DIRITTO D’AUTORE – Lorenzo Albertini

L’emittente radiotelevisica ha diritto al compenso per le copie private (art. 5.2.b) dir. 29-2001)

Corte di giustizia 23.,11.2023, Seven.One entertainment v. Corint, C-260/22 illumina (poco, per vero) la norma in oggetto, in relazione alla sua trasposizione nazionale tedesca, che esenta dal diritto al compenso tramite collecting society le emittenti per le loro trasmissioni.

La soluzione della corte è scontata, visto il dettato letterale della dir. 298/2001. Palesement irrilevante, poi, è la circostranza per cui certi organismi TV siano anche produttori di pellicole maturando così il relativo credito, giustamente osserva la CG.

Meno semplice è la sua attiazione e in particolare se lo Stao possa esentare certi aventi diritto sulla base di danno inesistente o minimo: o meglio, visto che può, quando ricorra tale fattiuspecie concreta. Serve poi parità di trattamento nel senso che è da vedere se può esentare tutti o solo caso per caso (ed allora in base a quali criteri oggettivi).

Ma su tutto ciò solo il giudice nazinale può decidere, conclude la CG

L’intelligenza artificiale di Facebook viola il diritto di elaborazione delle opere letterarie utilizzate?

Large Language Model Meta AI (LLaMA) (v.ne la descrizione nel sito di Meta) non viola il diritto di elaborazione sulle opere letterarie usate per creare tali modelli, dice il Trib. del distretto nord della Calofiornia Case No. 23-cv-03417-VC, 20 novembre 2023 , Kadrey v. Meta.

Nè nella costituzione dei modelli medesimi nè nell’output genrato dal loro uso:

<<1. The plaintiffs allege that the “LLaMA language models are themselves infringing
derivative works” because the “models cannot function without the expressive information
extracted” from the plaintiffs’ books. This is nonsensical. A derivative work is “a work based
upon one or more preexisting works” in any “form in which a work may be recast, transformed,
or adapted.” 17 U.S.C. § 101. There is no way to understand the LLaMA models themselves as a
recasting or adaptation of any of the plaintiffs’ books.

[più che altro non c’è prova: non si può dire che sia impossibile in astratto]
2. Another theory is that “every output of the LLaMA language models is an infringing
derivative work,” and that because third-party users initiate queries of LLaMA, “every output
from the LLaMA language models constitutes an act of vicarious copyright infringement.” But
the complaint offers no allegation of the contents of any output, let alone of one that could be understood as recasting, transforming, or adapting the plaintiffs’ books. Without any plausible
allegation of an infringing output, there can be no vicarious infringement. See Perfect 10, Inc. v.
Amazon.com, Inc., 508 F.3d 1146, 1169 (9th Cir. 2007).
The plaintiffs are wrong to say that, because their books were duplicated in full as part of
the LLaMA training process, they do not need to allege any similarity between LLaMA outputs
and their books to maintain a claim based on derivative infringement. To prevail on a theory that
LLaMA’s outputs constitute derivative infringement, the plaintiffs would indeed need to allege
and ultimately prove that the outputs “incorporate in some form a portion of” the plaintiffs’
books. Litchfield v. Spielberg, 736 F.2d 1352, 1357 (9th Cir. 1984); see also Andersen v.
Stability AI Ltd., No. 23-CV-00201-WHO, 2023 WL 7132064, at *7-8 (N.D. Cal. Oct. 30, 2023)
(“[T]he alleged infringer’s derivative work must still bear some similarity to the original work or
contain the protected elements of the original work.”); 2 Melville B. Nimmer & David Nimmer,
Nimmer on Copyright § 8.09 (Matthew Bender Rev. Ed. 2023) (“Unless enough of the pre-
existing work is contained in the later work to constitute the latter an infringement of the former,
the latter, by definition, is not a derivative work.”); 1 Melville B. Nimmer & David Nimmer,
Nimmer on Copyright § 3.01 (Matthew Bender Rev. Ed. 2023) (“A work is not derivative unless
it has substantially copied from a prior work.” (emphasis omitted)). The plaintiffs cite Range
Road Music, Inc. v. East Coast Foods, Inc., 668 F.3d 1148 (9th Cir. 2012), but that case is not
applicable here. In Range Road, the infringement was the public performance of copyrighted
songs at a bar. Id. at 1151-52. The plaintiffs presented evidence (namely, the testimony of
someone they sent to the bar) that the songs performed were, in fact, the protected songs. Id. at
1151-53. The defendants presented no evidence of their own that the protected songs were not
performed. Nor did they present evidence that the performed songs were different in any
meaningful way from the protected songs. Id. at 1154. The Ninth Circuit held that, under these
circumstances, summary judgment for the plaintiffs was appropriate. And the Court rejected the
defendants’ contention that the plaintiffs, under these circumstances, were also required to
present evidence that the performed songs were “substantially similar” to the protected songs.
That contention made no sense, because the plaintiffs had already offered unrebutted evidence
that the songs performed at the bar were the protected songs. Id. at 1154. Of course, if the
defendants had presented evidence at summary judgment that the songs performed at the bar
were meaningfully different from the protected songs, then there would have been a dispute over
whether the performances were infringing, and the case would have needed to go to trial. At that
trial, the plaintiffs would have needed to prove that the performed songs (or portions of the
performed songs) were “substantially similar” to the protected songs. That’s the same thing the
plaintiffs would need to do here with respect to the content of LLaMA’s outputs. To the extent
that they are not contending LLaMa spits out actual copies of their protected works, they would
need to prove that the outputs (or portions of the outputs) are similar enough to the plaintiffs’
books to be infringing derivative works. And because the plaintiffs would ultimately need to
prove this, they must adequately allege it at the pleading stage>>

