Search for: "Rose v. State et al" Results 121 - 140 of 211
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11 Aug 2021, 3:21 pm by Rebecca Tushnet
Prior attempts to measure tarnishment: Buccafusco et al 2016 focused on porn parodies, found burnishing except among very conservative consumers. [read post]
24 Feb 2011, 8:47 am by stevemehta
IN THE COURT OF APPEAL OF THE STATE OF CALIFORNIA   FOURTH APPELLATE DISTRICT   DIVISION THREE     KARENA WHERRY et al.,   Plaintiffs and Respondents,   v. [read post]
28 Aug 2014, 1:11 pm
The Weinstein Company L.L.C. et al, 1:13-cv-05488.Plaintiff is a company producing and distributing films. [read post]
7 Aug 2011, 11:24 pm by Marie Louise
Hitachi et al (EDTexweblog.com) CAFC sets new test for ‘inequitable’ patent prosecution: Therasense v Becton, Dickinson & Co (JIPLP) CAFC validity determination undone by appellant via patent reexamination? [read post]
7 Aug 2011, 11:24 pm by Marie Louise
Hitachi et al (EDTexweblog.com) CAFC sets new test for ‘inequitable’ patent prosecution: Therasense v Becton, Dickinson & Co (JIPLP) CAFC validity determination undone by appellant via patent reexamination? [read post]
29 Feb 2012, 3:34 pm by Robert Thomas (inversecondemnation.com)
This is a veritable 'Hobson's Choice' involving a decision which, as in the case of Jackson, et al. v. [read post]
31 Mar 2010, 3:36 am by John Day
Page Keeton et al., [Prosser and Keeton on the Law of Torts] § 121, at 897 (5th ed. 1984)). [read post]
24 Oct 2022, 5:14 am by INFORRM
Canada The Superior Court of Justice, Ontario handed down judgement in Marcellin v LPS et all 2022 ONSC 5886. [read post]
30 Mar 2020, 5:46 pm
London: Routledge, 2016.Goodin, Robert E., et al. [read post]
5 Jan 2014, 3:30 pm by Barry Sookman
One of the most important, if not the most important, United States copyright cases decided in 2013 is The Authors Guild, Inc. v Google Inc. 2013 WL 6017130 (S.D.N.Y. [read post]
28 Dec 2023, 6:49 pm by Chuck Cosson
  OpenAI published a paper in 2020, for example, outlining a scaling analysis for AI models, finding that “language modeling performance improves smoothly and predictably as we appropriately scale up model size, data, and compute”; see Kaplan, McCandlish, et. al, “Scaling Laws for Neural Language Models,” online at:  2001.08361.pdf (arxiv.org). [read post]