Introduction The evolution of communication technologies has necessitated a reevaluation of existing legal frameworks, particularly those concerning telecommunication practices. The Telephone Consumer Protection Act (TCPA) of 1991 exemplifies such legislation, which was originally designed to regulate telemarketing calls. However, as communication mediums expand and converge, the effectiveness and applicability of the TCPA come into question, especially in the context of modern technologies including text messaging and multimedia messaging services. This blog post seeks to analyze the implications of the recent legal case, Howard v. Republican National Committee, to elucidate how evolving communication forms challenge traditional regulatory frameworks, particularly within the LegalTech and AI sectors. Context and Analysis of TCPA in the Digital Age The TCPA was enacted with the intent to protect consumers from intrusive telemarketing practices. It specifically restricts the use of artificial or prerecorded voices in making or initiating calls to residential lines and cellular phones. However, the emergence of text messaging and multimedia communications creates complexities not anticipated at the time of the Act’s inception. The case of Howard v. RNC raises pertinent questions about whether sending a multimedia text message constitutes a “call” under the TCPA. The court ruled that a text message can be considered a call, reaffirming existing legal precedents, yet it simultaneously highlighted the limitations of the TCPA in addressing contemporary communication forms. Main Goals and Achievements The primary aim of the TCPA is to safeguard consumer privacy by regulating unwanted telemarketing communications. This goal can be achieved by adapting the statute to include emerging forms of communication, such as text messages enriched with multimedia content. The decision in Howard v. RNC underscores the necessity for legal frameworks to evolve in tandem with technological advancements, ensuring consumer protections remain robust in an increasingly digital landscape. Advantages of Adapting Legal Frameworks Enhanced Consumer Protection: Adapting the TCPA to cover new communication methods would strengthen consumer privacy protections, addressing the nuances of how marketing messages are delivered today. Legal Clarity: Updating regulations to reflect current technological realities would provide clearer guidelines for both consumers and marketers, reducing ambiguity in legal interpretations as evidenced in Howard v. RNC. Support for Innovation: A flexible regulatory environment can foster innovation in communication technologies while maintaining essential consumer protections. Alignment with Judicial Precedents: Aligning legal definitions with judicial interpretations and technological realities will ensure more consistent application of the law across different jurisdictions. Limitations and Caveats Despite the advantages outlined, there are limitations to adapting the TCPA. For instance, the rapid pace of technological advancement may outstrip legislative processes, leading to potential gaps in regulation. Moreover, any changes to the TCPA must carefully consider constitutional protections afforded to political advertising, which are often excluded from such regulations. Therefore, the challenge remains to strike a balance between consumer protection and constitutional rights. Future Implications of AI Developments on Communication Regulations As artificial intelligence continues to evolve, its impact on communication practices will likely necessitate further legal reconsiderations. AI-driven technologies, such as predictive analytics and automated messaging systems, are already reshaping how organizations interact with consumers. Future regulations will need to account for these advancements, ensuring that AI applications in communication do not infringe upon consumer privacy rights. The convergence of AI and communication will likely result in new legal precedents that address the unique challenges posed by automated interactions, thereby reinforcing the importance of adaptable legal frameworks. Conclusion The case of Howard v. RNC serves as a critical reminder of the need for legal frameworks, such as the TCPA, to evolve alongside technological advancements in telecommunications. By addressing the complexities introduced by modern communication methods, particularly in the realms of LegalTech and AI, regulators can enhance consumer protections and align legal definitions with current practices. As we move forward, it is imperative that legislators remain vigilant and proactive in adapting regulations to safeguard consumer rights in an ever-changing digital landscape. Disclaimer The content on this site is generated using AI technology that analyzes publicly available blog posts to extract and present key takeaways. We do not own, endorse, or claim intellectual property rights to the original blog content. Full credit is given to original authors and sources where applicable. Our summaries are intended solely for informational and educational purposes, offering AI-generated insights in a condensed format. They are not meant to substitute or replicate the full context of the original material. If you are a content owner and wish to request changes or removal, please contact us directly. Source link : Click Here
