Source: https://www.patentdocs.org/2018/11/index.html
Timestamp: 2019-04-19 10:37:51+00:00

Document:
The U.S. Patent and Trademark Office has released its 2018-2022 Strategic Plan, the first under new Director Andrew Iancu.
Director Iancu provides an introductory Message in which he cites the grant of U.S. Patent No. 10,000,000 on June 19th of this year that "authenticates the brilliance of our Founding Fathers and serves as a testament to those who continue to bring forward their creative genius, imagination, and talent for the betterment of all." While lauding the vitality of the Office's work in enabling inventors to contribute to the U.S. economy and the need for "predictable, reliable, and high-quality intellectual property (IP) rights," he notes that the USPTO needs to "continually challenge itself" to provide such patent rights. He previews the Plan (which he calls "the roadmap for the future") by stating those goals: "(1) optimizing patent quality and timeliness, (2) optimizing trademark quality and timeliness, and (3) providing domestic and global leadership to improve IP policy, enforcement, and protection worldwide."
Fostering innovation, competitiveness and job growth in the United States by conducting high quality and timely patent and trademark examination and review proceedings in order to produce reliable and predictable intellectual property rights; guiding intellectual property policy, and improving intellectual property rights protection; and delivering intellectual property information and education worldwide.
• Through a culture of quality, we promote excellence throughout the organization by valuing accurate and consistent results, primarily in the examination processes.
• Through timeliness, we recognize the needs of our customers and stakeholders to have our products and services delivered at a time that meets their individual needs.
• Through efficiency, we are sound financial stewards of the user fees paid by our customers, and we use the lowest number of inputs to create the greatest number of outputs, maximizing results.
• Through effectiveness, we listen to our customers and stakeholders, and incorporate their feedback.
• Through accountability, we measure our activities, accept responsibility for them, and disclose the results in a transparent manner.
The Plan also contains Accompanying Information that explicates the Strategic Planning Process, supported by relevant External Factors (Appendix A) and Background of the Strategic Framework (Appendix B).
While the language of the Strategic Plan is replete with precatory and aspirational language of how the Office intends to achieve its Goals, it also advantageously contains sufficient specificity for the patent community and the public to be able to assess how well the Office satisfies the expectations raised in the Plan, and supplies the metrics to do so. Director Iancu has raised expectations by his patent-supportive rhetoric since he assumed the position. This Plan reflects those expectations and provides not only hope but also the basis for evaluating how well those hopes are realized.
Growth is one of the defining properties of being biologically alive, and the biology of growth involves both cellular proliferation and differentiation from stem cells to one or a plurality of differentiated cells making up a bodily tissue. Examples range from regeneration of an arm in starfish (and in the related sea cucumber, the ability to propel its gastrointestinal tract to distract a predator) through phylogeny to the capacity of human liver to regrow after resection. Even normal development involves production of new tissues, and in some species both aspects of this biological phenomenon can be appreciated. For example, deer grow and regrow antlers, which can account for 28% of the animal's body weight, and their regrowth rates are so rapid that deer can produce 10 kg or more over 2-3 months. Deer antlers and human bone, while showing very different growth rates (2 cm/day for deer antler, 2 cm/yr for femoral growth during puberty) and other features have in common intramembranous and endochondrial modes of ossification, and this similarity prompted a research group* to undertake a study comparing the pattern of expressed genes in deer antlers and human bone. The results of this study, entitled "Identifying deer antler uhrf1 proliferation and s100a10 mineralization genes using comparative RNA-seq," were published on October 31 in Stem Cell Research and Therapy.
