Accenture Global Solutions Limited

Mathangi Sandilya

Sanjay Tiwari

Srikanth G Rao

Shantha Maheswari

Badarayan Panigrahi

Tarun Singhal

Surya Kumar Ivg

Gaurav Khanduri

Shubhashis Sengupta

Mayur Kolhe

Tarang Kumar

Marcio Miranda Theme

VijayChandar Natesan

Anand Vijendra

Azarudeen Anifa

Arunabh Sinha

Jayaprakash Nooji Shekar

Anshul Kapoor

Carl Matthew Dukatz

Dongay Choudary Nuvvula

Bolaka Mukherjee

Kung-Chuan Hsu

Ravi Shankar Nori

Avishek Gulshan

Vipin Anilkumar Bhasin

Ankur Garg

Indranil Choudhuri

Chetan Thukral

Mohit Mahajan

Shihab Ponnampadikkal Abdul Rahiman

Sachin Khandelwal

Mamta Kanad Bhagwat

Swati Rohit Ghiya

T Siva Rama Sarma

Binit Agrawal

Saisandeep Sanku

Anup Kumar Tiwary

Nitima Singhal

Sagnik Mazumder

Abhinav Shukla

Raja Sekhar Velagapudi

Issac Abraham Alummoottil

Rahel James Kale

Chandra Nanduri

Veera Raghavan Valayaputhur

J Nooji Shekar

Omkar Sudhakar Deorukhkar

Manish Phukan

Juhi Mandot

Swati Dubey

Poonam Mishra

A device may identify standard parameters and real-time parameters associated with content of a content type, and may process the content type, the standard parameters, and the real-time parameters, with a parameter unification model, to generate derived parameters for the content. The device may process the derived parameters and the content type, with a multi-level linear regression machine learning model, to calculate a content score for the content, and may process the derived parameters and the content score, with a linear regression machine learning model, to calculate a quantity of f-NFTs to generate for the content and a divestment ratio. The device may create a unique reference to the content, and may create an NFT for the content based on the unique reference. The device may generate the quantity of f-NFTs for the content based on the NFT, and may provide the quantity of f-NFTs to a content exchange.

Determining an optimum quantity of fractional non-fungible tokens to generate for content and a content exchange

Provided are systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Intelligent network operation platform for network fault mitigation

A plurality of personalized news feeds are generated from input feeds including digital content items based on a dynamic taxonomy data structure. Entities are extracted from the input feeds and relationship strengths are obtained for the extracted entities and the digital content items. The dynamic taxonomy data structure is updated with the extracted entities and entries for the digital content news items are included at the corresponding branches based on the relationship strengths. Attributes are obtained for the entities and those entities corresponding to the trending topics are identified. Personalized news feeds are generated including the digital content items listed under the entities. Digital content items are added or removed from the digital content feeds based on one or more entity attributes.

Dynamic taxonomy builder and smart feed compiler

Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

A machine learning (ML) based code transformation system that transforms a source programming code developed using a source library for execution on a source platform into remediated code for execution on a target platform is disclosed. Metadata extracted from the source programming code is used to detect the source programming language, source libraries, and the source platform. The metadata also enables modularizing the source programming code based on the functionality and identifying a node from a plurality of nodes in a communication network to execute the various source code modules. A similarity map is generated mapping the source libraries to the target libraries and the source code modules that are incompatible with the target platform are identified and remediated with similar target code modules using the similarity map.

Machine learning (ML) powered programming code transformations

A device determines, from a network slice template associated with a network slice, a quality of service (QoS) profile for the network slice that includes performance metrics for corresponding QoS parameters associated with providing a service. The device monitors performance of the network slice in association with the QoS profile, and determines, based on the performance, that a performance indicator for a QoS parameter of the network slice is outside a threshold range of a performance metric. The device determines, based on the performance information and the QoS profile, a slice modification to the network slice template for the network slice, where the slice modification is configured to cause the performance indicator to be within the threshold range of the performance metric. The device causes a network slice orchestrator to update an instantiation of the network slice according to the slice modification and the network slice template.