Contextualizing the Moonshot Initiative and Generative AI Applications The Japan Science and Technology Agency (JST) is at the forefront of a transformative research initiative known as the Moonshot program. This program aims to revolutionize the integration of artificial intelligence (AI) into daily life, with a particular focus on enhancing the quality of care for the aging population in Japan. By the year 2050, the Moonshot initiative envisions a world where AI-powered robots, capable of autonomous learning, are a common presence in households. This ambition is not solely limited to caregiving; it encapsulates a broader vision that includes ultra-early disease prediction and sustainable resource management. One of the key components of this initiative is the collaboration with NVIDIA, a leader in AI and robotics technology. The integration of NVIDIA’s advanced computing architectures into the development of robots, particularly those designed for elderly care, exemplifies how generative AI models and applications are paving the way for practical, humane solutions to societal challenges. Main Goals and Achievements of the Moonshot Initiative The primary aim of the Moonshot initiative, specifically under goal No. 3, is to develop robots that can assist with daily caregiving tasks, thereby alleviating the burden on human caregivers. Achieving this goal involves harnessing cutting-edge AI technologies and robotics to develop machines capable of performing complex caregiving activities such as cooking, cleaning, and personal hygiene assistance. This integration of AI into robotic systems is expected to enhance the quality of care for the elderly and improve their overall quality of life. Advantages of AI-Driven Robotics in Elderly Care Enhanced Autonomy: The AI-Driven Robot for Embrace and Care (AIREC) demonstrates an unprecedented level of operational autonomy, allowing it to perform tasks that traditionally required human intervention. This capability is crucial in a society facing a rising elderly population. Improved Quality of Life: By delegating caregiving tasks to robots, human caregivers can dedicate more time to emotional support and companionship, which are vital for the mental well-being of elderly individuals. Data-Driven Insights: The integration of NVIDIA’s technologies enables real-time data collection and analysis, which can inform care strategies and improve the effectiveness of caregiving interventions. Safety and Precision: Robots equipped with advanced sensors and AI algorithms can perform tasks—such as repositioning patients—safely and accurately, minimizing the risk of injury to both the caregiver and the patient. Limitations and Caveats While the potential benefits of AI-driven robotics in elderly care are substantial, there are limitations that must be considered. The technology’s reliability is contingent upon continuous advancements in AI and machine learning algorithms. Furthermore, ethical considerations surrounding the use of robots in personal care settings must be addressed, particularly concerning privacy and the emotional implications of robotic interactions. Future Implications of AI in Elderly Care The ongoing developments in generative AI models and applications are poised to significantly impact the future of elderly care. As AI algorithms become increasingly sophisticated, the capabilities of caregiving robots will expand, potentially allowing for more nuanced interactions with patients. This evolution may lead to a future where robots not only assist with physical tasks but also engage in meaningful communication, providing emotional support and companionship. Moreover, as society adapts to the integration of AI in caregiving, new frameworks for training, regulation, and ethical oversight will likely emerge. These developments will be crucial in ensuring that advancements in AI technology serve to enhance the quality of life for the elderly while respecting their autonomy and dignity. Disclaimer The content on this site is generated using AI technology that analyzes publicly available blog posts to extract and present key takeaways. We do not own, endorse, or claim intellectual property rights to the original blog content. Full credit is given to original authors and sources where applicable. Our summaries are intended solely for informational and educational purposes, offering AI-generated insights in a condensed format. They are not meant to substitute or replicate the full context of the original material. If you are a content owner and wish to request changes or removal, please contact us directly. Source link : Click Here
Contextual Overview of the Acquisition In a strategic move within the LegalTech sector, Filevine, a prominent provider of legal management software, has successfully acquired Pincites, a cutting-edge AI-driven drafting and contract redlining tool optimized for Microsoft Word. This acquisition is part of a broader trend where LegalTech companies are continuously innovating to enhance operational efficiencies and improve service delivery in the legal profession. Filevine’s previous acquisitions, including the virtual deposition platform Parrot and the AI-focused medical records chronology tool MedChron, underscore its commitment to expanding its capabilities and offerings. The company also raised $400 million in funding last year, further positioning itself as a leader in the sector. The integration of Pincites aligns with Filevine’s overarching goal to create a comprehensive legal operating ecosystem that streamlines various aspects of legal work. Main Goals of the Acquisition The primary objective of Filevine’s acquisition of Pincites is to enhance litigation-specific drafting capabilities while simultaneously improving contract redlining processes for transactional attorneys. By merging the expertise and technology of both