[anche qui manca la prova]

Motivazione un pò striminzita, per vero.

(notizia e link dal blog di Eric Goldman)

Coreografia v. “emotes” nel diritto di autore: l’appello in Hanagami v. Epic Games

Avevo notiziato il 05.09.2022 su Central District della California 24 agosto 2022, caso 2: 22-cv-02063-SVW-MRW che aveva rigettato la domanda di tutela verso l’uso nel gioco Fortnite.

Ora l’appello (in 14 mesi !!!) che riforma la sentenza di primo grado.

La sentenza è di una certa importanza per capire la disciplina della tutela autorale dell’opera coreografica.

Dal summary iniziale:

<<Games, Inc., the creator of the videogame Fortnite, infringed
the copyright of a choreographic work when the company
created and sold a virtual animation, known as an “emote,”
depicting portions of the registered choreography.
The panel held that, under the “extrinsic test” for
assessing substantial similarity, Hanagami plausibly alleged
that his choreography and Epic’s emote shared substantial
similarities. The panel held that, like other forms of
copyrightable material such as music, choreography is
composed of various elements that are unprotectable when
viewed in isolation. What is protectable is the
choreographer’s selection and arrangement of the work’s
otherwise unprotectable elements. The panel held that
“poses” are not the only relevant element, and a
choreographic work also may include body position, body
shape, body actions, transitions, use of space, timing, pauses,
energy, canon, motif, contrast, and repetition. The panel
concluded that Hanagami plausibly alleged that the creative
choices he made in selecting and arranging elements of the
choreography—the movement of the limbs, movement of the hands and fingers, head and shoulder movement, and tempo—were substantially similar to the choices Epic made in creating the emote.
The panel held that the district court also erred in dismissing Hanagami’s claim on the ground that the allegedly copied choreography was “short” and a “small component” of Hanagami’s overall work. The panel declined to address the issue whether the work was entitled to broad or only thin copyright protection>>.

(notizia e link da Eric Goldman)

Ancora su AI, data scraping e violazione di copyright (questa volta per lo più negata)

La corte del distr. Nord della California 30 ottobre 2023, Case 3:23-cv-00201-WHO, Andersen v. Stability AI, DeviantArt, Midjourney, esamina il tema in oggetto (segnalazione e link di Jess Miers su X).

Le domande sono tutte rigettate tranne quelal verso Stability, per la quale è cocnessa facoltà di modifica:

<<3. Direct Infringement Allegations Against Stability Plaintiffs’ primary theory of direct copyright infringement is based on Stability’s creation and use of “Training Images” scraped from the internet into the LAION datasets and then used to train Stable Diffusion. Plaintiffs have adequately alleged direct infringement based on the allegations that Stability “downloaded or otherwise acquired copies of billions of copyrighted images without permission to create Stable Diffusion,” and used those images (called “Training Images”) to train Stable Diffusion and caused those “images to be stored at and incorporated into Stable Diffusion as compressed copies.” Compl. ¶¶ 3-4, 25-26, 57. In its “Preliminary Statement” in support of its motion to dismiss, Stability opposes the truth of plaintiffs’ assertions. See Stability Motion to Dismiss (Dkt. No. 58) at 1. However, even Stability recognizes that determination of the truth of these allegations – whether copying in violation of the Copyright Act occurred in the context of training Stable Diffusion or occurs when Stable Diffusion is run – cannot be resolved at this juncture. Id. Stability does not otherwise oppose the sufficiency of the allegations supporting Anderson’s direct copyright infringement claims with respect to the Training Images>>.

