{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9854029","patent":{"patent_number":"US-9854029","title":"Systems for determining improper assignments in statistical hypothesis testing","assignee":null,"inventors":[],"filing_date":"2014-11-04T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["H04L","G06Q","G06Q"],"num_claims":20,"abstract":"Described are techniques for performing statistical hypothesis experiments and determining user responses that do not match an assigned treatment. One of multiple experiment states may be assigned to a set of client devices. Response data that includes indications of the experiment state that was executed may be compared to the assigned experiment state to determine allocation matches and allocation mismatches. The allocation mismatches may be separated based on the assigned and executed experiment states to determine mismatches caused by preexisting content stored on a client device and mismatches caused by other experiment parameters."},"analysis":{"summary":"Systems for Determining Improper Assignments in Statistical Hypothesis Testing is a patent that offers a solution for identifying and correcting errors in statistical hypothesis experiments. The core innovation lies in its ability to compare assigned and executed experiment states, pinpointing mismatches that can skew results. The problem being solved is the inaccuracy of data in A/B testing, clinical trials, and other experiments due to improper assignments. This patent tackles this issue by employing techniques to separate mismatches caused by pre-existing content stored on a client device from those caused by other experiment parameters.\n\nThe key technical approach involves collecting response data that includes indications of the experiment state that was executed and comparing it to the assigned experiment state to determine allocation matches and mismatches. The business value of this innovation is significant, as it improves the reliability and accuracy of experimental data, leading to better decision-making and more efficient resource allocation. The technology can be applied in various industries, including marketing, healthcare, and research, where statistical hypothesis testing is commonly used.\n\nThe market opportunity for this patent is substantial, as the need for accurate data analysis continues to grow. Companies and researchers are increasingly relying on statistical experiments to inform their strategies, making the ability to identify and correct errors critical. By providing a robust solution for ensuring data integrity, this technology can help organizations make more informed decisions and achieve better outcomes. The ability to distinguish between mismatches caused by pre-existing content and other experimental parameters offers a unique advantage, allowing for more precise error correction. This contributes to improved experimental designs and more reliable findings. The patent enables faster iteration and more efficient resource allocation by reducing the noise in experimental data. Researchers can quickly identify and address issues, leading to quicker insights and improved decision-making.\n\nIn conclusion, Systems for Determining Improper Assignments in Statistical Hypothesis Testing addresses a critical need for accuracy in statistical hypothesis experiments. Its innovative approach to identifying and correcting assignment errors offers significant business value and market opportunity. By improving the reliability of experimental data, this technology empowers organizations to make more informed decisions and achieve better outcomes.","layman_explanation":"Systems for Determining Improper Assignments in Statistical Hypothesis Testing is a technology designed to improve the accuracy of experiments and tests, particularly those used in business and research. Imagine you're running a test to see if a new website design leads to more sales. You divide your website visitors into two groups: one sees the old design, and the other sees the new design. This is a simplified example of statistical hypothesis testing.\n\n**1. What Problem Does This Solve?**\n\nIn real-world scenarios, things don't always go as planned. Some visitors assigned to see the old design might accidentally see the new design due to technical glitches, caching issues, or other unforeseen problems. This 'improper assignment' skews the results of your test, making it difficult to determine if the new design is truly better. Existing solutions often fail to catch these subtle errors, leading to inaccurate conclusions and potentially poor business decisions.\n\n**2. How Does It Work?**\n\nThis patent acts like a quality control system for your experiments. It compares what was *supposed* to happen (the assigned design) with what *actually* happened (what the visitor saw). If there's a mismatch, the system flags it as an error. Think of it like a mail sorter that checks if each letter is going to the right address. If a letter is misaddressed, the sorter corrects it before it reaches the wrong person. The system then categorizes these errors. For example, it can distinguish between errors caused by technical glitches (like caching) versus errors caused by other factors. This allows you to understand the *source* of the problem, not just the fact that there *is* a problem.\n\n**3. Why Does This Matter?**\n\nAccurate data is the lifeblood of good decision-making. If your A/B tests are flawed, you might make changes to your website or marketing campaigns that actually *hurt* your business. This technology provides a more reliable foundation for these decisions. By improving the accuracy of your experiments, you can make more confident choices about product development, marketing strategy, and other critical business areas. This leads to increased ROI on your testing efforts and a better understanding of your customers.\n\n**4. What's Next?