Quality of service manager for network slices of a network

Methods, systems, and apparatus for root cause analysis in a communication network. In one aspect, a method includes providing a quantum computer with data representing a topology of the communication network, the topology comprising a graph of vertices representing network devices and edges representing connections between network devices; receiving, from the quantum computer, data representing a first subset of network devices, wherein the first subset comprises a dominating set of vertices or a vertex cover for the graph; monitoring network devices in the first subset to generate alarm data representing triggered network device alarms; providing the alarm data to a quantum computer; receiving, from the quantum computer, data representing a second subset of network devices, wherein the second subset comprises a set cover for the alarm data and the network devices in the second subset comprise diagnosed sources of failures in the communication network.

Quantum computing in root cause analysis of 5G and subsequent generations of communication networks

A dynamic application testing and scoring system that tests applications under various tenets and categorizes the applications to be published, to be further reviewed or rejected. The results from administering a plurality of tests to an application are analyzed by applying rulesets that pertain to criteria under each of the tenets. An application score is determined from the analysis using weights associated with the tenets, priority levels of the criteria and the severity levels of the rulesets. The application score identifies a position for the application on a scoring scale relative to two trust threshold values. The application is categorized based on the position. Feedback regarding the categorization is received and the trust threshold values on the scoring scale can be adjusted if the categorization in the feedback is different from the categorization produced by the scoring system.

Artificial intelligence based application testing and scoring

A device may receive site data identifying a site associated with a telecommunications network. The device may receive RF drive test data identifying signal strengths of the site at different location points. The device may receive network KPI data and throughput data associated with the site. The device may assign unique identifiers to the different location points. The device may determine, based on the site data, the RF drive test data, the network KPI data, and the unique identifiers, at least one of reference signal strength data for each of the different location points or downlink channel data for each of the different location points. The device may determine degraded signal strength data identifying a degraded signal strength of each of the different location points, if the site is decommissioned and based on the at least one of the reference signal strength data or the downlink channel data.

Analyzing a communication network device, based on coverage analyses associated with decommissioning the communication network device, to determine next actions for 5G rollout

A device may receive video data associated with a video, and may identify image data associated with the video data and that corresponds to a frame of the video. The device may process the image data, with an image analysis model, to determine an image compliance score associated with a policy and with context of the image data, and may identify text data associated with the video data. The device may process the text data, with a text analysis model, to determine a text compliance score associated with the policy and with context of the text data, and may determine, based on the image compliance score and the text compliance score, an aggregate compliance score associated with the frame. The device may determine whether the aggregate compliance score satisfies a threshold associated with the policy, and may perform actions based on whether the aggregate compliance score satisfies the threshold.

Utilizing machine learning models to identify context of content for policy compliance determination

An intelligent return path data (iRPD) system enables transmission of return path data via a communication network for a television connected to a non-hybrid set top box (STB). The iRPD system is configured to receive the key codes of the keys pressed on a remote control device along with the date time stamps and the location information. The iRPD system analyzes the keypress data along with the date time stamps to recognize the channels accessed in programming operations and the non-programming control operations executed by a viewer operating the remote control device. The viewer's behavior pattern is thus recorded and analyzed to identify the viewer. Upon identifying the viewer, various functions such as collecting the viewership statistics, implementing metered usage billing or ecommerce activities are enabled.

Enabling return path data on a non-hybrid set top box for a television

In some examples, natural language processing based sign language generation may include ascertaining a speech video that is selected by a user, and determining, based on application of natural language processing to contents of the speech video, a plurality of sentences included in the speech video. For each sentence of the plurality of sentences identified in the speech video, a sign language sentence type, a sign language sentence structure, and a sentiment may be determined. For each sign language sentence structure and based on a corresponding sentiment, a sign video may be determined. Based on the sign video determined for each sentence of the plurality of sentences identified in the speech video, a combined sign video may be generated.

Natural language processing based sign language generation

A device identifies a subset of network performance indicator (NPI) values that measure network performance during peak usage time periods, selects particular NPI values associated with the peak usage time periods, based on one or more of the subset of NPI values satisfying particular threshold network performance values, and compares the particular NPI values with threshold upper and lower bound network performance values to determine weighted scores for the particular NPI values. The device determines, by processing the weighted scores, a weighted mean score that represents a mean of the weighted scores, and determines, based on an analysis of the particular NPI values and the weighted mean score, an overall site score that represents a viability level in upgrading the cell site. The device generates, based on the overall site score, a plan to upgrade one or more network technologies associated with the cell site.

Identifying a cell site as a target for utilizing 5th generation (5G) network technologies and upgrading the cell site to implement the 5G network technologies

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Bengaluru, India

Mathangi Sandilya