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Enregistrement W4393609385 · doi:10.5281/zenodo.6395308

patccat: A classifier for patent claims

2022· dataset· en· W4393609385 sur OpenAlexaboutno aff
Bernhard Ganglmair, W. Keith Robinson, Michael Seeligson

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Langueen
DomaineBusiness, Management and Accounting
ThématiqueIntellectual Property and Patents
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésClassifier (UML)Artificial intelligenceComputer science

Résumé

récupéré en direct d'OpenAlex

Data version: 3.3.0 Authors:Bernhard Ganglmair (University of Mannheim, Department of Economics, and ZEW Mannheim)W. Keith Robinson (Wake Forest University, School of Law)Michael Seeligson (Southern Methodist University, Cox School of Business) 1. Notes on Data Construction2. Citation and Code3. Description of the Data Files3.1. File List3.2. List of Variables for Files with Claim-Level Information3.3. List of Variables for Files with Patent-Level Information4. Coming Soon! 1. Notes on Data Construction This is version 3.3.0 of the patccat data (patent claim classification by algorithmic text analysis). Patent claims define an invention. A patent application is required to have one or more claims that distinctly claim the subject matter which the patent applicant regards as her invention or discovery. We construct a classifier of patent claims that identifies three distinct claim types: process claims, product claims, and product-by-process claims. For this classification, we combine information obtained from both the preamble and the body of a claim. The preamble is a general description of the invention (e.g., a method, an apparatus, or a device), whereas the body identifies steps and elements (specifying in detail the invention laid out in the preamble) that the applicant is claiming as the invention. The combination of the preamble type and the body type provides us with a more detailed and more accurate classification of claims than other approaches in the literature. This approach also accounts for unconventional drafting approaches. We eventually validate our classification using close to 10,000 manually classified claims. The data files contain the results of our classification. We provide claim-level information for each independent claim of U.S. utility patents granted between 1836 and 2020. We also provide patent-level information, i.e., the counts of different claim types for a given patent. For a detailed description of our classification approach, please take a look at the accompanying paper (Ganglmair, Robinson, and Seeligson 2022). 2. Citation Please cite the following paper when using the data in your own work: Ganglmair, Bernhard, W. Keith Robinson, and Michael Seeligson (2022): "The Rise of Process Claims: Evidence from a Century of U.S. Patents," unpublished manuscript available at https://papers.ssrn.com/abstract=4069994. In the paper, we document the use of process claims in the U.S. over the last century, using the patccat data. We show an increase in the annual share of process claims of about 25 percentage points (from below 10% in 1920). This rise in process intensity of patents is not limited to a few patent classes, but we observe it across a broad spectrum of technologies. Process intensity varies by applicant type: companies file more process-intense patents than individuals, and U.S. applicants file more process-intense patents than foreign applicants. We further show that patents with higher process intensity are more valuable but are not necessarily cited more often. Last, process claims are on average shorter than product claims (with the gap narrowing since the 1970s). We would love to see how other researchers use the data and eventually learn from it. If you have a discussion paper or a publication in which you use the data, please send us a copy at patccat.data@gmail.com. We will the R code used to construct the data on Github with the next data version (version 3.4.0). Contact us at b.ganglmair@gmail.com if you would like to take a look at an earlier version of the code. 3. Description of the Data Files The data files contain claim-level information for independent claims of 10,140,848 U.S. utility patents granted between 1836 and 2020. The files further contain patent-level information for U.S. utility patents. 3.1. File List File list claims-patccat-v3-3-sample.csv claim-level information for independent claims of a sample of 1000 patents issued between 1976 and 2020 claims-patccat-v3-3-1836-1919.csv claim-level information for independent claims of 1,038,041 patents issued between 1836 and 1919 claims-patccat-v3-3-1920-2020.csv claim-level information for independent claims of 9,102,807 patents issued between 1920 and 2020 patents-patccat-v3-3-sample.csv patent-level information for a sample of 1000 patents issued between 1976 and 2020 patents-patccat-v3-3-1836-1919.csv patent-level information for 1,038,041 patents issued between 1836 and 1919 patents-patccat-v3-3-1920-2020.csv patent-level information for 9,102,807 patents issued between 1920 and 2020 3.2. List of Variables for Files with Claim-Level Information For detailed descriptions, see the appendix in Ganglmair, Robinson, and Seeligson (2022). List of Variables (Claim-Level Information) PatentClaim patent claim identifier; 8-digit patent number and 4-digit claim number (Ex: 01234567-0001) singleLine =1 if claim is published in single-line format singleReformat outcome code of reformating of single-line claims Jepson =1 if claim is a Jepson claim JepsonReformat outcome code of reformating of Jepson claims inBegin =1 if claim begins with the word "in" wordsPreamble number of words in the claim preamble wordsBody number of words in the claim body dependentClaims number of dependent claims that refer to this independent claim isMeansPreamble =1 if term "means" is used in the preamble isMeansBody =1 if term "means" is used in the body isMeans =1 if term "means" is used anywhere in the claim (~ means-plus-function claim) processPreamble =1 if terms "method" or "process" are used in the preamble processBody =1 if terms "method" or "process" are used in the body processSimple =1 if terms "method" or "process" are used anywhere in the claim (for simple approach of process claim classification) claimType claim type of full classification (1 = process; 2 = product; 3 = product-by-process; 0 = no type) preambleType preamble type preambleTerm keyword used to classify preamble type preambleTermAlt alternative keyword (if preambleTerm were not used) preambleTextStub first 15 words of the preamble bodyType body type bodyLinesStep number of steps in the body bodyLinesElement number of elements in the body bodyLinesTotal total number of identified lines in the body label 2-character label of the preamble-body combination; classification table maps label to claim type 3.3. List of Variables for Files with Patent-Level Information For detailed descriptions, see the appendix in Ganglmair, Robinson, and Seeligson (2022). List of Variables (Patent-Level Information) patent_id U.S. patent number (8-digit patent number) claims number of independent claims (the sum of the four claim types: 0, 1, 2, and 3) noCategory number of claims without a classified type processClaims number of process claims productClaims number of product claims prodByProcessClaims number of product-by-process claims firstClaim type of the first claim (1 = process; 2 = product; 3 = product-by-process; 0 = no type) simpleProcessClaims number of process claims by simple approach (terms "method" or "process" anywhere in the claim) simpleProcessPreamble number of process claims by simple approach (terms "method" or "process" in the preamble) meansClaims number of means-plus-function claims meansFirst =1 if first claim is a means-plus-function claim JepsonClaims number of Jepson claims JepsonFirst =1 if first claim is a Jepson claim Note: The following variables/fields are currently empty (March 30, 2020); we will populate these variables/fields with data version 3.4.0. preambleTermpreambleTermAltpreambleTextStubbodyLinesStepbodyLinesElementbodyLinesTotal Note: We will release the data for patents issued in 2021 with data version 3.4.0. 4. Coming Soon! We are working on a number of extensions of the patccat data. - With data version 3.4.0, we plan to release data for all published U.S. patent applications (2001 through 2021)- In late spring/early summer 2022, we will release data for patents issued by the European Patent Office (EPO) [Update: March 28, 2023: see https://doi.org/10.5281/zenodo.7776092]- In late spring/early summer 2022, we will release data for patents issued by the Canadian Intellectual Property Office (CIPO)

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Communication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,330
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,000
Communication savante0,0020,000
Science ouverte0,0020,003
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,3520,022

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,224
Tête enseignante GPT0,242
Écart entre enseignants0,018 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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