companies, Filevine aims to develop a holistic platform that integrates various legal tasks—ranging from depositions to contract management—into one cohesive system. This integration is expected to deliver substantial productivity gains for legal professionals by allowing them to manage their workflows more efficiently. Advantages of the Acquisition Enhanced Drafting Technology: The combination of Pincites’ AI capabilities with Filevine’s existing systems promises to deliver superior drafting tools tailored specifically for litigation, which can significantly reduce the time attorneys spend on document preparation. Comprehensive Legal Platform: The integration of Pincites will enable a seamless flow of information across different legal tasks, thus reducing silos and enhancing collaboration among legal teams. Increased Efficiency: By automating routine tasks such as contract redlining, attorneys can focus on strategic decision-making and client interaction, thereby improving overall service quality. Improved User Experience: As indicated by Ryan Anderson, CEO of Filevine, the user-friendliness of Pincites’ product contributes to a more satisfying experience for attorneys, which can lead to higher adoption rates and overall productivity. Considerations and Limitations While the acquisition presents several advantages, it is essential to recognize potential limitations. The integration of different technologies may pose challenges in terms of compatibility and user adaptation. Additionally, there is the risk that the anticipated efficiencies may not be realized immediately as teams adjust to the new systems and processes. Future Implications of AI in LegalTech The landscape of LegalTech is poised for significant transformation as AI technologies continue to evolve. The integration of tools like Pincites into broader legal management platforms illustrates a shift towards more interconnected and intelligent systems that can augment legal professionals’ capabilities. In the long term, we can expect AI to play an increasingly central role in legal work, optimizing not only drafting and redlining but also predictive analytics, case law research, and more. As more firms adopt AI-driven solutions, the competitive dynamics within the legal profession may shift, rewarding those who are agile in adapting to technological advancements. Legal professionals will need to stay abreast of these developments to leverage AI effectively and maintain a competitive edge in an increasingly digital landscape. Disclaimer The content on this site is generated using AI technology that analyzes publicly available blog posts to extract and present key takeaways. We do not own, endorse, or claim intellectual property rights to the original blog content. Full credit is given to original authors and sources where applicable. Our summaries are intended solely for informational and educational purposes, offering AI-generated insights in a condensed format. They are not meant to substitute or replicate the full context of the original material. If you are a content owner and wish to request changes or removal, please contact us directly. Source link : Click Here
Contextual Overview of AI in Online Dispute Resolution The integration of artificial intelligence (AI) in online dispute resolution (ODR) represents a significant evolution in legal technology, shaping the landscape for legal professionals and clients alike. Dyspute.ai’s recent launch of Adri v2, a 24/7 asynchronous AI mediation platform, exemplifies this trend, enabling enhanced accessibility and efficiency in dispute resolution. The inception of ODR dates back to the mid-1990s, a time when the internet was emerging as a viable alternative forum for resolving disputes. Since then, interest in the intersection of technology and legal processes has grown, leading to innovations that streamline resolution mechanisms and reduce the burden on traditional judicial systems. For legal professionals, the increasing reliance on AI tools can facilitate a more efficient workflow, allowing them to focus on complex cases while routine disputes can be managed effectively through automated platforms. This transition not only enhances service delivery but also democratizes access to legal resources, making mediation available to a broader audience. Main Goal of Dyspute.ai’s Adri v2 The primary goal of Dyspute.ai’s Adri v2 is to provide an efficient, round-the-clock mediation platform that leverages AI technology to facilitate conflict resolution without the constraints of traditional scheduling. This objective can be achieved through the deployment of sophisticated algorithms that analyze disputes, propose resolutions, and enable communication between parties asynchronously. The platform aims to reduce the time and costs associated with mediation while maintaining the integrity and confidentiality of the process. By implementing such a system, Dyspute.ai seeks to not only enhance user experience but also to promote the acceptance of AI in legal contexts, thereby paving the way for broader adoption of technology in conflict resolution. Advantages of Adri v2 The introduction of Adri v2 presents several advantages for legal professionals and users engaged in dispute resolution: 1. **24/7 Availability**: Unlike traditional mediation that requires scheduling, Adri v2 functions continuously, allowing users to engage with the platform at their convenience, thus catering to a global audience across various time zones. 2. **Cost Efficiency**: By automating many aspects of the mediation process, the platform reduces operational costs, making mediation more financially accessible for individuals and small businesses. 