Due to difficulties in elucidating the genes involved in antler production in the past, these researchers initiated their study by isolating reserve mesenchymal (RM) cells from fallow deer and human mesenchymal stem cells (hMSCs) and comparing RNA expression patterns in cultures of both cell types. RM cells were chosen for the study because they expressed robust amounts of alkaline phosphatase (ALP) when contacted with bone morphogenic protein 2 (BMP-2) when assayed using immunofluorescence staining. Cell growth for RM and hMSCs cultures were determined, and RM cells yielded 1.1-4.5 x 105 cells after six day's growth in culture with a doubling time of 17.9-24.7 h and hMSCs yielded 1.2-3.9 x 104 cells after six days in culture (10-fold fewer cells) and a doubling time of 37.8-60.1 h. Differentiation studies showed RM cells differentiated into chondrocytes and osteocytes rather than adipocytes (the predominant differentiated cell type for hESCs). In the presence of BMP-2, RM cells expressed ALP as well as osteocalcin, osteoblast-specific factor 1 and runt-related transcription factor, consistent with the results of the differentiation studies. RM cells were also preferentially stained with Alizarin Red S in mineralization studies (1702.2.ug/mL) compared with hESCs (98.0 ug/mL). These and other comparison of RM and hESCs supported the use of these two cell types for further studies of gene expression related to antler and bone growth in these two species.
These results were verified by immunofluorescence, gene over-expression and gene knockdown studies, using FD-derived proliferation candidate gene uhrf1 gene and mineralization candidate gene s100a10. These results were consistent with the purported role in antler growth and regeneration suggested by gene expression patterns identified in in vitro cultured RM cells.
The authors of this study suggest that "[t]he approach developed here may be broadly applied towards studying another biological phenomenon, and the genes identified with this approach will not only advance our understanding of mammalian bone regeneration but also offer promising therapeutic strategies for bone tissue engineering." In addition, they speculate that "a similar comparison strategy can be applied to almost any tissue for identifying the contributions of uniquely expressed genes to a phenomenon of interest." These sentiments are supported by the results set forth in this paper, where a previously intractable biological problem (identification of genes associated with deer antler growth) was successfully addressed using the combination of in vitro biological and RNA-seq approaches set forth herein.
It seems like everyone is talking about artificial intelligence, especially the subset thereof referred to as machine learning. While some of the discussion is cast in terms of politically-stirred angst about human jobs being replaced by robots or algorithms, a more informed and rational dialog would also set forth machine learning as a platform for the next great breakthroughs in science, technology, medicine, and lifestyle. Regardless of rhetorical positioning, machine learning represents a fundamental shift in how problems are solved across industries and lines of business. In the near future, a machine learning library may become a standard part of all computers, just like networking and database technologies have in the past.
For the majority of the existence of computers, programmers wrote functions that were designed to take some input and produce a desired output. Machine learning inverts this paradigm. A data set (which in practice usually needs to be quite extensive) of mappings between inputs and their respective desired outputs is obtained. This data set is fed into a machine learning algorithm (e.g., a neural network, decision tree, support vector machine, etc.) which trains a model to "learn" a function that produces the mappings with a reasonably high accuracy. In other words, if you give the computer a large enough set of inputs and outputs, it finds the function for you. And this function may even be able to produce the correct output for input that it has not seen during training. The programmer (who has now earned the snazzy title of "data scientist") prepares the mappings, selects and tunes the machine learning algorithm, and evaluates the resulting model's performance. Once the model is sufficiently accurate on test data, it can be deployed for production use.
Such models are already in use today -- they suggest what products you might want to purchase, and movies and music that you might find interesting. They also silently improve the quality of photos taken from your digital camera, help security screeners at airports and sports stadiums, detect financial fraud, and improve your online search results. And the real-world applicability of machine learning has yet to peak.
Naturally, innovators in machine learning, like innovators in any other industry sector, seek to protect their work with patents. Indeed, the number of patent application filings related to artificial intelligence and machine learning has been growing dramatically in the past several years, especially in the U.S. Nonetheless, inventors, applicants, and even patent attorneys have often struggled to adopt a claiming strategy for inventions incorporating machine learning.
Not surprisingly, the strategy to be taken when drafting such claims depends on the character of the invention and how machine learning is incorporated therein. Thus, there is no one particular "silver bullet" approach. Use of just a small number of guidelines, however, can help you focus your claim drafting approach in a direction that may bear the most fruit for your clients.