Provvedimento itneressante poer chi si occupa del tema, dato che da noi ancora non se ne son visti.

Copyright e standards

La corte di appello del distretto di Columbia , 12.09.2023, No. 22-7063, AMERICAN SOCIETY FOR TESTING AND MATERIALS, ET AL v. PUBLIC.RESOURCE.ORG, INC., dà qualche interessante insegnamento sul tema (qui la pagina della corte mentre qui il link diretto al pdf).

Tre organizzazioni, che predispongno standard per certi settori di impresa, fanno causa a public.resource.org, per aver pubblicato centinaia di standards: il che violerebbe il copyright su di essi gravante.

Di questi la maggior parte era anche stata inserita (incorporate) nella legislazione usa.

La corte di appello dice che tale pubblicaizone da parte di https://public.resource.org/ costituisce fair use (per la parte incorporated).

I primi tre fattori del 17 us code § 107 sono a favore del convenuto.

L’ultimo (effetti economici sul mercato dell’opera protetta) è invece incerto: ma non basta a controbilanciare gli altri tre.

<<n ASTM II, we noted that Public Resource’s copying may harm the market for the plaintiffs’ standards, but we found the extent of any such harm to be unclear. 896 F.3d at 453. We noted three considerations that might reduce the amount of harm: First, the plaintiffs themselves make the incorporated standards available for free in their reading rooms. Second, Public Resource may not copy unincorporated standards—or unincorporated portions of standards only partially incorporated. Third, the plaintiffs have developed and copyrighted updated versions of the relevant standards, and these updated versions have not yet been incorporated into law. We asked the parties to address these issues, among others, on remand. See id.
The updated record remains equivocal. The plaintiffs press heavily on what seems to be a common-sense inference: If users can download an identical copy of an incorporated standard for free, few will pay to buy the standard. Despite its intuitive appeal, this argument overlooks the fact that the plaintiffs regularly update their standards—including all 185 standards at issue in this appeal. And regulators apparently are much less nimble in updating the incorporations. So, many of the builders, engineers, and other regular consumers of the plaintiffs’ standards may simply purchase up-to-date versions as a matter of course. Moreover, some evidence casts doubt on the plaintiffs’ claims of significant market injury. Public Resource has been posting incorporated standards for fifteen years. Yet the plaintiffs have been unable to produce any economic analysis showing that Public Resource’s activity has harmed any relevant market for their standards. To the contrary, ASTM’s sales have increased over that time; NFPA’s sales have decreased in recent years but are cyclical with publications; and ASHRAE has not pointed to any evidence of its harm. See ASTM III, 597 F. Supp. 3d at 240.
The plaintiffs’ primary evidence of harm is an expert report opining that Public Resource’s activities could put the plaintiffs’ revenues at risk. Yet although the report qualitatively describes harms the plaintiffs could suffer, it makes no serious attempt to quantify past or future harms. Like the district court, we find it “telling” that the plaintiffs “do not provide any quantifiable evidence, and instead rely on conclusory assertions and speculation long after [Public Resource] first began posting the standards.” ASTM III, 597 F. Supp. 3d at 240.
Finally, our analysis of market effects must balance any monetary losses to the copyright holders against any “public benefits” of the copying. Oracle, 141 S. Ct. at 1206. Thus, even if Public Resource’s postings were likely to lower demand for the plaintiffs’ standards, we would also have to consider the substantial public benefits of free and easy access to the law. As the Supreme Court recently confirmed: “Every citizen is presumed to know the law, and it needs no argument to show that all should have free access” to it. Georgia v. Public.Resource.Org., Inc., 140 S. Ct. 1498, 1507 (2020) (cleaned up)>>.

Sintesi sul quarto:

<<We conclude that the fourth fair-use factor does not significantly tip the balance one way or the other. Common sense suggests that free online access to many of the plaintiffs’ standards would tamp down the demand for their works. But there are reasons to doubt this claim, the record evidence does not strongly support it, and the countervailing public benefits are substantial.>>

Sintesi comlpèessiva: <<In sum, the first three factors under section 107 strongly favor fair use, and the fourth is equivocal. We thus conclude that Public Resource’s non-commercial posting of incorporated standards is fair use>>

Altra azione contro società di A. I., basata su diritto di autore: Concord Music, Universal Music e altri c. Anthropic PBC

Tramite il modello AI chiamato Claude2, Anthropic violerebbe il copyright di molte canzoni (della loro parte letterariA) . Così la citazione in giudizio da parte di molti produttori (tra i maggiori al mondo, parrebbe).

Ne dà notizia The Verge oggi 19 ottobre (articolo di Emilia David), ove trovi pure il link all’atto introduttivo di citazione in giudizio.