3. **Speed of Resolution**: The asynchronous nature of the platform allows for quicker exchanges of information and proposals, potentially leading to faster resolutions compared to conventional methods. 4. **Data-Driven Insights**: Adri v2 utilizes data analytics to identify patterns in disputes, which can help legal professionals understand common issues and develop proactive strategies for future cases. 5. **Enhanced User Experience**: The intuitive interface of the platform is designed to simplify the mediation process, making it more user-friendly for individuals who may not be familiar with legal procedures. While these advantages are substantial, it is essential to recognize potential limitations, such as the need for human oversight in complex cases where emotional intelligence and nuanced understanding are paramount. Additionally, the reliance on technology may inadvertently exclude individuals lacking access to digital tools or internet connectivity. Future Implications of AI in Legal Dispute Resolution As AI technology continues to evolve, its implications for online dispute resolution will likely expand significantly. Future developments may include enhanced predictive analysis that anticipates dispute outcomes based on historical data, as well as the integration of machine learning algorithms that improve the mediation process over time. Moreover, as legal professionals become more accustomed to utilizing AI tools, there may be a cultural shift within the legal sector towards embracing technology as a collaborative partner in the dispute resolution process. This evolution will necessitate ongoing education and adaptation among legal practitioners to ensure they can effectively leverage these technologies to benefit their clients. In conclusion, advancements in AI, exemplified by platforms like Dyspute.ai’s Adri v2, herald a transformative era in online dispute resolution. By facilitating greater access, efficiency, and insight, these technologies are poised to redefine the role of legal professionals and the landscape of conflict resolution in the coming years. Disclaimer The content on this site is generated using AI technology that analyzes publicly available blog posts to extract and present key takeaways. We do not own, endorse, or claim intellectual property rights to the original blog content. Full credit is given to original authors and sources where applicable. Our summaries are intended solely for informational and educational purposes, offering AI-generated insights in a condensed format. They are not meant to substitute or replicate the full context of the original material. If you are a content owner and wish to request changes or removal, please contact us directly. Source link : Click Here
Contextual Framework of AI Agent Orchestration The rapid evolution of artificial intelligence (AI) has transitioned the discourse from merely inquiring about the capabilities of AI agents to a more nuanced exploration of their collaborative effectiveness. In enterprise environments, a pivotal consideration is whether AI agents are effectively communicating and coordinating with one another. This orchestration across multi-agent systems is not only critical but also serves as a distinctive factor that can set organizations apart in a competitive landscape. As highlighted by Tim Sanders, Chief Innovation Officer at G2, the lack of orchestration can lead to significant misunderstandings among agents, akin to individuals conversing in disparate languages. Such miscommunications can compromise the quality of operational outcomes and elevate risks, including data security breaches and misinformation. Main Goal of AI Agent Orchestration The central objective of orchestrating AI agents is to enhance their collaborative capabilities, thereby improving overall operational efficiency and decision-making quality. Achieving this goal necessitates the implementation of sophisticated orchestration platforms that facilitate seamless interaction among various AI agents and robotic process automation (RPA) systems. As the landscape evolves, organizations must transition from traditional data-centric orchestration to action-oriented collaborative frameworks that can dynamically adapt to real-time operational needs. Advantages of Effective AI Agent Orchestration 1. **Enhanced Communication**: Orchestration platforms promote effective agent-to-agent communication, mitigating the risks of misunderstandings. This facilitates a more coherent and efficient workflow, which is particularly crucial in environments requiring real-time decision-making. 2. **Increased Operational Consistency**: By coordinating diverse agentic solutions, organizations can achieve more consistent outcomes. This is akin to the transition observed in answer engine optimization, where the focus has shifted from mere monitoring to generating tailored content and code. 3. **Improved Risk Management**: The evolution of orchestration tools towards technical risk management enhances quality control. Organizations can implement agent assessments and proactive scoring to evaluate agent reliability, thereby minimizing the likelihood of operational disruptions caused by erroneous actions. 4. **Streamlined Processes**: Advanced orchestration platforms can automate tedious approval processes, significantly reducing ‘ticket exhaustion’ caused by excessive human intervention in agent workflows. This allows organizations to realize velocity gains, moving from marginal improvements to substantial enhancements in efficiency. 