In short, these guidelines involve looking to several areas where innovation is most likely to occur in machine learning inventions: (i) the structure of the model, (ii) the training process, (iii) input data preparation, (iv) input data mapping to the model, and (v) post-processing and interpretation of output data from the model. Along with these five "positive" rules are two "negative" rules of what not to do: (i) do not mix the training phase and the execution phase in the same claim, and (ii) be careful with inventions that are no more than just conventionally applying an existing model to existing data.
Each will be addressed in turn. But throughout this discussion, it is important to remember that details matter. Like claims to most inventions, claims to a machine learning process must specify enough detail for the reviewer (e.g., patent examiner or judge) to be able to determine that the invention as claimed is substantial enough for patenting. High-level or vague claims are unlikely to meet the requirements of novelty and non-obviousness, much less subject-matter eligibility.
When the invention involves a new or unusual model structure, this aspect may be a candidate for claiming. For example, is a neural network with a particular pattern of layers or number of neurons per layer key to providing a desirable result? Or are multiple neural networks used in parallel or tandem? Taking this point one step further, the best known solutions for some problems involve ensembles of two or more models. If your problem is well-addressed by an ensemble, and the structure of the ensemble is new, that might be the starting point of a claim.
Especially when an unconventional model is used, the training of this model may be unconventional as well. This provides another avenue for claiming. For instance, are parts of the model trained with specific subsets of the input data? Or is the model trained in phases? Does the training employ a clever form of parallel processing in order to reduce training time? Regardless, if the execution of the trained model (i.e., its application to input data) is carried out in an orthodox manner, the training might be more easily protected and should be carefully considered. On the other hand, detecting an infringing training phase might be difficult once the training is complete and the model is commercially deployed.
Data scientists spend a surprising amount of time preparing their data for introduction to a model. Real-world data is messy and often needs to be normalized, transformed, have outliers removed, or otherwise processed so that its characteristics can help the model produce quality results. Often, this is a trial-and-error procedure, with the data scientist attempting several approaches before finding one that is satisfactory. For instance, some natural language processing models might be utilize word counts, but common words such as "and", "the", "it", and so on may be removed from these counts in order to force the model to focus of the words with a contextual meaning closer to that of the problem being addressed.
Once you have chosen your model and prepared your data, you have to map the input data to the model's input. This is frequently an inherent process, as the model and input data preparation are selected to work together. Nonetheless, this mapping can be of interest. For example, a neural network that classifies sections of black and white images might have 64 inputs, one for each pixel in an 8x8 patch of an image. The individual inputs may be numbers representing the intensity (brightness) of the respective pixels. To the extent that such a mapping is innovative, it is fodder for a claim.
Just because a model provides a result, even a desirable result, that does not mean that the overall machine learning pipeline is over. In some cases, the raw output of a model has to be transformed, normalized, or run through another algorithm to provide useful output data. In other cases, and as hinted at above, the output of one model may be used (with or without intermediate processing) as input to another model. For certain classes of models, part of the model itself is the output -- perhaps a particular layer of a neural network that encodes a semantic meaning of the input.
A machine learning model is first trained and then deployed for production use. Thus, it is likely that the entity performing the training and the entity executing the model are different. Accordingly, combining steps or elements directed to both training and execution in a claim can result in divided infringement of the claim by these entities. Instead, draft separate independent claims for the training and execution phases. In cases where the execution phase does not seem to have enough substance to stand alone, the details of the training phase can be provided in passive clauses (e.g., "wherein the model was trained by comparing random pixel inputs to ground-truth image classifications . . .").
As with any type of technology, some solutions are less patentable than others. If the invention at hand applies an off-the-shelf model to a known data set in a non-specific manner, and none of the above "positive" rules are pertinent, then the machine learning aspects might not be where to focus your claiming efforts. While machine learning is new, the general concept of applying any generic model to a data set is likely to be considered obvious, at least. Instead, look to other aspects of the invention for protectable innovation.
While machine learning will almost certainly remain an active area of patenting in the coming years, the path to obtaining meaningful protection for machine learning inventions is scattered with pitfalls. While the above guidelines are not exhaustive, following them will avoid most of the elementary traps.
