naveen Gururaja Yeri

Ajay Kumar Bajaj

Parul Ghosh

Shishir Vasant Rao

Niravkumar N Bajaj

Priyanka Dixit

Arvind Kumar Gottapally

Anil Kaushik

Raghunandan Prabhakar

Sreenivas Voruganti

Tal Ben Ari

Stephen Grau

Unmesh Gurjar

Sridhar Pasupula Chandrasekaran

Vineet Verma

Manish Satish Vimla Kumar

Hail Tal

Sreenevas Subramaniam

Ankur Chauhan

Subrato Roy

Sheeladitya Karmakar

Christopher Dean Peterson

Arik Sityon

Prabhu Murthy

Abhrarup Barman Roy

Aakash Umeshbhai Bhagat

Srikanth Venkatasubramanian

Hemanth Yamijala

Vinayak G

Ramkumar Devanathan

Atul Gupta

Shruti Jain

Abhinay Patil

Kallesh N Patil

Jerin Cherian

Mukesh Vijay

Bhawani Shekhawat

Manikandan M

Renan Coelho Silva

Julia A Kosheleva-Coates

Mayank Kumari

Ashwin Rajeev

Ramesh Kumar

Manish S V Kumar

Lawrence J McCay, Iii

Praveen Singh Thakur

Aditya Kumar

Anudeep Chandra Thatipalli

Purvi Dayabhai Pipaliya

Various implementations disclosed herein include obtaining data indicative of a request to modify a configuration item (CI) of a database and one or more portions of a pattern applicable to a service based, at least in part, on the request. A command is identified based on the one or more portions and one or more values of the CI are modified using the command.

Modification of database objects

In various embodiments, a process for determining metrics including resource expenditures of a digital service includes discovering a plurality of configuration items of a computing infrastructure. The process includes identifying a subset of the plurality of configuration items utilized to provide a digital service, obtaining a plurality of resource expenditures respectively associated with at least a portion of the plurality of configuration items, and associating a subset of the plurality of resource expenditures with the subset of the plurality of configuration items. The process includes aggregating the subset of the plurality of resource expenditures to generate a metric of the digital service.

Determining metrics including resource expenditures of a digital service

Persistent storage may contain a list of discovery commands, the discovery commands respectively associated with lists of network addresses. A discovery validation application, when executed by one or more processors, may be configured to: read, from the persistent storage, the list of discovery commands and the lists of network addresses; for each discovery command in the list of discovery commands, transmit, by way of one or more proxy servers deployed external to the system, the discovery command to each network address in the respectively associated list of network addresses; receive, by way of the one or more proxy servers, discovery results respectively corresponding to each of the discovery commands that were transmitted, wherein the discovery results either indicate success or failure of the discovery commands; and write, to the persistent storage, the discovery results.

Rapid error detection through command validation

Techniques are described for generating a set of emotion factor values using one or more machine learning models for customer communications. For example, a computing system includes one or more processors in communication with a memory. The one or more processors are configured to receive communication data of a current communication associated with a customer, apply the communication data to an emotion-based indexer as input wherein the emotion-based indexer includes a set of machine-learning models for a set of emotion factors, generate as output from the emotion-based indexer a set of emotion factor values for the current communication wherein each emotion factor value indicates the measure of a particular emotion factor in the current communication, apply the set of emotion factor values to an emotion classification model, and classify the current communication into an emotion state based on the set of emotion factor values.

Emotion analysis using deep learning model

Techniques are described for personalizing customer interactions using one or more machine learning models for customer communications. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: retrieve, from a database in memory, one or more sets of emotion factor values for communication data associated with a customer over time; classify, using an emotion propensity model running on the one or more processors, the customer into an emotional profile according to the customer's typical emotional response during customer communications based on the one or more sets of emotion factor values for the communication data associated with the customer over time; and determine a probability that the customer will respond positively to a particular type of customer engagement based on the emotional profile for the customer.

Predicting customer interaction using deep learning model

Techniques are described for determining a likelihood that a customer communication is fraudulent using one or more machine learning models. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: receive a set of emotion factor values for communication data of a current communication associated with a customer, wherein each emotion factor value indicates a measure of a particular emotion factor in the current communication; classify, using an emotion variance model running on the one or more processors, the current communication into an emotional fraud category based on the set of emotion factor values for the current communication associated with the customer; and determine a risk score for the current communication indicative of a probability that the current communication is fraudulent based on at least the emotional fraud category for the current communication.

Fraud detection using emotion-based deep learning model

Techniques are described for performing complaint prioritization using one or more machine learning models for customer communications. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to receive communication data indicative of a service inquiry from a user device, generate a set of emotion factor values that indicate a measure of particular emotions in the service inquiry, determine, using a machine learning model and based on the set of emotion factor values, an emotional priority score for the service inquiry, and determine a response priority order for the service inquiry based on at least the emotional priority score.

