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Beyond Med Plans (“BMP”) is a discount medical plan, affording members easy and immediate access to sought after medical services at reduced rates. Through its comprehensive provider network, members receive benefits on elective, voluntary and cosmetic treatments that traditional health ...

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Beyond Medical is a technology company developing digital telehealth solutions and manufacturing of medical grade PPE equipment.

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Beyond Med is a membership program that elevates your health and well-being by providing access to a proprietary network of board-certified doctors and licensed providers at reduced rates of up to 20% off on elective and cosmetic services.

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Through its comprehensive provider network, members receive benefits on elective, voluntary and cosmetic treatments that traditional health plans do not offer, which include: Plastic Surgery, Infertility Treatments, Med Spa, Dermatology, Weight Loss and Bariatric, Veterinary Care, Holistic Medicine, Chiropractic Care and more

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Introduction Remember when Femtech was a brand-new term nobody knew? It feels like we have been using this word forever, but it has been around for less than a decade! Yet the history of products, technologies, and services addressing health issues specific or prevalent to women started long ago, ages before this umbrella term was first used in 2016 to address this rapidly evolving market. Since then, this industry has had major plot twists, from Maven Clinic becoming the first unicorn in 2021 to venture capital infusions shaking up the game; the sector is sizzling hot! Femtech And let us give a thumbs-up to the growing acceptance and issues. It is no longer a niche but a booming market, with its size estimated at over $55 billion in 2022 and . de-stigmatization of women’s health expected to surpass $100 billion by 2030 Why is it important to me? I am a digital tech specialist coming from the Artificial Intelligence/ Machine Learning field with a rich background in building and successfully deploying voice recognition, AI cameras, and virtual assistant products catering to millions. Lately, I have shifted my focus, stepping into the intriguing world of Femtech and healthcare drawn by the promise of AI to simplify and enhance healthcare solutions, changing stagnant approaches to women’s healthcare through daring initiatives. With this article providing a thoughtful inquiry into how advanced technologies like LLMs shape modern healthcare and Femtech, I invite you to join me on this exciting journey. Together, we will take a look at how boosts the Femtech market and sift through the new directions and strategies reshaping this burgeoning sector. Our aim will be not only to learn about the tendencies of the industry but also to identify key patterns that could offer thrilling opportunities. And so, if you are curious like me about how novel technologies improve healthcare experiences for women and what strategic footholds exist in the Femtech market, let us dive in and explore. AI/ML Current trends and thrilling developments in Femtech In the last few years, the Femtech sector has been undergoing a transformative shift. Gone are the days when Femtech products predominantly focused on menstrual cycle tracking and fertility planning. Today, the industry is blossoming with more specialized solutions, from advanced home-testing kits to cutting-edge wearables. Technological advances have attracted a range of strong players in a multitude of niche markets, each aiming to revolutionize different aspects of women’s health. 1. From Basic Tracking to Comprehensive Health Solutions Take Sonio, a Paris-based Femtech firm. They recently received FDA clearance for their AI product, Sonio Detect, which aims to redefine prenatal ultrasounds. What sets Sonio apart is its manufacturer-independent tool that simplifies the screening process while raising its efficiency and accuracy, offering an easy-to-use yet powerful solution. San Francisco startup Mira is pioneering another monumental shift in Femtech by embedding lab technology into a handheld fertility monitor. With a user base exceeding 76,000, Mira’s advanced fluorescent detector goes beyond traditional ovulation kits. The technology allows for daily health checks and has accumulated an impressive amount of data — over 8.5 million hormone data points and analysis of more than 670,000 menstrual cycles. With AI-driven insights into hormone fluctuations, Mira is offering more personalized and accurate predictions than ever before, quite literally putting a mini hormone lab in the palms of its users. Recently, The Inc. placed Mira at number 437 on its 2023 list of fastest-growing private companies in the US, ranking it the highest among Femtech companies. More complex products that require significant medical research are also quickly gaining momentum. Teal Health exemplifies this with its recent $8.8 million seed funding round, backed by prominent investors like Emerson Collective and Serena Ventures. The funds aim to build a comprehensive platform, starting with the crucial issue of cervical cancer. women’s telemedicine 2. The Rise of Wearable Innovations Wearable technology is another frontier where Femtech is making significant strides. One famous example is Bloomlife, a clinically validated diagnostic device for pregnant women that allows future mothers to track contractions, giving them a better awareness of their progress. Movano Health has captured attention with its Evie Ring — a wearable gadget currently under examination by the FDA. Upon receiving FDA clearance, the Evie Ring will have the potential to set new standards in women’s health monitoring, offering an integrated, round-the-clock health monitoring and tracking solution right at the user’s fingertips. Ava, a fertility and pregnancy tracking bracelet with an accuracy rate of over 90%, has already been cleared by the FDA. Established in 2014, well before we even started using the term ‘Femtech,’ Ava is a prime example of a successful Femtech startup. Having attracted over $42 million in funding, the company reported to have helped over 70 thousand women in 36 countries to get pregnant by 2020. 