And finally, a note to patent agents and attorneys attempting to break into machine learning -- do your homework. Do not treat machine learning as a black box technique that can be added to claims as an afterthought. Instead, educate yourself in how and why machine learning works. Read papers and books, watch videos, take a course, do some programming -- use whatever means are available. Doing so will dramatically enhance your ability to draft meaningful claims (and other parts of patent applications) that employ these technologies.
December 6, 2018 - "Apportioning Patent Damages: Analysis, Calculations for Multi-Component Products, Impact of Claim Scope -- Implications of Apportionment for Patent Drafting, Licensing and Enforcement" (Strafford) - 1:00 to 2:30 pm (EST).
• The PTAB's recent flexibility in sometimes allowing a petitioner to choose between naming additional RPIs to win institution or risking non-institution by contesting the need to do so.
Washington College of Law Post-Argument Discussion of Helsinn Healthcare S.A. v. Teva Pharmaceuticals USA Inc.
As part of its ongoing Supreme Court series, the American University Washington College of Law Program on Information Justice & Intellectual Property will be hosting a post-argument discussion on the Helsinn Healthcare S.A. v. Teva Pharmaceuticals USA Inc. case beginning at 4:00 pm (Eastern) on December 4, 2018 at the American University Washington College of Law in Washington, DC.
Additional information about the post-argument discussion, including registration/CLE information, can be found here.
• How does the claim scope impact apportionment?
• How can counsel draft patent claims to maximize the value of the invention?
• What litigation strategies can companies and their counsel employ to improve their damages analysis?
• How are the courts applying the framework for patent eligibility created in Alice Corp.?
• How can patent litigation defendants take advantage of the guidance for §101 challenges?
• What are best practices for patent counsel to demonstrate patent eligibility?
• How can patent counsel address subject eligibility rejections made by patent examiners?
The authors and contributors of Patent Docs wish their readers and families a Happy Thanksgiving. Publication of Patent Docs will resume on November 23rd.
What is a Printed Publication for Prior Art Purposes?
Patent owner Acceleration Bay, LLC ("Acceleration") appealed the final written decisions of the Patent Trial and Appeal Board holding unpatentable claims of U.S. Patent Nos. 6,829,634; 6,701,344; and 6,714,966. Activision Blizzard, Inc., Electronic Arts Inc., Take-Two Interactive Software, Inc., 2k Sports, Inc., and Rockstar Games, Inc. (collectively, "Blizzard") cross-appealed portions of the Board's decisions holding that the Lin article is not a printed publication under 35 U.S.C. § 102(a), among other issues.
Here, we look at the determination of features in a "preamble" as being limitations of the claim, as well as, requirements of an article being considered a printed publication for prior art purposes.
The patents at issue are directed to a broadcast technique in which a broadcast channel overlays a point-to-point communications network. The communications network consists of a graph of point-to-point connections between host computers or "nodes," through which the broadcast channel is implemented.
Blizzard filed six inter partes review ("IPR") petitions—two for each of the '344, '966, and '634 patents—based principally on two different prior art references: one set of IPRs challenged claims based on the Shoubridge article alone or combined with a prior art book Direct-Play ("Shoubridge IPRs"), and another set of IPRs challenged claims based on the Lin article alone or combined with DirectPlay ("Lin IPRs").
The Board instituted an IPR on each petition, on many of the grounds and claims raised, and rendered six final decisions. In the Shoubridge IPRs, the Board determined many claims to be unpatentable, and in the Lin IPRs, the Board concluded that Lin is not a printed publication under 35 U.S.C. § 102(a) and thus determined Blizzard failed to show the challenged claims are unpatentable over Lin.
1. A computer network for providing an information delivery service for a plurality of participants, each participant having connections to at least three neighbor participants, wherein an originating participant sends data to the other participants by sending the data through each of its connections to its neighbor participants and wherein each participant sends data that it receives from a neighbor participant to its other neighbor participants, further wherein the network is m-regular, where m is the exact number of neighbor participants of each participant and further wherein the number of participants is at least two greater than m thus resulting in a non-complete graph.
