Accenture Global Solutions Limited

Accenture Global Services Gmbh

Vikrant Shyamkant Kaulgud

Vibhu Saujanya Sharma

Sanjay Podder

Kapil Singi

Adam Patten Burden

Janardan Misra

Rohit Mehra

Shubhashis Sengupta

Kuntal Dey

Kishore P Durg

Milind Ravikiran Savagaonkar

Poulami Debnath

Venkatesh K Subramanian

Swati Sharma

Priyavanshi Pathania

Sanjoy Paul

Amitabh Saxena

Sanjay Mittal

Pavel Valerievich Ponomarev

Mainak Basu

Sundeep Singh

Akshay Kasera

Peter Michael Lacy

Joshy Ravindran

Swapnajeet Gon Choudhury

Pradeepkumar Duraisamy Soundrapandian

Rajan Dilavar Mithani

Nataraj Kuntagod

Jagadeesh Chandra Bose Rantham Prabhakara

Gary Titus

Pramod Bijani

Ravi Kiran Velama

Dipin Era

Subramanya Prasad

Alpana A Dubey

Saurabh Bhadkaria

Annervaz K M

Samarth Sikand

Aditya Bhola

Chethana Dinakar

Nilesh Agrawal

Shrikanth Narayanaswamy Chandrasekaran

Annervaz Km

Siddharth Mehrotra

Sankar Narayan Das

Colin P Jarrett

Neville Dubash

Santonu Sarkar

Anurag Dwarakanath

Anutosh Maitra

Annervaz Karukapadath Mohamedrasheed

Divya Rawat

Parikshit Maniar

Nishant Kumar

Gurdeep Virdi

Sanjeev Vohra

Satya Sai Srinivas

Aravindan Thoppe Santharam

Tom Geo Jain

Narendranath Sukhavasi

Raghotham M Rao

Debadatta Tripathi

Subani Basha Nure

Rambhau Eknath Rote

Sourav Bhattacharya

Jhilam Bera

K M Annervaz

Vallinath Sriramula

Rohit Punja

Afsal Marattil

Hillel Solomon Awaskar

Michael A Brinkley

Kanchanjot Kaur Phokela

Rupesh Kaslay

Nibedita Sarmah

Manisha Sriraman

Namrata Maheshwary

Jagadeesh Chandra Bose R P

Bernhard F Kraft

Shantiprakash Motwani

Raghunandan Hemmige Keshava

Anirudha Singh

Reva Modi

A method and system for assisting program code development are disclosed. The method may include obtaining multimodal data of a meeting discussing a program code development, extracting a plurality of topics and a plurality of concepts from the multimodal data, identifying a plurality of meeting segments for the plurality of concepts. The method may further include determining a coding intent from program codes for the program code development, aligning the coding intent to a set of topics, identifying the set of concepts associated with the topic aligned with the coding intent, identifying a set of meeting segments associated with the concept. The method may further include determining an alignment metric of the meeting segment based on an alignment metric between the coding intent and the topic, and outputting one or more meeting segments for the coding intent based on alignment metrics of the one or more meeting segments.

Assisting program code development by processing multimodal data of a meeting

Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

Computer code generator

Implementations include providing a robotic model representative of a robotic system, determining baseline data by executing a simulation using the robotic model and a simulation world that represents the environment, generating, by a perturbation system, perturbations to be included in a set of test cases, each test case including at least one perturbation to at least one object relative to a baseline of the at least one object determined from the baseline data, determining test result data for the set of test cases by modifying the at least one object in view of the at least one perturbation to provide a modified object and executing the simulation with the modified object, and displaying a graph representative of the at least one object based on the baseline data and test result data for a test case, the graph depicting variance of the object during the simulation using the test case.

Testing robotic software systems using perturbations in simulation environments

In some implementations, an advisor system may receive a description of a problem to be solved and problem data identifying quantum computing-related and classical computing-related problems. The advisor system may perform natural language processing on the description of the problem and the problem data to respectively generate a problem embedding vector for the problem and to generate embedding vectors that represent the quantum computing-related and classical computing-related problems. The advisor system may process the problem embedding vector and the embedding vectors, with a vector matching model, to determine a semantically closest matching one of the embedding vectors to the problem embedding vector and, accordingly, may generate a recommendation that includes an indication to solve the problem with a classical computing resource, a quantum computing resource, or a combination of a classical computing resource and a quantum computing resource.

Utilizing natural language processing similarity matching to determine whether a problem requires quantum computing or classical computing

In some implementations, a device may receive first energy consumption information relating to a set of hardware components of a computing system. The device may receive second energy consumption information relating to a set of virtual machines associated with the computing system. The device may receive third energy consumption information relating to a set of software elements associated with the computing system. The device may determine an energy consumption of the computing system based on the first energy consumption information, the second energy consumption information, and the third energy consumption information. The device may identify, based on the energy consumption of the computing system, an energy optimization associated with a usage context of the computing system. The device may transmit a set of instructions to alter one or more parameters of the computing system to implement the energy optimization for the computing system.

Energy management for computing systems

In some examples, collective application portfolio migration control may include determining, for a plurality of applications that are to be clustered for migration to a cloud environment, a coupling coefficient that represents a type of coupling between pairs of applications from the plurality of applications, a proximity coefficient that represents application proximities, and a connectedness coefficient that represents application connections. A combined application relatedness coefficient may be determined based on the coupling coefficient, the proximity coefficient, and the connectedness coefficient. A portfolio graph may be generated based on the combined application relatedness coefficient to generate migration application clusters to duster the plurality of applications. Migration of the plurality of applications to the cloud environment may be controlled based on the migration application clusters.