3. The Expansion to Different Age Groups One of the noteworthy trends in Femtech recently is the surge in startups focusing on menopause. According to Female Insights, over 30% of companies in this sector were founded in the last three years alone. This uptick is accompanied by increasing diversity in the types of solutions being offered to tackle menopausal symptoms — from mobile apps that track hormones and symptoms and platforms facilitating user interaction and community building to consumer goods and therapeutic medications. According to Pubmed, by 2030, up to 1.2 billion women will be in or approaching menopause globally, with 47 million new women joining every year. By 2025, it is estimated that over 1 billion people worldwide—about 12% of the population—will be going through menopause. Financially, the menopause product market was valued at $15 billion in 2020 and is projected to skyrocket to $23 billion by 2028. These figures illustrate a maturing market with significant financial potential, underscoring why it has become a magnet for innovation and investment. An Irish startup, IdentifyHer, offers menopausal women sensor solutions. Controlled through a smartphone app, the sensor passively gathers information on menopause symptoms without interfering with the wearer's daily activities. The app also makes use of the data gathered to track progress and assess the effectiveness of lifestyle changes. The collected data is presented in periodic reports and can be used by medical professionals to diagnose and treat menopausal symptoms. AI-driven Midday is another customized menopause product developed by US-based startup Lisa Health that offers comprehensive management of the menopause journey. Midday’s solution, including a wearable device, an AI-based symptom tracking system, and a decision support tool for hormone therapy, uses advanced tracking to determine the menopause stage and anticipate symptoms. The app is used by doctors to manage the entire menopause process and provide decision support, while users are provided with a variety of therapeutic options and informational resources to give them personalized insights and get them ready for every stage of menopause. 4. Going Beyond Just Health Although Femtech is considered, first of all, a rising star in the Medtech industry, the possibilities of women-oriented digital technology products are not limited to healthcare alone. Recently, the term started to see new applications, one of which is Women’s Safety. Indian startup Shakti Wearables manufactures Shakti, a women’s defensive wearable device that increases women's safety in dangerous situations. When the wearer activates the button on the device, a small, non-lethal current is released, electrifying an attacker, while the internal insulation of the bangle keeps the wearer safe; this gives women a chance to flee their attackers and call for assistance. Additionally, Shakti sends the wearer's most important three contacts a WhatsApp message with their most recent live location. Another interesting personal safety app is being developed by Australian startup Safely, offering women peer-to-peer support in tense or frightening circumstances. With their contacts, users of the app can create private groups or circles for internal communication and location sharing. In case of emergency, users can also switch on the high-alert mode and notify each of their designated guardians. The app then uses live monitoring, records live video and audio, sends GPS updates, and saves all of this data to the cloud for later use as evidence. Additionally, users receive safety alerts from locations of interest to help them plan their commute or travel safely. Expanding healthcare’s reach with new technologies The world of healthcare is rapidly evolving, especially when it comes to Femtech. This dynamic transformation is powered by modern innovative technologies that are reshaping how women engage with healthcare and how healthcare providers offer services tailored to them. 1. The Personalization Revolution Fueled by LLMs One of the most disruptive advancements in this domain is the rise of Large Language Models (LLMs) like ChatGPT and GPT-4. Far from being a mere tech fad, LLMs are revolutionizing healthcare, with Femtech embracing this new technology. LLMs have the potential to serve as virtual nursing assistants, supporting busy doctors in responding to patients' basic queries about medications or health conditions. As you will see from the examples below, LLMs are proving to be instrumental in facilitating the creation of highly promising Femtech products, from personalized consulting products to innovative community engagement tools. One of the most intriguing examples of the implementation of this technology in Femtech is Soula, a Cyprus-based startup that provides women with support during pregnancy, childbirth, and motherhood with its "AI-powered" assistant. Powered by Med-PaLM, an LLM developed by Google to generate answers to medical questions, the app is expected to be able to respond to inquiries about pregnancies and maternity issues and provide information on a range of health-related concerns. It will also be able to answer questions about diet, sleep, physical activity, and emotional self-care, as well as track fetal movement and kick frequency. Mexican startup Kuna utilizes an LLM-powered chatbot to give women a secure way to access information and advice on parenting and sexual health. The startup's , Kuna Chat, offers details on sex, reproduction, pregnancy, and parenthood. The startup also provides online consultations with experts for more complex or difficult questions. In this way, Kuna encourages sex education for the uneducated while offering trustworthy, practical, and concrete answers to users' queries. AI-powered chatbot 2. Cost-Effective Health Management An extraordinarily important aspect of building successful medical products is to make services affordable to the customer, reaching broader but less solvent user segments and geographies. In this vein, Femtech offers not only personalized but also cost-effective healthcare solutions. Apps often provide free basic versions, with premium services available through subscription fees. Quite obviously, the use of LLMs trained on medical data sets and specialized in specific product cases can provide a boost to the more affordable cost of the products themselves. For example, the Ava fertility bracelet mentioned in the previous section of this article costs around $249 in the U.S., and the accompanying app is free with the bracelet purchase. When considering that the average cost of infertility treatment in the U.S. is around $11,000—often not covered by basic healthcare plans—Femtech presents a more affordable starting point for many women. 