Claim 1 of the '344 patent is very similar and begins "1. A computer network for providing a game environment for a plurality of participants, ...".
Acceleration argued that the terms "game environment" and "information delivery service," appearing in the '344 and '966 patents, respectively, should have been given patentable weight despite appearing in the preambles because they provide structure for the remainder of the claims. Acceleration alternatively argues that these terms appear in the body of the claims because there is no transition phrase denoting a preamble.
Regarding the latter argument, the Federal Circuit responded that Acceleration's poor claim drafting will not be an excuse for it to infuse confusion into its claim scope, and simply concluded that "game environment" and "information delivery service" are part of the preamble of the claims.
The Federal Circuit noted that there is no beneficial purpose to be served by failing to include a transition word in a claim to clearly delineate the claim's preamble from the body, and it "caution[ed] patentees against doing so."
Then, because the terms at issue appear in preambles, it must be determined whether the terms are limitations. General case law indicates that a preamble limits the invention if it recites essential structure or steps, or is necessary to give life, meaning, and vitality to the claim.
The Federal Circuit agreed with the Board that the claim terms "game environment" and "information delivery service" are non-limiting because they merely describe intended uses for what is otherwise a structurally complete invention. They do not impart any structure into or serve as antecedents for the claims at issue. Instead, they simply provide an intended use for what is otherwise a claim for a network.
Blizzard cross-appealed many portions of the Board's decisions in the Lin IPRs, and here we only discuss the issue of whether Lin is a printed publication under § 102(a).
Whether a reference qualifies as a printed publication under § 102 is a legal conclusion based on underlying fact findings. One such fact question is public accessibility. Because there are many ways in which a reference may be disseminated to the interested public, public accessibility has been called the touchstone in determining whether a reference constitutes a printed publication.
Case law has indicated that a reference is considered publicly accessible if it was disseminated or otherwise made available to the extent that persons interested and ordinarily skilled in the subject matter or art, exercising reasonable diligence, can locate it.
The Board found that Lin was not publicly accessible before the critical date.
Based on the testimony of Glenn Little, a Systems Administrator at the Computer Science and Engineering ("CSE") department of the University of California, San Diego ("UCSD"), the Board found that Lin had been uploaded to the CSE Technical Reports Library's website as of November 23, 1999, which is not challenged on appeal. As the Board explained, according to Mr. Little, "the CSE department regularly maintains electronic technical reports and records concerning those reports, and a staff member assigns a unique identifier to each report based on the year it was uploaded and the relative order it was uploaded in comparison to other papers."
The Board then noted that public accessibility requires more than technical accessibility. Because there was no evidence that Lin was disseminated to the public, the Board focused on whether an interested skilled artisan, using reasonable diligence, would have found Lin on the CSE Technical Reports Library website. The Board found that despite some indexing and search functionality on the website, Lin was not publicly accessible. It found the website allowed a user to view a list of technical reports indexed only by author or year and that there was no evidence as to how many reports were in the Library's database in 1999. The Board determined that at best, Blizzard's evidence suggests that an artisan might have located Lin by skimming through potentially hundreds of titles in the same year, with most containing unrelated subject matter, or by viewing all titles in the database listed by author, when the authors were not particularly well known. The Board also found the website's advanced search form to be deficient. It found that while the advanced search form appeared to allow a user to search keywords for author, title, and abstract fields, evidence demonstrated that functionality was not reliable. In sum, the Board determined that Blizzard had not shown sufficiently that the UCSD CSE Technical Reports Library was searchable or indexed in a meaningful way so that a person of ordinary skill in the art would have located Lin. The Board, therefore, concluded Lin is not a printed publication under § 102(a).
The Federal Circuit found no error in the Board's reasoning. Namely, the Federal Circuit found that substantial evidence supports the Board's findings that Lin was not publicly accessible, including that Lin was not indexed in a meaningful way and that the website's advanced search form was deficient. Mr. Little testified he does not know how the search works or how keywords are generated, that he never searched for Lin using the advanced search form, and that it was not the CSE department's practice to verify the advanced search capability for title and abstract when a new article was uploaded. Mr. Little also admitted it was possible the search function did not work.