Riposto solo i passi sul come fuinziona il traininig e l’output di Claude2 e poi dove stia la vioalzione.

<<6 . Anthropic is in the business of developing, operating, selling, and licensing AI technologies. Its primary product is a series of AI models referred to as “Claude.” Anthropic builds its AI models by scraping and ingesting massive amounts of text from the internet and potentially other sources, and then using that vast corpus to train its AI models and generate output based on this copied text. Included in the text that Anthropic copies to fuel its AI models are the lyrics to innumerable musical compositions for which Publishers own or control the copyrights, among countless other copyrighted works harvested from the internet. This copyrighted material is not free for the taking simply because it
can be found on the internet. Anthropic has neither sought nor secured Publishers’ permission to use their valuable copyrighted works in this way. Just as Anthropic does not want its code taken without its authorization, neither do music publishers or any other copyright owners want their works to be exploited without permission.
7.
Anthropic claims to be different from other AI businesses. It calls itself an AI “safety and research” company, and it claims that, by training its AI models using a so-called “constitution,” it ensures that those programs are more “helpful, honest, and harmless.” Yet, despite its purportedly principled approach, Anthropic infringes on copyrights without regard for the law or respect for the creative community whose contributions are the backbone of Anthropic’s infringing service.
8.
As a result of Anthropic’s mass copying and ingestion of Publishers’ song lyrics, Anthropic’s AI models generate identical or nearly identical copies of those lyrics, in clear violation of Publishers’ copyrights. When a user prompts Anthropic’s Claude AI chatbot to provide the lyrics to songs such as “A Change Is Gonna Come,” “God Only Knows,” “What a Wonderful World,” “Gimme Shelter,” “American Pie,” “Sweet Home Alabama,” “Every Breath You Take,” “Life Is a Highway,” “Somewhere Only We Know,” “Halo,” “Moves Like Jagger,” “Uptown Funk,” or any other number of Publishers’ musical compositions, the chatbot will provide responses that contain all or significant portions of those lyrics>>.

<<11. By copying and exploiting Publishers’ lyrics in this manner—both as the input it uses to train its AI models and as the output those AI models generate—Anthropic directly infringes Publishers’ exclusive rights as copyright holders, including the rights of reproduction, preparation of derivative works, distribution, and public display. In addition, because Anthropic unlawfully enables, encourages, and profits from massive copyright infringement by its users, it is secondarily liable for the infringing acts of its users under well-established theories of contributory infringement and vicarious infringement. Moreover, Anthropic’s AI output often omits critical copyright management information regarding these works, in further violation of Publishers’ rights; in this respect, the composers of the song lyrics frequently do not get recognition for being the creators of the works that are being distributed. It is unfathomable for Anthropic to treat itself as exempt from the ethical and legal rules it purports to embrace>>

Come funziona il training di AI:

<<54. Specifically, Anthropic “trains” its Claude AI models how to generate text by taking the following steps:
a. First, Anthropic copies massive amounts of text from the internet and potentially other sources. Anthropic collects this material by “scraping” (or copying or downloading) the text directly from websites and other digital sources and onto Anthropic’s servers, using automated tools, such as bots and web crawlers, and/or by working from collections prepared by third parties, which in turn may have been harvested through web scraping. This vast collection of text forms the input, or “corpus,” upon which the Claude AI model is then trained.
b.   Second, as it deems fit, Anthropic “cleans” the copied text to remove material it perceives as inconsistent with its business model, whether technical or subjective in nature (such as deduplication or removal of offensive language), or for other reasons.
In most instances, this “cleaning” process appears to entirely ignore copyright infringements embodied in the copied text.
c.   Third, Anthropic copies this massive corpus of previously copied text into computer memory and processes this data in multiple ways to train the Claude AI models, or establish the values of billions of parameters that form the model. That includes copying, dividing, and converting the collected text into units known as “tokens,” which are words or parts of words and punctuation, for storage. This process is referred to as “encoding” the text into tokens. For Claude, the average token is about 3.5 characters long.4
d.   Fourth, Anthropic processes the data further as it “finetunes” the Claude AI model and engages in additional “reinforcement learning,” based both on human feedback and AI feedback, all of which may require additional copying of the collected text.
55.   Once this input and training process is complete, Anthropic’s Claude AI models generate output consistent in structure and style with both the text in their training corpora and the reinforcement feedback. When given a prompt, Claude will formulate a response based on its model, which is a product of its pretraining on a large corpus of text and finetuning, including based on reinforcement learning from human feedback. According to Anthropic, “Claude is not a bare language model; it has already been fine-tuned to be a helpful assistant.”5 Claude works with text in the form of tokens during this processing, but the output is ordinary readable text>>.

