Accenture Global Solutions Limited

Accenture Global Services Gmbh

Janardan Misra

Sanjay Podder

Shubhashis Sengupta

Vikrant Shyamkant Kaulgud

Divya Rawat

Neville Dubash

Milind Ravikiran Savagaonkar

Subhabrata Das

Gary Titus

Sanjay Mittal

Narendranath Sukhavasi

Bhaskar Ghosh

Kapil Singi

Annervaz K M

Ravi Kiran Velama

Kuntal Dey

Vibhu Saujanya Sharma

Sakshi Jain

Rohit Mehra

Poulami Debnath

Karthik Acharyulu

Sayantan A Mitra

Nibedita Sarmah

Rupesh Kaslay

Adam Patten Burden

Rashmi Singh

Naveen Gordhan Balani

Navveen Gordhan Balani

K M Annervaz

Ruchika Sharma

Sachin Hanumantappa Waddar

Ashwin Menon

Robert E Kress

Venkat Surya

Srinivas Keshava Murthy

Milind Savagaonkar

Methods, systems and apparatus, including computer programs encoded on computer storage medium, for allocating computation resources using ESG reporting. In one aspect a method includes obtaining data from a knowledge source for an entity, the knowledge source comprising a plurality of ESG disclosures that relate to one or more ESG dimensions; computing vulnerability indicator scores that represent measures of latent vulnerability with respect to the ESG dimensions; computing descriptive distribution scores that represent distributions of descriptions of the ESG dimensions within the knowledge source; determining, using the vulnerability indicator scores and the descriptive distribution scores, an allocation of computational resources to ESG computational processes associated with the ESG dimensions that achieves an increased gain in sustainability for the entity; and initiating allocation of the computational resources to the ESG computational processes according to the determined allocation.

System and method for ESG reportng based optimized resource allocation across ESG dimensions

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 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, 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

A device may identify unique segments within data objects, of an object corpus stored in a data structure, as elements, and may generate an embedding space based on unique elements and mappings of the data objects to embeddings. The device may estimate semantic proximities among the data objects based on the mappings, and may build a semantic cohesion network among the data objects based on the semantic proximities. The device may identify semantically cohesive data clusters in the semantic cohesion network, and may sort the data objects in the semantically cohesive data clusters. The device may determine, from the semantically cohesive and sorted data clusters, a home data cluster for a new data object, and may store bookkeeping details of the new data object in the data structure based on the new data object being semantically similar to the data object in the home data cluster.

Providing energy efficient dynamic redundancy elimination for stored data

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

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, 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, collaborative learning-based cloud migration implementation may include identifying a migration agent that is to perform an application migration from a first cloud environment to a second cloud environment, and identifying a plurality of additional migration agents. A technical context and a migration flow context may be determined for the migration agent and for the plurality of additional migration agents. Executed allowed and error-response migration actions may be identified for states that are similar to a current state of the application migration, and a similarity between the migration agent and each of the migration agents that executed the allowed and error-response migration actions may be determined. A migration action that is to be performed may be identified based on a maximum relevance associated with the allowed and error-response migration actions. The identified migration action may be executed by the migration agent to perform the application migration.

Collaborative learning-based cloud migration implementation

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

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for allocating computing resources for a data object. The system obtains a plurality of characteristics of a data object, and estimates, from the obtained characteristics, one or more cumulative sustainability metrics characterizing one or more categories of energy consumption during a life-cycle of the data object. The system further determines, from the cumulative sustainability metrics, allocations of one or more computing resources to the data object to optimize one or more objectives including minimizing a cumulative carbon cost during the life-cycle of the data object.

Sustainability-based computing resource allocation

In some examples, system behavior profiling-based dynamic competency analysis may include identifying a plurality of software generation entities that have contributed to a module of a system, and generating an index to associate each software generation entity of the plurality of software generation entities. Execution links may be extracted from execution traces of the system, and an execution competency list may be generated. A dynamic competency score may be generated for each software generation entity for the system, and an overall dynamic competency score and a combined competency score may be determined. A software generation entity role may be obtained for a new application, and a software generation entity of the plurality of software generation entities may be identified to perform the software generation entity role. Development of the new application may be implemented using the identified software generation entity.

System behavior profiling-based dynamic competency analysis

In some examples, natural language dialogue system perturbation testing may include identifying semantic segments for conversation data for a natural dialogue system. For each semantic segment, a perturbed variant that includes a perturbation may be generated, and forwarded to the natural dialogue system. An updated response to the perturbed variant may be obtained from the natural dialogue system. A semantic similarity may be determined between an original response to a semantic segment and the updated response, and based on the semantic similarity between the original response and the updated response, a perturbability of the natural dialogue system may be determined. A determination may be made as to whether the perturbability of the natural dialogue system is greater than a specified perturbability threshold, and if so, a training corpus that includes a failed response to a perturbed variant may be utilized to train the natural dialogue system.

Natural language dialogue system perturbation testing

A device may identify, for a first analytics application, a first set of characteristics and obtain, for a second analytics application, a second set of characteristics. The device may determine a measure of similarity between the first analytics application and the second analytics application based on the first set of characteristics and the second set of characteristics. The device may also determine a relevance score for a feature of the first analytics application, the relevance score being based on a relevance score associated with a feature of the second analytics application. In addition, the device may determine a relevance score for a machine learning technique associated with the first analytics application, the relevance score being based on a relevance score associated with a machine learning technique associated with the second analytics application. Based on the first relevance score or the second relevance score, the device may perform an action.

Recommending machine learning techniques, features, and feature relevance scores

A device may receive, from a user device, a request for an automation service for a project. The device may analyze the request to identify one or more input keywords. The device may obtain, by using the one or more input keywords to search one or more data sources, project information for a set of projects and/or automation services information for a set of automation services. The device may determine one or more similar projects and/or one or more similar automation services by analyzing the project information and/or the automation services information. The device may provide, to the user device, a recommendation that identifies one or more automation services associated with the one or more similar projects and/or the one or more similar automation services.

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Bengaluru, India

Janardan Misra