Acceleration also presented evidence that a recent advanced search for keywords in the title and abstract of Lin failed to produce any results.
Blizzard argued these results are unauthenticated hearsay and are based on searches conducted years after the critical date, but Mr. Little testified that as to the website, "[i]t's pretty much the same, actually, between  and now. We're running the same software." The Federal Circuit found no reason to discount the Board's weighing of the evidence. Substantial evidence was thus found to support the Board's finding that there was insufficient evidence of record to support a finding that a person of ordinary skill in the art in 1999 could have located Lin using the CSE Library website's search function.
The Federal Circuit thus distinguished a similar case: In re Lister, 583 F.3d 1307 (Fed. Cir. 2009). In Lister, a reasonably diligent researcher with access to a database that permits the searching of titles by keyword would be able to attempt several searches using a variety of keyword combinations, and thus, the manuscript at issue was publicly accessible as of the date it was included in databases that permitted keyword searching of titles.
The Federal Circuit noted that unlike in Lister, here the record supports the Board's finding that the CSE Library website's advanced search function did not successfully permit keyword searching of titles, a key feature in Lister. The Board's fact finding that, with available reports indexed only by author or year, Lin was not meaningfully indexed, was found to be supported by substantial evidence.
Blizzard argued that because Lin was indexed by title for a given year, author name, and unique sequence number, that was sufficient for public accessibility. But, the Federal Circuit stated that the test for public accessibility is not "has the reference been indexed?" Where indexing is concerned, whether online or in tangible media, the ultimate question has been whether the reference was available to the extent that persons interested and ordinarily skilled in the subject matter or art, exercising reasonable diligence, can locate it.
Last month, in FWP IP ApS v. Biogen MA, Inc., the Federal Circuit affirmed a decision by the U.S. Patent and Trademark Office's Patent Trial and Appeal Board granting Biogen's motion that FWP's U.S. Application No. 11/576,871 did not provide an adequate written description under 35 U.S.C. § 112 for claims directed to a method for treating multiple sclerosis. The MS treatment at dispute involves administering a specific daily dosage (480 mg) of fumarates, specifically dimethyl fumarate (DMF) and/or monomethyl fumarate (MMF), to a subject.
The appeal arose from an interference proceeding involving the above method for treating multiple sclerosis. U.S. Patent No. 8,399,514, owned by Biogen, describes and claims such a method. The '871 application, which was assigned to FWP by original Appellant Forward Pharma A/S after the appeal was docketed, discloses controlled release compositions of fumarates. In the interference proceeding, Forward argued that the '871 application describes the treatment method in dispute. Although the Board determined that the '871 application had an earlier priority date than the '514 patent, it granted Biogen's motion that the MS treatment method Forward sought to claim was not supported by an adequate written description.
The '871 application is a U.S. national phase filing of a PCT application that was filed on October 7, 2005, and which claims priority to a Danish patent application filed on October 8, 2004. The '514 patent claims priority to a U.S. provisional patent application filed on February 8, 2007. During prosecution of the '871 application, Forward cancelled all pending claims and added claims that closely tracked the claims that issued in the '514 patent. The Board declared an interference between the '871 application and '514 patent, designating Forward as the senior party with a constructive reduction to practice date of October 8, 2004.
(b) one or more pharmaceutically acceptable excipients, wherein the therapeutically effective amount of dimethyl fumarate is about 480 mg per day.
Looking to claim 69, the Board distilled the claimed treatment method at issue into three limitations: (1) an MS treatment, (2) by oral administration of a therapeutically effective amount of DMF and/or MMF, at (3) a dosage of 480 mg per day. With respect to the first limitation, the Board determined that the '871 application lists over twenty diseases and conditions, that MS is not identified as a disease or condition of particular interest, and that the application instead focuses on psoriasis and conditions associated with psoriasis. Regarding the third limitation, the Board determined that while the 480 mg/day dosage is expressly mentioned in the specification, there "is no discussion that would guide one skilled in the art to treat MS with a therapeutically effective dose of 480 mg/day, or any other therapeutically effective dose within the ranges disclosed."