Violazioni:

<<56.
First, Anthropic engages in the wholesale copying of Publishers’ copyrighted lyrics as part of the initial data ingestion process to formulate the training data used to program its AI models.
57.
Anthropic fuels its AI models with enormous collections of text harvested from the internet. But just because something may be available on the internet does not mean it is free for Anthropic to exploit to its own ends.
58.
For instance, the text corpus upon which Anthropic trained its Claude AI models and upon which these models rely to generate text includes vast amounts of Publishers’ copyrighted lyrics, for which they own or control the exclusive rights.
59.
Anthropic largely conceals the specific sources of the text it uses to train its AI models. Anthropic has stated only that “Claude models are trained on a proprietary mix of publicly available information from the Internet, datasets that we license from third party businesses, and data that our users affirmatively share or that crowd workers provide,” and that the text on which Claude 2 was trained continues through early 2023 and is 90 percent English-language.6 The reason that Anthropic refuses to disclose the materials it has used for training Claude is because it is aware that it is copying copyrighted materials without authorization from the copyright owners.
60.
Anthropic’s limited disclosures make clear that it has relied heavily on datasets (e.g., the “Common Crawl” dataset) that include massive amounts of content from popular lyrics websites such as genius.com, lyrics.com, and azlyrics.com, among other standard large text
collections, to train its AI models.7
61.
Moreover, the fact that Anthropic’s AI models respond to user prompts by generating identical or near-identical copies of Publishers’ copyrighted lyrics makes clear that Anthropic fed the models copies of those lyrics when developing the programs. Anthropic had to first copy these lyrics and process them through its AI models during training, in order for the models to subsequently disseminate copies of the lyrics as output.
62.
Second, Anthropic creates additional unauthorized reproductions of Publishers’ copyrighted lyrics when it cleans, processes, trains with, and/or finetunes the data ingested into its AI models, including when it tokenizes the data. Notably, although Anthropic “cleans” the text it ingests to remove offensive language and filter out other materials that it wishes to exclude from its training corpus, Anthropic has not indicated that it takes any steps to remove copyrighted content.
63.
By copying Publishers’ lyrics without authorization during this ingestion and training process, Anthropic violates Publishers’ copyrights in those works.
64.
Third, Anthropic’s AI models disseminate identical or near-identical copies of a wide range of Publishers’ copyrighted lyrics, in further violation of Publishers’ rights.
65.
Upon accessing Anthropic’s Claude AI models through Anthropic’s commercially available API or via its public website, users can request and obtain through Claude verbatim or near-verbatim copies of lyrics for a wide variety of songs, including copyrighted lyrics owned and controlled by
Publishers. These copies of lyrics are not only substantially but strikingly similar to the original copyrighted works>>

<<70.
Claude’s output is likewise identical or substantially and strikingly similar to Publishers’ copyrighted lyrics for each of the compositions listed in Exhibit A. These works that have been infringed by Anthropic include timeless classics as well as today’s chart-topping hits, spanning a range of musical genres. And this represents just a small fraction of Anthropic’s infringement of Publishers’ works and the works of others, through both the input and output of its AI models.
71.
Anthropic’s Claude is also capable of generating lyrics for new songs that incorporate the lyrics from existing copyrighted songs. In these cases, Claude’s output may include portions of one copyrighted work, alongside portions of other copyrighted works, in a manner that is entirely inconsistent and even inimical to how the songwriter intended them.
72.
Moreover, Anthropic’s Claude also copies and distributes Publishers’ copyrighted lyrics even in instances when it is not asked to do so. Indeed, when Claude is prompted to write a song about a given topic—without any reference to a specific song title, artist, or songwriter—Claude will often respond by generating lyrics that it claims it wrote that, in fact, copy directly from portions of Publishers’ copyrighted lyrics>>.

<<80.
In other words, Anthropic infringes Publishers’ copyrighted lyrics not only in response to specific requests for those lyrics. Rather, once Anthropic copies Publishers’ lyrics as input to train its AI models, those AI models then copy and distribute Publishers’ lyrics as output in response to a wide range of more generic queries related to songs and various other subject matter>>.

La citazione in giudizio dell’associazione scrittori usa contro Open AI

E’ reperibile in rete (ad es qui) la citazione in giuidizio avanti il South. Dist. di New Yoerk contro Open AI per vioalzione di copyright proposta dalla importante Autorhs Guild e altri (tra cui scrittori notissimi) .

L’allenamento della sua AI infatti pare determini riproduzione e quindi (in assenza di eccezione/controdiritto) violazione.