3. Breaking Barriers in Remote and Rural Areas Digital technology in Femtech is also crucial for improving healthcare access in remote and rural areas, especially in low- or middle-income countries in Africa and Asia — two regions with the highest female populations. Femtech provides innovative solutions such as virtual prenatal visits and remote monitoring, to manage their health more cost-effectively, regardless of their location. empowering women One successful example of such a product is M♡THer – a virtual care product facilitating the diagnosis of gestational diabetes mellitus by measuring blood glucose levels, blood pressure, weight, and more. GDM affects up to 20% of pregnant women, leading to various birth complications, and M♡THer, developed by the Australia E-Health Research Centre, has already benefited thousands of women in remote areas of the region. 4. A Multifaceted Approach to Chronic Diseases Femtech is not solely about reproductive health; it also targets female-specific chronic diseases through a mix of prevention, societal change, and technology. Mobile apps and wearable devices make it easier to manage and monitor conditions over time, a vital component of tackling chronic diseases that affect women disproportionately. To name an example of a health issue specific to women, it is estimated that 1 in 10 women have endometriosis – the growth of endometrial tissue outside of the uterus that can cause significant problems from pelvic pain to infertility. DotLab, a US-based medical diagnostics company, pioneers medical tests in this area, applying machine learning mechanisms to diagnose and assess this currently incurable disease. On the other side of the Atlantic Ocean, Kheiron Medical Technologies, a UK Medtech startup, combines data science and radiology to help with the diagnosis of breast cancer, another terrible disease affecting predominantly women. According to published clinical trial results, the company’s breakthrough technology achieves previously unseen heights in malignancy detection in breast cancer screening. Applied Conclusions Recent years have shown rapid exponential growth in the number of Femtech companies and products, both through the use of innovative technologies and through the discovery of more and more new applications and solutions. With the Femtech market estimated to reach an imposing $100 billion globally by 2030, it is one of the areas of the modern technology economy that is expected to provide maximum lucrative opportunities for new bright ideas and prosperous businesses in the coming years. While deciding whether to follow this attractive route, aspiring Femtech entrepreneurs should consider the following market characteristics. While there are highly competitive niches in the Femtech market, specifically well-established fields like menstrual cycle and pregnancy tracking, there are also areas that are more attractive for entry. Examples of such areas include app-controlled wearables and menopause management. The largest share of the global women’s health market surprisingly pertains to chronic women’s health conditions (forecast to reach $218 billion by 2027), and only 1 percent of existing Femtech companies address this area. Did you hear that? That is an opportunity knocking. AI and specifically Large Language Models can be a powerful boost for Femtech products as (a) with proper customization, LLMs assist in scaling the product or service itself and strengthen the core part of the product, and (b) they provide magnificent tools for community development, and the community is said to be the most important part of a product! Organic communities built around products are both a way of monetization and a tool that increases retention, helping build durable community-led growth. Taking into account the fact that the Femtech market is rapidly developing with a simultaneous increase in competition, and given the general market conditions (in particular, subscription applications revenues are currently declining; even if positive dynamics have remained in the Medtech segment, it has become more difficult to attract and retain users), companies need to be cost-efficient and provide a highly personalized, secure service to survive and rise above the competitors. A relevant and extremely effective tool employed by more and more technology startups for achieving these goals, again, is AI technologies. Whether it's addressing questions about menstrual health, pregnancy, or chronic conditions, LLMs can fill the information gap quickly and efficiently. Statistically, only 6% of Femtech companies used AI in 2021, mostly in diagnostics and data analytics, and while this share has seen stable growth over the years, there is enough room for new inspired ideas and success stories in this area. All in all, the advent of heralds an exciting era for Femtech entrepreneurs. LLMs have the potential to revolutionize user engagement and content delivery, supporting busy healthcare professionals by handling preliminary patient communication, serving as virtual nursing assistants or health advisors, and simply bringing a personalized touch to technology. With cutting-edge AI technologies charging Femtech products, humanity has the chance to drastically improve the efficiency of the entire healthcare system catering to women. Finally, there is a chance to overcome the millennia-old barriers plaguing this area of medicine, such as high costs, gender bias, stigmatization of women's health-related topics, and a lack of resources. Large Language Models Powered by the latest technological advancements, Femtech waits for its next hero — and it may be you!