The Board also rejected Forward's arguments based on Snitzer v. Etzel, 465 F.2d 899 (CCPA 1972); Falkner v. Inglis, 448 F.3d 1357 (Fed. Cir. 2006); and Streck, Inc. v. Research & Diagnostic Sys., Inc., 665 F.3d 1269 (Fed. Cir. 2012). The Board determined that unlike the invention in Snitzer, Forward's case required the selection and combination of claim elements from more than a single limited list, and that unlike Falkner and Streck, the '871 application did not provide the blaze marks necessary to guide a skilled artisan to the claimed invention. The Board concluded that even though each required claim element is mentioned separately in the '871 application, the specification did not disclose the claimed invention in a manner that adequately describes the claimed MS treatment to a skilled artisan, and therefore, that the claims of the '871 application failed to meet the written description requirement.
On appeal, Forward argued that the interference count was disclosed in an up-scale table in the '871 application, which Forward argued (1) discloses a 480 mg/day dosage, (2) is reasonably directed at using DMF to treat the listed conditions, and (3) is linked to the treatment of MS. The up-scale table describes scaling up the daily dosage level of fumerates over a nine-week period, with the 480 mg/day dosage occurring during week seven, and is designed to minimize the side-effects of ingesting fumarates by increasing the dose gradually over time to allow the patient to acclimate. The Federal Circuit, however, stated that it was not persuaded by Forward's argument, and agreed with the Board that "the '871 application does not disclose the now-claimed MS treatment as a unified whole."
With respect to the 480 mg/day dosage, the Federal Circuit noted that this dosage is an interim dosage, and that Forward failed to present persuasive evidence as to why a skilled artisan would have understood the week seven interim dosage to be therapeutically effective. The Court also noted that Biogen's expert testified that because MS is a chronic disease, a skilled artisan would not have viewed a one week, interim dosage in the middle of a nine week up-scale schedule as an adequate treatment dosage, let alone one for a particular disease such as MS.
In affirming the Board's decision, the Federal Circuit stated that "[g]iven the brief references to MS and the lack of recognition of 480 mg/day as a therapeutically effective daily dosage, we agree with the Board’s finding that there 'is no discussion [in the '871 application] that would guide one skilled in the art to treat MS with a therapeutically effective dose of 480 mg/day. . . .'" The Court was also unpersuaded by Forward's attempt to use the prior art to supply the link between the therapeutically effective dose of 480 mg/day and MS, noting that "even if we allow Forward to rely on the prior art for establishing a prior, known link between MS and fumarates, the prior art does not teach the key limitation of the count: the 480 mg daily dosage."
Th[e original] claims are mainly directed to controlled release compositions of numerous fumarates and pharmaceutically acceptable salts. Some claims recited numerous possible dosing schedules (claims 30–32) and dosages (claims 33–38). And others replicate the laundry list of diseases and conditions enumerated in the specification (claims 44 and 45). This large number of disease conditions, dosages, dosing schedules, active ingredients, pharmaceutical formulations for controlled release, and combinations thereof covered by the original claims detracts from Forward's argument that it possessed and invented the now-claimed, specific MS treatment. . . . Rather, what the scale of the claims demonstrate is that Forward possessed, as of the 2004 critical date, a mere wish for obtaining some type of fumarate formulation to treat any one of a number of diseases and conditions, one of which was MS, using almost any possible daily dosage.
Finding that "the '871 application does not disclose 480 mg of fumarates per day as a therapeutically effective dose for treating MS," the Federal Circuit determined that substantial evidence supported the Board's finding that the MS treatment Forward sought to claim was not supported by an adequate written description under 35 U.S.C. § 112. The Federal Circuit therefore affirmed the Board's decision.

References: v. 
 v. 
 §101
 § 102
 § 102
 § 102
 § 102
 § 102
 v. 
 Application No. 11
 § 112
 v. 
 v. 
 v. 
 § 112