Nel diritto UE l’art. 4 della dir 790/2019 presuppone il diritto di accesso all’opera per invocare l’eccezione commerciale di text and data mining:

<< 1. Gli Stati membri dispongono un’eccezione o una limitazione ai diritti di cui all’articolo 5, lettera a), e all’articolo 7, paragrafo 1, della direttiva 96/9/CE, all’articolo 2 della direttiva 2001/29/CE, all’articolo 4, paragrafo 1, lettere a) e b), della direttiva 2009/24/CE e all’articolo 15, paragrafo 1, della presente direttiva per le riproduzioni e le estrazioni effettuate da opere o altri materiali cui si abbia legalmente accesso ai fini dell’estrazione di testo e di dati.

2. Le riproduzioni e le estrazioni effettuate a norma del paragrafo 1 possono essere conservate per il tempo necessario ai fini dell’estrazione di testo e di dati.

3. L’eccezione o la limitazione di cui al paragrafo 1 si applica a condizione che l’utilizzo delle opere e di altri materiali di cui a tale paragrafo non sia stato espressamente riservato dai titolari dei diritti in modo appropriato, ad esempio attraverso strumenti che consentano lettura automatizzata in caso di contenuti resi pubblicamente disponibili online.

4. Il presente articolo non pregiudica l’applicazione dell’articolo 3 della presente direttiva>>.

Il passaggio centrale (sul se ricorra vioalzione nel diritto usa) nella predetta citazione sta nei §§ 51-64:

<<51. The terms “artificial intelligence” or “AI” refer generally to computer systems designed to imitate human cognitive functions.
52. The terms “generative artificial intelligence” or “generative AI” refer specifically to systems that are capable of generating “new” content in response to user inputs called “prompts.”
53. For example, the user of a generative AI system capable of generating images
from text prompts might input the prompt, “A lawyer working at her desk.” The system would then attempt to construct the prompted image. Similarly, the user of a generative AI system capable of generating text from text prompts might input the prompt, “Tell me a story about a lawyer working at her desk.” The system would then attempt to generate the prompted text.
54. Recent generative AI systems designed to recognize input text and generate
output text are built on “large language models” or “LLMs.”
55. LLMs use predictive algorithms that are designed to detect statistical patterns in the text datasets on which they are “trained” and, on the basis of these patterns, generate responses to user prompts. “Training” an LLM refers to the process by which the parameters that define an LLM’s behavior are adjusted through the LLM’s ingestion and analysis of large
“training” datasets.
56. Once “trained,” the LLM analyzes the relationships among words in an input
prompt and generates a response that is an approximation of similar relationships among words in the LLM’s “training” data. In this way, LLMs can be capable of generating sentences, p aragraphs, and even complete texts, from cover letters to novels.
57. “Training” an LLM requires supplying the LLM with large amounts of text for
the LLM to ingest—the more text, the better. That is, in part, the large in large language model.
58. As the U.S. Patent and Trademark Office has observed, LLM “training” “almost
by definition involve[s] the reproduction of entire works or substantial portions thereof.”4
59. “Training” in this context is therefore a technical-sounding euphemism for
“copying and ingesting.”
60. The quality of the LLM (that is, its capacity to generate human-seeming responses
to prompts) is dependent on the quality of the datasets used to “train” the LLM.
61. Professionally authored, edited, and published books—such as those authored by Plaintiffs here—are an especially important source of LLM “training” data.
62. As one group of AI researchers (not affiliated with Defendants) has observed,
“[b]ooks are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.”5
63. In other words, books are the high-quality materials Defendants want, need, and have therefore outright pilfered to develop generative AI products that produce high-quality results: text that appears to have been written by a human writer.
64. This use is highly commercial>>

Plagio di lettera da parte di un breve saggio: “The Kindest” in Larson v. Dorland Perry

La corte del Massachussets 14.09.2023 n. Case 1:19-cv-10203-IT, larson v. Dorland Perry, (segnalato e linkato dal prof. Edward Lee su X ).

Qui la peculiarità fattuale è che il lavoro plagiario si è evoluto in tre versioni, sempre più lontane dal lavoro originale.

Sulla substantial similarity : <<“Substantial similarity is an elusive concept, not subject to precise definition.” Concrete Mach. Co. v. Classic Lawn Ornaments, Inc., 843 F.2d 600, 606 (1st Cir. 1988). The inquiry is a “sliding scale”: If there are many ways to express a particular idea, then the burden of proof on the plaintiff to show substantial similarity is lighter. Id. at 606-07. Here, there are many ways to write a letter, even one dealing specifically with kidney donations. Larson Mem. SJ, Ex. 8 [Doc. No. 189-8] (examples of sample letters from organ donors/family members of organ donors to recipients); Id., Ex. 1 ¶ 7 (Larson Aff.) [Doc. No. 189-1]>>.