Complaint prioritization using deep learning model

Disclosed in some examples are methods, systems, and machine readable mediums which provide summaries of topics determined within a corpus of documents. These summaries may be used by customer service associates, analysts, or other users to quickly determine both topics discussed and contexts of those topics over a large corpus of text. For example, a corpus of documents may be related to customer complaints and the topics may be summarized to produce summaries such as 'credit report update due to stolen identity.' These summarizations may be used to efficiently spot trends and issues.

Dynamic topic definition generator

An example method for monitoring operations of a business organization electronic computing device includes executing a checklist on a monitoring electronic computing device to obtain an operational status of an application being implemented on the business organization electronic computing device. Data is received from the business organization electronic computing device regarding the operational status of the application being implemented on the business organization electronic computing device. When a determination is made that the data identifies an operational threat for the business organization electronic computing device, an action is proposed or implemented to remediate the operational threat. An implementation of the checklist is adjusted based on an effectiveness of the action to remediate the operational threat.

Maintaining operational continuity of business systems

Persistent storage may contain an input discovery payload that contains entries representing configuration items and relationships therebetween, wherein the configuration items contain attributes defining devices, components, or applications on a network. One or more processors may be configured to: provide, for display, a graphical user interface containing a representation of the input discovery payload and a button; provide the input discovery payload to an identification and reconciliation engine (IRE) software application; receive, from the IRE software application, an output discovery payload that includes a log generated from execution of the IRE software application on the input discovery payload, wherein the log indicates, for the configuration items and the relationships in the input discovery payload, how a configuration management database (CMDB) would be updated by the IRE software application; and provide, for display, a further graphical user interface containing a further representation of the output discovery payload.

Payload recording and comparison techniques for discovery

Example implementations relate to determining and implementing a feasible resource optimization plan for public cloud consumption. Telemetry data over a period of time is obtained for a current deployment of virtual infrastructure resources within a current data center of a cloud provider that supports an existing service and an application deployed on the virtual infrastructure resources. Information regarding a set of constraints to be imposed on a resource optimization plan is obtained. Indicators of resource consumption relating to the currently deployed virtual infrastructure resources during the period of time are identified by applying a deep learning algorithm to the telemetry data. A resource optimization plan is determined that is feasible within the set of constraints based on a costing model associated with resources of an alternative data center of the cloud provider, the indicators of resource consumption and costs associated with the current deployment.

Determining and implementing a feasible resource optimization plan for public cloud consumption

In some examples, a system may include a processing resource and a memory resource. The memory resource may store machine-readable instructions to cause the processing resource to create a migration plan, defining characteristics of a migration of an application from a native computing data center to a computing cloud of a plurality of distinct candidate computing clouds, based on: a first migration constraint for the application determined from an analysis of historical behavior of the application executed on the native computing data center; a second migration constraint for the application determined from an analysis of administrator cloud migration preferences for the application; and a cloud computing characteristic of each of the plurality of distinct candidate computing clouds.

Application migrations

Example implementations relate to migration of a stateful application from a source computing environment to a destination virtualized computing environment by prioritizing migration of data of the application based on a priority map created based on data usage patterns. An instance of the application is installed within the destination environment. The priority map includes priorities for chunks of the data based on historical data access patterns. The data is migrated from a source volume of the source environment to a destination volume of the destination environment on a chunk-by-chunk basis by performing a background data migration process based on the priority map. Usage of the application concurrent with the data migration process is facilitated by abstracting a location of data being operated upon by the application by maintaining migration status for the chunks. The priority map is periodically updated based on observed data access patterns post application migration.

Prioritizing migration of data associated with a stateful application based on data access patterns

Example implementations relate to a upgrade of a host that hosts application units of a container-based application. According to an example, monitoring is performed to identify new system software component availability for the cluster. When a new system software component is available, a historical workload pattern of the cluster is analyzed to identify an upgrade window for each host of the cluster. When the upgrade window arrives for a host, it is determined whether reconfiguration of an application is to be performed based on a capacity of the cluster. When the determination is affirmative, a reconfiguration option for the application is identified and a configuration of the application is adjusted accordingly. The host may then be drained, removed from the cluster, upgraded, added back into the cluster and any application configuration changes can be reversed.

Upgrade of hosts hosting application units of a container-based application based on analysis of the historical workload pattern of the cluster

A computing system for automatic discovery of a storage cluster. A computing system includes a discovery server, which receives a user-input including authentication credentials associated with a storage cluster. The discovery server executes a discovery pattern comprising a sequence of operations for a discovery of a set of resources associated with the storage cluster, based on the received user-input. One of the sequence of operations may correspond to transmission of a plurality of Application Programming Interface (API) calls to an Internet Protocol (IP) address of the storage cluster. The discovery server further receives a plurality of responses from the storage cluster. The plurality of responses includes information associated with the set of resources. The discovery server further generates a dependency map between the set of resources associated with the storage cluster based on the received information and outputs the generated dependency map.

Automatic discovery of storage cluster

Growing community of inventors

Bengaluru, India

Abhishek Kumar