5. **Democratization of AI Development**: With the advent of no-code agent builder platforms, the ability to create functional AI agents is becoming accessible to a broader range of users. This democratization fosters innovation and enables diverse stakeholders to contribute to the development of AI solutions. Considerations and Limitations While the advantages of AI agent orchestration are compelling, there are essential caveats to consider. The successful integration of orchestration platforms requires a comprehensive understanding of existing automation stacks. Organizations must conduct thorough inventories of their technological assets to prevent dis-synergies that may arise from the coexistence of legacy systems and cutting-edge technologies. Additionally, the transition from a human-in-the-loop to a human-on-the-loop paradigm may necessitate cultural shifts within organizations, as employees adapt to new roles in designing and overseeing AI workflows. Future Implications of AI Agent Orchestration The trajectory of AI development indicates that orchestration capabilities will continue to evolve, with implications that extend well beyond current capabilities. As organizations increasingly rely on AI for critical functions, the sophistication of orchestration tools will likely expand. Future advancements may include enhanced predictive capabilities, allowing organizations to anticipate and preemptively address potential challenges in agent interactions. Furthermore, the proliferation of generative AI models will necessitate ongoing refinement of orchestration strategies to ensure that AI systems can collaboratively generate high-quality outputs while mitigating the risks associated with misinformation and operational failures. In conclusion, the orchestration of AI agents represents a transformative opportunity for organizations aiming to enhance their operational efficiency and decision-making capabilities. By prioritizing effective communication and coordination among AI systems, enterprises can navigate the complexities of modern workflows, ultimately positioning themselves for sustained success in an increasingly AI-driven landscape. Disclaimer The content on this site is generated using AI technology that analyzes publicly available blog posts to extract and present key takeaways. We do not own, endorse, or claim intellectual property rights to the original blog content. Full credit is given to original authors and sources where applicable. Our summaries are intended solely for informational and educational purposes, offering AI-generated insights in a condensed format. They are not meant to substitute or replicate the full context of the original material. If you are a content owner and wish to request changes or removal, please contact us directly. Source link : Click Here
Context and Background The legal landscape is undergoing significant transformation, particularly with the advent of artificial intelligence (AI) and alternative legal service providers (ALSPs) like Eudia, a notable market disruptor. Eudia uniquely integrates AI technology, legal services, and consultancy to cater specifically to the needs of in-house legal teams. As articulated by CEO Omar Haroun, the company aims to redefine the legal technology framework by addressing the dichotomy between the requirements of in-house teams and traditional law firms. Eudia’s strategic goal for 2026 is to double its annual recurring revenue (ARR), building upon a successful foundation established with Fortune 500 clients. Main Goals and Achievements The primary goal of Eudia is to enhance the operational efficiency and effectiveness of in-house legal teams by leveraging sophisticated AI-driven platforms. As highlighted in the original content, Eudia seeks to provide measurable business outcomes, such as reducing external counsel expenses by 20% and significantly minimizing the time spent on contract review processes. To achieve this, Eudia has developed a comprehensive suite of tools that include a data platform, a knowledge management system, and an AI platform specifically tailored for the unique challenges faced by in-house legal departments. Advantages of Eudia’s Approach Targeted Solutions for In-House Teams: Eudia acknowledges that the needs of in-house legal teams often conflict with those of law firms. By focusing exclusively on in-house requirements, Eudia can provide tailored solutions that address specific pain points. Significant Cost Savings: The company aims to deliver concrete financial benefits, such as reducing outside legal expenses and streamlining contract review processes, which can lead to substantial cost reductions for organizations. Enhanced Productivity: Eudia’s AI-driven platforms are designed to improve productivity not just incrementally but exponentially, enabling legal teams to operate more efficiently and effectively. Measurable Outcomes: Eudia emphasizes the importance of ROI and key performance indicators (KPIs), ensuring that clients can see tangible results from their investments in legal technology. Human-AI Collaboration: Unlike other tech companies that may overlook the human element, Eudia integrates skilled legal professionals with AI systems, enhancing the quality of output and ensuring accountability in legal processes. Limitations and Caveats Despite the numerous advantages presented by Eudia’s approach, there are inherent limitations. The reliance on AI tools necessitates a cultural shift within legal teams that may encounter resistance. Furthermore, the effectiveness of AI solutions is contingent upon the quality and quantity of data provided by clients. Organizations