Sulle parti non originali:

<<However, “[n]o infringement claim lies if the similarity between two works rests necessarily on non-copyrightable aspects of the original—for example, ‘the underlying ideas, or expressions that are not original with the plaintiff.’” TMTV, Corp. v. Mass. Prods., Inc., 645 F.3d 464, 470 (1st Cir. 2011) (internal citation omitted). “[I]t is only when ‘the points of dissimilarity not only exceed the points of similarity, but indicate that the remaining points of similarity are (within the context of plaintiff’s work) of minimal importance either quantitatively or qualitatively, [that] no infringement results.’” Segrets, Inc., 207 F.3d at 66. “‘The test is whether the accused work is so similar to the plaintiff’s work that an ordinary reasonable person would conclude that the defendant unlawfully appropriated the plaintiff’s protectible expression by taking material of substance and value.’” Id. at 62. “While summary judgment for a plaintiff on these issues is unusual,” it may be warranted based on the factual record. Id.; accord T-Peg, Inc. v. Vt. Timber Works, Inc., 459 F.3d 97, 112 (1st Cir. 2006)>>.

Sui dati fattuali sostenenti il giudizio di accertato plagio nella prima versione:

<<The 2016 Brilliance Audio Letter.8 As Larson concedes, the undisputed evidence mandates a conclusion that the 2016 Letter is substantially similar to the Dorland Letter. The Dorland Letter is approximately 381 words long, Dorland Mem. SJ, Ex. C [Doc. No. 181-3]; of those 381 words, the 2016 Letter copies verbatim approximately 100, and closely paraphrases approximately 50 more, Larson Mem. SJ, Appendix I [Doc. No. 193-1]. Many of these verbatim or near-verbatim lines gave the Dorland Letter its particular character, including: “My gift…trails no strings”; “I [focused/channeled] [a majority of] my [mental] energ[y/ies] into imagining and celebrating you”; “I accept any level of involvement,…even if it is none”; “To me the suffering of strangers is just as real”; and “I [wasn’t given/didn’t have] the opportunity to form secure attachments with my family of origin.” Id. The 2016 Letter also follows an identical structure to the Dorland Letter: a paragraph introducing the donor, including information on race, age, and gender; a paragraph explaining how the donor discovered the need for kidney donation; a paragraph explaining the donor’s traumatic childhood; a paragraph expressing the donor’s focus on the future recipient; a paragraph wishing the recipient health and happiness; and a concluding paragraph expressing a desire to meet. Id. Based on the documents before the court, the 2016 Letter took “material of substance and value” from the Dorland Letter in such a quantity and in such a manner that the points of similarity outweigh the points of dissimilarity. See Segrets, Inc., 207 F.3d at 62, 66.>>

Con analitico esame ravvisa comunque fair use.

Il giudice esclude tortiuous interference nelle continue dichiaraizoni dell’asserito plagiato verso le contriopati contrattiuali dell’asserito plagiante

Esclude anche che ricorra diffamazione.

Arriva la sentenza di appello in Sony Music v. eredi Lucio Battisti in tema di diritti del produttore fonografico

App. Milano n. 2597/2023 del 5 settembre 2023, Rg 3103/2021, rel. Orsenigo, conferma il rigetto delle domande di Sony pur correggendo (giustamente) la motivaizone.

Sony , produttore fonografico ex art. 72 l. aut., lamentava l’illegititmo rifuto di autorizzare ( o direttamente o tramito mandato a Siae) la distribuzione on line e la sincronizzazione.

Ma o queste erano prevista nei contratti alla base della realizzazione dei dischi oppure no. Nel secondo caso non c’è modo per riconoscere a Sony diritti che non ha ; nè può lametnarsi di revoca del mandato a SIae, che è liberissimo in capo al’autore o ai suoi eredi o aventi casua. Nel primo caso allora Sony avrebbe potuto procedere e poi resistere ad eventuali domande degli eredi Battisti.

Il vero punto allora è se i contratti iniziali (quando le facoltà desiderate da Sony tecnocologicamente non esistevano) potevano essere interpretati evolutivamente: nel senso di far rientarre nelle facolà acquisite da Sony pure quelle che tecnologcamente avrebbeero potuto venir eventualmente scoperte in futuro.

La risposta positiva è difficile, parrebbe: ma sarebbe stato bene riproatare in setnenza uil testo eatto dei poatti pertinenti.

