Reengineering Classification at the USPTO Marti Hearst, Chief IT Strategist, USPTO PIUG Conference May 4, 2010
Questions to Consider What is classification used for? What currently works well? What is currently problematic about the classification system? How can we fix these problems?
How is classification used? As a search aid –Grouping related art together –Narrowing down what needs to be looked at To assign work –Associated with art units –Assigning applications to examiners
What are its strengths? Familiar For some art areas, is nicely detailed.
What are the problems? Time-consuming to update Backwards looking (does not anticipate new directions in technology) The non-patent literature is not classified Is difficult to understand: –For new examiners –For many in the external community Is not harmonized internationally
What are some goals? Note: these are just ideas to discuss. –Make it easier for examiners, managers, or others, to suggest new classes as they arise. –Engage the external community in suggesting up-and-coming classes. –Engage the community in classifying NPL.
What are some goals? Note: these are just ideas to discuss. –Flexible, adaptive to rapidly-developing technology –Easier to browse classified documents dynamically –Easier to classify complex topics that span many fields –Easier to classify across different points of view (structure vs function, for instance) –More aligned with modern classification practices and technology.
Modern Classification Today, most online systems that use classes use faceted classification. –Not just e-commerce, but also digital libraries Bioscience (gopubmed.org, nextbio.com) Computer Science (dblp.l3s.de) Worldcat library catalog U Chicago DL ( Image collections
Lens.lib.uchicagoe.edu
Lens.lib.uchicago.edu
Worldcat.org
worldcat.org
How to apply to the USPC? Speech Signal Processing. Psychoacoustic. For storage or transmission.. Neural Network.. Transformation … Orthogonal functions.. Frequency … Specialized Information …. Pitch ….. Voiced or Unvoiced …. Formant …. Silence decision.. Voice recognition … Preliminary matching …Endpoint detection.. Word recognition … Preliminary matching … Endpoint detection … Specialized models …. Markov ….. Hidden Markov Models (HMM) …… Training of HMM ……. With insufficient amount of training data …… HMM network. Synthesis.. Neural network.. Transformation
How to apply to the USPC? Speech Signal Processing. Psychoacoustic. For storage or transmission.. Neural Network.. Transformation … Orthogonal functions.. Frequency … Specialized Information …. Pitch ….. Voiced or Unvoiced …. Formant …. Silence decision.. Voice recognition … Preliminary matching …Endpoint detection.. Word recognition … Preliminary matching … Endpoint detection … Specialized models …. Markov ….. Hidden Markov Models (HMM) …… Training of HMM ……. With insufficient amount of training data …… HMM network. Synthesis.. Neural network.. Transformation … Specialized models …. Markov ….. Hidden Markov Models (HMM) …… Training of HMM ……. With insufficient amount of training data …… HMM network.. Neural Network.. Transformation … Orthogonal functions
How to Facet the USPC? Speech Signal Properties. Psychoacoustic. Pitch.. Voiced or unvoiced. Formant Speech Signal Problems. Voice recognition. Word recognition. Phrase recognition. Noise reduction. Generation Speech Signal Applications. Text to speech. Speech to text. Meeting recording Machine Learning Methods. HMMs.. HMM Network. Neural Nets.. Linear.. Sigmoidal. Maximum Entropy Machine Learning Techniques issues. Limited training data. Training techniques Data Transformations. Orthogonal functions. Quantization
Potential Advantages More familiar to outsiders More flexible for navigation Easy to include multiple points of view Potentially easier to capture cross-domain similarities (more systematic than x-class) May integrate well with IPC and F-Terms May be easier to automate assignment
Is this a good idea? We’re doing a pilot test on classes in There are of course many issues to consider. Let’s discuss!