USPTO Quality Measurements

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Transcript of USPTO Quality Measurements

  • National Association of Patent

    Practitioners 20th Annual

    Meeting and Conference

    Martin Rater Office of Patent Quality Assurance, USPTO

    July 28, 2016

    1

  • USPTO Quality Measurements

    Challenges, Processes, and Techniques

  • Challenges

  • Defining Quality

    Biggest challenge has been generating metrics that meet a wide range of quality definitions

    Examiners view vs. Applicants view

    Big Q vs. little q

    Timeliness and value

    Reality vs. expectations

    Everything in between I know it when I see it

    Akin to defining the color medium blue

  • Patent Examination Quality

    Primary focus has been on examination quality Examiners adherence to laws, rules, and procedures

    Tracked against some established standards for desired outcomes Correctness statutory compliance

    Clarity

    Consistency

    Reopening

    Rework

    Impacts on advancing prosecution

    Basis for historic compliance metrics reported by USPTO

  • Additional Challenges

    Objectivity vs. Subjectivity

    Leading vs. Lagging indicators What we are doing rather than what we did

    Controlling for a wide range of factors e.g. Technology; Examiner experience; applicant behavior; pilot programs

    and initiatives

    Establishing causal effects

    Balloon-effect of pushing quality problems elsewhere

    Verification and validation of quality metrics

    There is no silver bullet

    Uniqueness of what we do

  • Processes and

    Techniques

  • Office of Patent Quality Assurance (OPQA)

    56 Review Quality Assurance Specialists (RQAS) Average of 20 years of patent examination experience

    Demonstrated skills in production, quality, and training

    Assignments based on technology

    Major activities

    Review of examiner work product

    Coaching and mentoring

    Practice and procedure training

    Program evaluations, case studies, ad-hoc analyses

    Operate under an established ISO 9001-certified Quality

    Management System

  • Data Sources Patent Application and Monitoring System (PALM)

    Objective transactional data

    Review-based questionnaires/forms OPQA

    Patent Operations

    Recent transition to Master Review Form

    Perception surveys External stakeholders

    Patent examiners and managers

    Administrative data

    Big Data Office Actions

  • Study Types

    Descriptive Describe and explain conditions

    Time series / Longitudinal Emphasis on growth or change

    Correlational Explore relationships

    Causal Cause and effect

    Current case studies initiative encompasses multiple study types

  • Study Designs

    Quasi-experimental

    Lack random assignment

    e.g. program eligibility requirements

    Must control or account for characteristics such as

    Examination experience

    Technology

    Training/background

  • Sampling

    Random sampling Larger sample sizes needed for estimating proportions

    Primary factors in sample size determination How data will be used

    Resources necessary for data collection

    Maintain representativeness

    OPQA-generated metrics based on random sampling: Compliance rate reviews

    Employee perceptions

    Training effectiveness

  • Sampling

    Stratified sampling Superior to simple random sampling

    Requires weighting of data

    OPQA-generated metrics based on random sampling: External Quality Survey

    Reduce sampling error and limit respondent burden

    Non-Probability Methods Used sparingly to get general estimate of the results

    Convenience sampling

    Judgement sampling

  • Analysis of Quality Data

    Trend Analysis Longitudinal data sets such as compliance rates, Quality Index Reporting

    (QIR) database, quality surveys

    Statistically-significant differences Significance is not always significant!

    Rolling 12-month statistics

    Pre-Post Comparisons Evaluations of pilot programs

    Impacts of training initiatives and rules/policy changes

    Key points: Establish a baseline

    Find a control group

  • Analysis of Quality Data

    Outlier analysis Anomaly detection

    Primarily has been used for monitoring examination behaviors vis QIR database Objective, comprehensive data set of transactions

    Big Data greatly enhances ability to detect anomalies Not biased by what we think are the items of interest

    Custer and Factor Analysis Cluster: classify

    Factor: reduce

    Correlation and Regression Analysis Relationships

    Cause and effect

  • Reporting

    Frequency Quarterly reporting most common for longitudinal data series

    Allow time for implementation of corrective and/or preventive actions

    Performance award plan inputs

    Level/Details Maintain statistical validity

    Report only what the sample will support Corps, TC, Art Unit, Examiner

    Combine reporting periods to enhance level of detail if sample size not sufficient

    Providing data sets for external analyses of data Data hub

    Master Review Form

  • Moving Forward

  • Next steps in USPTO Quality Metrics

    Discontinue Quality Composite

    More granularity

    Statute

    Action Type

    Technology

    Consistency, Reopening, Rework

    External perceptions for validation

    Transparency and sharing of data for external analyses

  • Questions?