Giustamente rigettata la (incomprensibile) domanda di danno basata su contatto sociale tra Battisti e le case editrici musicali (il contratto per la produzione del dischi era stato sottoscritto da Battisti personalmente): non c’era alcun affidamento di Sony da tutelare in essi.

Palese infondatezza pure nella domanda verso gli amministratori delle due società ex art. 2476 c. 6 (ora c. 7) c.c. come danno diretto al terzi

Altro stop amministrativo alla tutela da copyright del disegno creato con AI

Da ringraziare Franklin Graves che su Linkedin dà notizia del provv. USCO-Review Board 5 settembre 2023, nel caso “Théâtre D’opéra Spatial” di Jason M. Allen (qui link al documento , da lui offerto).

Si tratta di una prima immagine creata con l’AI di Midjourney , poi ritoccata con Adobe Photoshop e Ggiapixel AI.

immmagine creata inizialmente (sx) e immgine finale dopo adobe Photoshop e Gigapixel AI (dx)

Ne aveva chiesto protezione senza menzionare la modalità creativa.

Anche l’organo amministrativamente gerarchico rigetta perchè l’opera creata da AI non contiene creatività umana nè l’istante, pur invitato, ha distinto ciò che è creato da AI e ciò che è da lui creato.

Il lavoro fatto dall’istante era questo:

<<Because the Work here contains AI-generated material, the Board starts with an analysis
of the circumstances of the Work’s creation, including Mr. Allen’s use of an AI tool. According
to Mr. Allen, the Work was created by 1) initially generating an image using Midjourney (the
“Midjourney Image”), 2) using Adobe Photoshop to “beautify and adjust various cosmetic
details/flaws/artifacts, etc.” in the Midjourney Image, and 3) upscaling the image using Gigapixel AI>>.

Decisum conseguente:

<<In his Second Request, Mr. Allen asserts a number of arguments in support of his claim.
He argues that his use of Midjourney allows him to claim authorship of the image generated by
the service because he provided “creative input” when he “entered a series of prompts, adjusted
the scene, selected portions to focus on, and dictated the tone of the image.” Id. at 4. As
explained in his correspondence, Mr. Allen created a text prompt that began with a “big picture
description” that “focuse[d] on the overall subject of the piece.” Allen Sept. Creation
Explanation. He then added a second “big picture description” to the prompt text “as a way of
instructing the software that Mr. Allen is combining two ideas.” Id. Next, he added “the overall
image’s genre and category,” “certain professional artistic terms which direct the tone of the
piece,” “how lifelike [Mr. Allen] wanted the piece to appear,” a description of “how colors
[should be] used,” a description “to further define the composition,” “terms about what style/era
the artwork should depict,” and “a writing technique that Mr. Allen has established from
extensive testing” that would make the image “pop.” Id. He then “append[ed the prompt] with
various parameters which further instruct[ed] the software how to develop the image,”7 resulting
in a final text prompt that was “executed . . . into Midjourney to complete the process” and
resulted in the creation of the Midjourney Image above. Id.8
In the Board’s view, Mr. Allen’s actions as described do not make him the author of the
Midjourney Image because his sole contribution to the Midjourney Image was inputting the text
prompt that produced it. Although Mr. Allen describes “input[ing] numerous revisions and text
prompts at least 624 times” before producing the Midjourney Image, Allen Sept. Creation
Explanation, the steps in that process were ultimately dependent on how the Midjourney system
processed Mr. Allen’s prompts. According to Midjourney’s documentation, prompts “influence”
what the system generates and are “interpret[ed]” by Midjourney and “compared to its training
data.”9 As the Office has explained, “Midjourney does not interpret prompts as specific
instructions to create a particular expressive result,” because “Midjourney does not understand grammar, sentence structure, or words like humans.”10 It is the Office’s understanding that,
because Midjourney does not treat text prompts as direct instructions, users may need to attempt
hundreds of iterations before landing upon an image they find satisfactory. This appears to be
the case for Mr. Allen, who experimented with over 600 prompts before he “select[ed] and
crop[ped] out one ‘acceptable’ panel out of four potential images … (after hundreds were
previously generated).” Allen Sept. Creation Explanation. As the Office described in its March
guidance, “when an AI technology receives solely a prompt from a human and produces
complex written, visual, or musical works in response, the ‘traditional elements of authorship’
are determined and executed by the technology—not the human user.” AI Registration
Guidance, 88 Fed. Reg. at 16,192. And because the authorship in the Midjourney Image is more
than de minimis, Mr. Allen must exclude it from his claim. See id. at 16,193. Because Mr. Allen
has refused to limit his claim to exclude its non-human authorship elements, the Office cannot
register the Work as submitted.>>