Accenture Global Solutions Limited

Accenture Global Services Gmbh

Sanjay Podder

Vikrant Shyamkant Kaulgud

Janardan Misra

Neville Dubash

Vibhu Saujanya Sharma

Adam Patten Burden

Anurag Dwarakanath

Rohit Mehra

Kapil Singi

Shubhashis Sengupta

Kishore P Durg

Divya Rawat

Priyavanshi Pathania

Milind Ravikiran Savagaonkar

Swati Sharma

Nisha Ramachandra

Venkatesh K Subramanian

Samarth Sikand

Joshy Ravindran

Peter Michael Lacy

Bargav Jayaraman

Aditya Priyadarshi

Pavel Valerievich Ponomarev

Akshay Kasera

Mainak Basu

Sundeep Singh

Rajan Dilavar Mithani

Anitha Chandran

Poulami Debnath

Swapnajeet Gon Choudhury

Manish Ahuja

Narendranath Sukhavasi

Raghotham M Rao

Shalini Chaudhari

Bhanu Anand

Bindu Madhav Tummalapalli

Parvathy Vijay Raghavan

Bhaskar Ghosh

Roshni Ramesh Ramnani

Nataraj Kuntagod

Satya Sai Srinivas

Pradeepkumar Duraisamy Soundrapandian

Sumitraj Ganapat Patil

Tirupal Rao Ravilla

Karthik Acharyulu

Rakesh Thimmaiah

Jagadeesh Chandra Bose Rantham Prabhakara

Kuntal Dey

Prakash Ghatage

Naveen Kumar Thangaraj

Annervaz Karukapadath Mohamedrasheed

Srikanth G Rao

Ranjana Bhalchandra Narawane

Amitabh Saxena

Tarun Singhal

Surya Kumar Ivg

Sanjay Mittal

Annervaz K M

Dongay Choudary Nuvvula

Chethana Dinakar

Dipin Era

Ravi Kiran Velama

Aditya Bhola

Harshawardhan Madhukar Wabgaonkar

Shrikanth Narayanaswamy Chandrasekaran

Sourav Bhattacharya

Subani Basha Nure

Jayati Deshmukh

Nikhil Chandrakant Khedkar

Rambhau Eknath Rote

Sebastian Fernandes

Yogesh Mallaiah

Rekha M Menon

Raghunandan Hemmige Keshava

Rupesh Kaslay

Nibedita Sarmah

Bruno Marra

Padmaraj Bhat

Rashmi Singh

Sachin Hanumantappa Waddar

Afsal Marattil

Chaitra Hareesh

Reeja Jose

Soumya Chandran

Shrikanth N C

Prashant Sahadev Sawant

Jagadeesh Chandra Bose R P

Srinivas Keshava Murthy

Niharika Gupta

Venkat Surya

Robert E Kress

Ashwin Menon

Kaushik Turlapaty

Subramanian Palanisami

Nishant Goel

Annervaz Karukapadath Mohamedrashe

Milind Savagaonkar

In some examples, recruitment process graph based unsupervised anomaly detection may include obtaining log data associated with a recruitment process for a plurality of candidates, and generating knowledge graphs and graph embeddings. The graph embeddings may be trained to include a plurality of properties such that graph embeddings of genuine candidate hires and fraudulent candidate hires are appropriately spaced in a vector space. The trained graph embeddings may be clustered to generate a plurality of embedding clusters that include a genuine candidate cluster, and a fraudulent candidate cluster. For a new candidate graph embedding for a new candidate, a determination may be made as to whether the new candidate graph embedding belongs to the genuine candidate cluster, to the fraudulent candidate cluster, or to an anomalous cluster, and instructions may be generated to respectively retain or suspend the new candidate.

Recruitment process graph based unsupervised anomaly detection

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, temporal impact analysis of cascading events on metaverse-based organization avatar entities may include determining a temporal impact of a metaverse event on a specified organization avatar entity. With respect to the specified organization avatar entity, a similarity of the metaverse event may be determined in a current temporal context to past events. A reaction plan of a plurality of reaction plans may be selected from an event database and based on the determined similarity. Based on an analysis of the temporal impact with respect to the selected reaction plan, instructions may be generated to execute the selected reaction plan by a metaverse operating environment.

Temporal impact analysis of cascading events on metaverse-based organization avatar entities

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, machine learning based semantic structural hole identification may include mapping each text element of a plurality of text elements of a corpus into an embedding space that includes embeddings that are represented as vectors. A semantic network may be generated based on semantic relatedness between each pair of vectors. A boundary enclosure of the embedding space may be determined, and points to fill the boundary enclosure may be generated. Based on an analysis of voidness for each point within the boundary enclosure, a set of void points and void regions may be identified. Semantic holes may be identified for each void region, and utilized to determine semantic porosity of the corpus. A performance impact may be determined between utilization of the corpus to generate an application by using the text elements without filling the semantic holes and the text elements with the semantic holes filled.

Machine learning based semantic structural hole identification

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

In some examples, human centered computing based digital persona generation may include generating, for a digital persona that is to be generated for a target person, synthetic video files and synthetic audio files that are combined to generate synthetic media files. The digital persona may be generated based on a synthetic media file. An inquiry may be received from a user of the generated digital persona. Another synthetic media file may be used by the digital persona to respond to the inquiry. A real-time emotion of the user may be analyzed based on a text sentiment associated with the inquiry, and a voice sentiment and a facial expression associated with the user. Based on the real-time emotion of the user, a further synthetic media file may be utilized by the digital persona to continue or modify a conversation between the generated digital persona and the user.

Human centered computing based digital persona generation

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, 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 learning system for automatically transmitting files according to user capabilities is provided. The learning system may include non-transitory memory storing instructions executable to transmit a file to a user device, and a processor circuitry configured to execute the instructions to determine a home base location of a user from at least one of a database storing user information of the user and the user device of the user. The processor circuitry further configured to calculate a travel distance from the home base location of the user to a hub circuitry of the learning system, determine a type of the file and an amount of content of the file based on the travel distance, and transmit, to the user device, the file according to the type of the file and the amount of content of the file.

Location aware learning system for content dispensation for resource-constrained edge devices

In some examples, software CoBot engineering, execution, and monitoring may include extracting a CoBot requirement from a requirement specification for a CoBot that is to be implemented. Based on application of a CoBot description language to the CoBot requirement, a CoBot workflow that specifies a plurality of tasks to be performed by the CoBot may be generated. A determination may be made as to whether a task is to be performed by a bot or by a human. A team that includes a plurality of bots and at least one human may be generated to execute the CoBot workflow. The bots of the team may be prioritized to identify a bot that is a best match to the CoBot requirement. The CoBot that includes configured bots may be deployed in an operational environment to perform the CoBot workflow.

Software CoBot engineering, execution, and monitoring

Growing community of inventors

Thane, India

Sanjay Podder