Collective application portfolio migration control

In some examples, policy-based application architecture generation may include generating, based on a knowledge model schema, data residency policies, and a data classification ontology, a knowledge model, and determining, based on the knowledge model, whether an application includes regulated data. Based on an analysis of application data and user data, user location and regulated data insights may be generated to determine location specific data residency policies. Location specific regulated data in-flow and data source hosted location insights may be analyzed to determine a location compliance assessment that includes an indication of whether a location associated with the application is compliant or not compliant with the location specific data residency policies. Based on an indication of non-compliance, a strategy may be generated for compliance of application architecture for the application with the location specific data residency policies, and re-architecting of the application architecture may be implemented.

Policy-based application architecture generation

In some examples, continuous learning-based application related trade-off resolution and implementation may include generating, based on a plurality of historical tradeoff instances, an application feature matrix. Further, association rules for historical tradeoff instances for which decisions are not known, and a decision tree for historical tradeoff instances for which decisions are known may be generated. Decision rules may be induced, and default rules may be applied to a cold start scenario. The decision rules and the default rules may be refined to generate refined rules, and a confidence level may be determined for the refined rules. The refined rules may be prioritized based on the confidence level and applied to a new tradeoff instance to generate a resolution associated with the new tradeoff instance. The resolution may be implemented with respect to the new tradeoff instance.

Continuous learning-based application related trade-off resolution and implementation

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Green cloud computing recommendation system

In some examples, simulation-based software design and delivery attribute tradeoff identification and resolution may include receiving requirements specification, and generating, based on an analysis of the requirements specification, canonical sustainability requirements. Based on an analysis of the canonical sustainability requirements, sustainable software attribute decisions and an attribute optimization score may be generated, and used to generate a sustainable software attribute balance score and a tradeoff attributes list. Based on an analysis of the sustainable software attribute balance score and the tradeoff attributes list, a green quotient may be generated and used to generate an architecture document. Further, based on an analysis of the architecture document, software quality rules may be generated, and used to generate a software application.

Simulation-based software design and delivery attribute tradeoff identification and resolution

A method and system for cloud distributed hybrid data storage and normalization are disclosed. The method may include obtaining a data set comprising data entities. A data entity may comprise data fields each containing a data element. The method may further include determining policy constraint meta-data for each of the data elements based on the storage policy constraint. The policy constraint meta-data may include a first meta-tag indicating the storage policy constraint for the data element. The method may further include determining whether a server satisfies the storage policy constraint based on the first meta-tag for the data element. When the server satisfies the storage policy constraint, the method may further include transmitting the data element to the server to store the data element on the server. When the server fails to satisfy the storage policy constraint, the method may further include, storing the data element on the client.

Cloud distributed hybrid data storage and normalization

In some examples, data-driven social media analytics application synthesis may include generating, for each social media analytics application of a plurality of social media analytics applications, a corpus, performing term normalization, and generating a normalized corpus. An actor, an action and an object may be generated for each social media analytics application, which may be mapped into an embedding space. A semantic cohesion network may be generated for each social media analytics application, and a pair-wise semantic cohesion may be determined to identify semantically cohesive groups. A new social media analytics application may be synthesized based on the identified semantically cohesive groups.

Data-driven social media analytics application synthesis

In some examples, migration context and flow graph based migration control may include ascertaining an application that is to be migrated from a physical environment to a cloud environment, and determining a migration issue associated with the migration of the application. Migration context and flow graph based migration control may further include identifying, from a historical issue database, a plurality of historical issues, determining, for the migration issue and the plurality of historical issues, unified proximities, sorting, based on the determined unified proximities, the historical issues, selecting, from the sorted historical issues, a topmost historical issue, and determining, from the topmost historical issue, a resolution associated with the topmost historical issue. Further, migration context and flow graph based migration control may include executing the resolution to resolve the migration issue, and performing, based on the resolved migration issue, migration of the application from the physical environment to the cloud environment.

Migration context and flow graph based migration control

In some examples, extended reality based immersive project workspace creation may include obtaining sensing data from at least one sensor of a device worn by a user, and analyzing, based on the sensing data, an environment of the user. A plurality of insights associated with the user may be determined, and prioritized based on a plurality of prioritization criteria. An insight of the plurality of prioritized insights associated with the user may be rendered in the environment of the user. Further, the rendering of the insight may be controlled based on an interaction of the user with the rendered insight.

Extended reality based immersive project workspace creation

A device may receive application data identifying an application to be executed by a hybrid cloud computing environment and constraint data identifying constraints associated with the hybrid cloud computing environment. The device may select objectives based on the application data and the constraint data, and may identify factors associated with the objectives. The device may correlate the factors with potential data centers, and may apply weights to the correlated factors to generate correlated factor scores. The device may identify a list of data centers based on the correlated factor scores, and may determine a reduced list of data centers. The device may apply a preference technique to the reduced list of data centers to generate a ranked list of data centers, and may determine the hybrid cloud computing environment based on the ranked list of data centers. The device may perform actions based on the hybrid cloud computing environment.

Determining a recommended hybrid cloud computing environment for an application on demand

Growing community of inventors

Maharashtra, India

Vikrant Shyamkant Kaulgud