that are not prepared to invest in these technologies or that lack a clear strategy for integration may not realize the expected benefits. Future Implications in Legal Technology The trajectory of AI in legal tech indicates that as adoption increases, we are likely to witness a fundamental shift in how legal services are delivered. The integration of AI will not only automate routine tasks but also unlock new capabilities, enabling lawyers to focus on higher-value strategic activities. Eudia’s model could serve as a blueprint for future legal practices, emphasizing efficiency, cost-effectiveness, and client-centric service delivery. As the industry evolves, law firms will need to adapt to these changes, potentially leading to more collaborative environments where AI and human expertise coexist harmoniously. Disclaimer The content on this site is generated using AI technology that analyzes publicly available blog posts to extract and present key takeaways. We do not own, endorse, or claim intellectual property rights to the original blog content. Full credit is given to original authors and sources where applicable. Our summaries are intended solely for informational and educational purposes, offering AI-generated insights in a condensed format. They are not meant to substitute or replicate the full context of the original material. If you are a content owner and wish to request changes or removal, please contact us directly. Source link : Click Here
Context and Relevance The rapid evolution of Generative AI Models, particularly Large Language Models (LLMs), necessitates an efficient framework for deployment and management. As AI builders strive to incorporate diverse LLM architectures and specialized variants into applications, the complexities of testing and deployment can severely hinder progress. This post addresses the critical need for streamlined deployment methods, emphasizing NVIDIA’s NIM (NVIDIA Inference Microservices) as a pivotal tool for AI scientists and developers working within the Generative AI sector. Main Goal and Achievement Strategy The primary goal articulated in the original post is to facilitate the rapid and reliable deployment of LLMs through NVIDIA’s NIM framework. By leveraging NIM’s capabilities, users can effectively manage the intricacies of diverse LLM architectures without the need for extensive manual configuration. The structured workflow provided by NIM, which automates model analysis, architecture detection, backend selection, and performance setup, serves as a blueprint for achieving this goal. To realize these benefits, users must ensure their environments are equipped with compatible NVIDIA hardware and software prerequisites, ultimately leading to enhanced innovation and reduced time-to-market for AI applications. Advantages of Using NVIDIA NIM Simplified Deployment: NIM provides a single Docker container that supports a broad range of LLMs, enabling users to deploy models with minimal manual intervention. This automation reduces the complexity typically associated with managing multiple inference frameworks. Enhanced Performance: The framework optimizes performance by automatically selecting appropriate inference backends based on model architecture and quantization formats, which in turn improves operational efficiency. Support for Diverse Formats: NIM accommodates various model formats, including Hugging Face Transformers and TensorRT-LLM checkpoints, thus broadening the scope of available models for deployment. Rapid Access to Models: With access to over 100,000 LLMs hosted on Hugging Face, users can quickly integrate state-of-the-art models into their applications, promoting innovation and reducing development cycles. Community Engagement: The integration with the Hugging Face community facilitates feedback and collaboration, which is vital for continuous improvement and adaptation of the deployment framework. Caveats and Limitations While NVIDIA NIM presents numerous advantages, users should be aware of certain limitations. The requirement for specific NVIDIA GPUs and the need for a properly configured environment may pose accessibility challenges for some users. Additionally, the complexity of certain models may still necessitate advanced user knowledge to optimize deployment fully. Future Implications The advancements in AI deployment frameworks like NVIDIA NIM herald a transformative era for Generative AI applications. As the demand for sophisticated AI solutions continues to grow, the seamless integration of LLMs into various sectors, including healthcare, finance, and entertainment, will likely accelerate. Future developments in AI will demand increasingly efficient deployment strategies, making tools that simplify these processes indispensable for researchers and developers alike. The continuous evolution of NVIDIA NIM and similar frameworks will be crucial in meeting these burgeoning demands, shaping the future landscape of AI-driven applications. Disclaimer The content on this site is generated using AI technology that analyzes publicly available blog posts to extract and present key takeaways. We do not own, endorse, or claim intellectual property rights to the original blog content. Full credit is given to original authors and sources where applicable. Our summaries are intended solely for informational and educational purposes, offering AI-generated insights in a condensed format. They are not meant to substitute or replicate the full context of the original material. If you are a content owner and wish to request changes or removal, please contact us directly. Source link : Click Here
