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Record W4319337623 · doi:10.31584/jhsmr.2023926

A Review on the Indian Patent System and Its Implication on the Pharmaceutical Industry

2023· review· en· W4319337623 on OpenAlexaff
Deepak Kumar Dash, Riya Vaiswade, Gayatri Gupta

Bibliographic record

VenueJournal of Health Science and Medical Research · 2023
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsRoyal College of Physicians and Surgeons of Canada
Fundersnot available
KeywordsIntellectual propertyPatentabilityPharmaceutical industryContext (archaeology)TRIPS AgreementBusinessTRIPS architectureAuthorizationLaw and economicsPatent lawInternational tradeIndustrial organizationCommerceLawEconomicsBiotechnologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

A patent is the main design of Intellectual Property Rights employed in the pharmaceutical industry. Claims of patents in India are imposed under the Patents Act of 1970. The goal of patent authorization is to inspire and progress in the industry and associated modern technologies. Intellectual property rights can help grow the economy due to their industrial applicability in regard to businesses within the country as well as exports. The Indian pharmaceutical industry, is a distinctly uneven one, is influenced by others and there were difficulties in regards to intellectual property rights in the context of the world trade organization.This review illustrates a brief outline of patent law in India due to the significance of Trade-Related Aspects of Intellectual Property Rights (TRIPS) contracts and the benefits of patentability as well as different types of pharmaceutical patents are described accordingly. Other appropriate necessities linked with patenting of pharmaceuticals like, pre and post-trade related aspects of Intellectual Property Rights, compulsory licensing etc. are also explained. The objective of this paper is to study the patent act of the pharmaceutical industry and several patents granted in India in the pharmaceutical industry, aiming to provide information in the context of pharmaceutical patenting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.054
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.914
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0540.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.802
GPT teacher head0.541
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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