**\n\nAs businesses become increasingly data-driven, the demand for accurate and reliable testing methods will continue to grow. This technology could be integrated into existing A/B testing platforms, analytics tools, and other business software. In the future, we might see even more sophisticated error detection and correction techniques, further improving the accuracy of experimental data. This could lead to a new era of data-driven decision-making, where businesses can make more informed choices with greater confidence. The market adoption timeline will depend on how quickly businesses recognize the value of accurate testing and how easily this technology can be integrated into existing workflows. Investment implications are positive, as companies that adopt this technology stand to gain a competitive advantage by making better, more data-driven decisions.","technical_analysis":"Systems for Determining Improper Assignments in Statistical Hypothesis Testing presents a robust technical solution for ensuring the integrity of statistical hypothesis testing. The core of this innovation lies in its meticulous comparison of assigned experiment states with executed experiment states. This comparison allows the system to identify discrepancies, or mismatches, which can significantly skew experimental results. The architecture involves several key components working in concert to achieve this goal.\n\nFirst, the system employs data collection modules on client devices to gather information about both assigned and executed experiment states. The assigned state represents the intended treatment or condition for a given device, while the executed state reflects the actual treatment or condition experienced. This data is then transmitted to a central server for analysis. The heart of the system is the comparison algorithm, which analyzes the collected data to identify mismatches. This algorithm employs a variety of techniques, including pattern matching and statistical analysis, to detect discrepancies between assigned and executed states. One of the key technical challenges addressed by the patent is the differentiation between mismatches caused by pre-existing content and those caused by other experimental parameters. This requires sophisticated analysis to isolate the sources of error.\n\nTo address this challenge, the system incorporates a filtering mechanism that identifies and removes the effects of pre-existing content. This mechanism uses information about the client device's configuration and history to identify potential sources of interference. The system also incorporates a feedback loop that allows it to learn from past errors and improve its accuracy over time. This feedback loop uses machine learning techniques to identify patterns in the data and adjust the comparison algorithm accordingly. The implementation details of the system involve a combination of software and hardware components. The software components are responsible for data collection, analysis, and reporting, while the hardware components provide the necessary computing power and storage capacity.\n\nThe system is designed to be scalable and efficient, capable of handling large volumes of data without compromising performance. This is achieved through the use of distributed computing techniques and optimized data structures. The patent has significant implications for code-level implementation. Developers can leverage the system's APIs to integrate error detection and correction capabilities into their experimental platforms. This can help to improve the accuracy and reliability of their experiments and reduce the risk of making decisions based on flawed data. Moreover, the system's modular design allows developers to customize it to meet their specific needs. They can add new data collection modules, comparison algorithms, and error correction mechanisms to extend the system's capabilities.","business_analysis":"Systems for Determining Improper Assignments in Statistical Hypothesis Testing addresses a critical need in the realm of data-driven decision-making. The market opportunity for this technology is substantial, as companies and researchers increasingly rely on statistical experiments to inform their strategies. The core problem being solved is the inaccuracy of data due to improper assignments, which can lead to flawed conclusions and wasted resources. The competitive advantages of this patent lie in its ability to identify and correct these errors, providing more reliable data for analysis. This can translate into significant revenue potential for organizations that adopt the technology.\n\nThe business models that can be built around this patent are diverse. One potential model is a software-as-a-service (SaaS) offering, where companies pay a subscription fee to access the system's error detection and correction capabilities. Another model is a consulting service, where experts help organizations implement and optimize the system for their specific needs. The strategic positioning of this technology is strong, as it addresses a fundamental need in the data analysis process. By ensuring data integrity, the patent enables organizations to make more informed decisions and achieve better outcomes. The ROI projections for this technology are compelling. By reducing the risk of making decisions based on flawed data, the patent can help organizations avoid costly mistakes and improve their overall profitability.\n\nFurthermore, the technology can help organizations accelerate their innovation cycles. By providing more reliable data, it enables researchers and developers to iterate more quickly and efficiently. This can lead to faster product development and a greater competitive advantage. The market size for this technology is estimated to be in the billions of dollars, as the need for accurate data analysis continues to grow across various industries. The patent has the potential to disrupt the market by providing a more robust and reliable solution for ensuring data integrity. The business impact of this technology extends beyond mere financial gains. By improving the accuracy of data analysis, it can also help organizations make more ethical and responsible decisions. This can enhance their reputation and build trust with their customers and stakeholders. From a strategic perspective, this patent offers a significant opportunity for companies looking to gain a competitive edge in the data-driven economy.","faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Systems for determining improper assignments in statistical hypothesis testing","description":"Described are techniques for performing statistical hypothesis experiments and determining user responses that do not match an assigned treatment. One of multiple experiment states may be assigned to ","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9854029","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9854029","citation_suggestion":"Patentable. \"Systems for determining improper assignments in statistical hypothesis testing\" (US-9854029). https://patentable.app/patents/US-9854029","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9854029","json":"https://patentable.app/api/llm-context/US-9854029","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T07:43:30.558Z"}