Beyond Wellness: How Femtech is Shaping the Future of Women's Healthcare

সুস্থতার বাইরে: কীভাবে ফেমটেক মহিলাদের স্বাস্থ্যসেবার ভবিষ্যত গঠন করছে

Beyond Wellness: Wie Femtech die Zukunft der Frauengesundheit gestaltet

Más allá del bienestar: cómo Femtech está dando forma al futuro de la atención médica de las mujeres

Au-delà du bien-être : comment Femtech façonne l'avenir des soins de santé des femmes

कल्याण से परे: कैसे फेमटेक महिलाओं के स्वास्थ्य देखभाल के भविष्य को आकार दे रहा है

ウェルネスを超えて: フェムテックが女性のヘルスケアの未来をどのように形作るか

웰니스를 넘어서: Femtech가 여성 건강 관리의 미래를 형성하는 방법

Além do bem-estar: como a Femtech está moldando o futuro da saúde feminina

За пределами здоровья: как Femtech формирует будущее женского здравоохранения

Wellness'ın Ötesinde: Femtech Kadın Sağlığı Hizmetlerinin Geleceğini Nasıl Şekillendiriyor?

Ngoài sức khỏe: Femtech đang định hình tương lai của ngành chăm sóc sức khỏe phụ nữ như thế nào

超越健康：Femtech 如何塑造女性医疗保健的未来

Oracle vs. Google (2014) Court Filing, retrieved on May 9, 2014, is part of . You can jump to any part in this filing . This is part 11 of 16. HackerNoon’s Legal PDF Series here 2. The Structure, Sequence, and Organization of the API Packages The district court found that the SSO of the Java API packages is creative and original, but nevertheless held that it is a “system or method of operation . . . and, therefore, cannot be copyrighted” under 17 U.S.C. § 102(b). Copyrightability Decision, 872 F. Supp. 2d at 976-77. In reaching this conclusion, the district court seems to have relied upon language contained in a First Circuit decision: Lotus Development Corp. v. Borland International, Inc., 49 F.3d 807 (1st Cir. 1995), aff’d without opinion by equally divided court, 516 U.S. 233 (1996).[9] In Lotus, it was undisputed that the defendant copied the menu command hierarchy and interface from Lotus 1- 2-3, a computer spreadsheet program “that enables users to perform accounting functions electronically on a computer.” 49 F.3d at 809. The menu command hierarchy referred to a series of commands—such as “Copy,” “Print,” and “Quit”—which were arranged into more than 50 menus and submenus. Id. Although the defendant did not copy any Lotus source code, it copied the menu command hierarchy into its rival program. The question before the court was “whether a computer menu command hierarchy is copyrightable subject matter.” Id. Although it accepted the district court’s finding that Lotus developers made some expressive choices in selecting and arranging the command terms, the First Circuit found that the command hierarchy was not copyrightable because, among other things, it was a “method of operation” under Section 102(b). In reaching this conclusion, the court defined a “method of operation” as “the means by which a person operates something, whether it be a car, a food processor, or a computer.” Id. at 815.[10] Because the Lotus menu command hierarchy provided “the means by which users control and operate Lotus 1-2-3,” it was deemed unprotectable. Id. For example, if users wanted to copy material, they would use the “Copy” command and the command terms would tell the computer what to do. According to the Lotus court, the “fact that Lotus developers could have designed the Lotus menu command hierarchy differently is immaterial to the question of whether it is a ‘method of operation.’” Id. at 816. (noting that “our initial inquiry is not whether the Lotus menu command hierarchy incorporates any expression”). The court further indicated that, “[i]f specific words are essential to operating something, then they are part of a ‘method of operation’ and, as such, are unprotectable.” Id. On appeal, Oracle argues that the district court’s reliance on Lotus is misplaced because it is distinguishable on its facts and is inconsistent with Ninth Circuit law. We agree. First, while the defendant in Lotus did not copy any of the underlying code, Google concedes that it copied portions of Oracle’s declaring source code verbatim. Second, the Lotus court found that the commands at issue there (copy, print, etc.) were not creative, but it is undisputed here that the declaring code and the structure and organization of the API packages are both creative and original. Finally, while the court in Lotus found the commands at issue were “essential to operating” the system, it is undisputed that—other than perhaps as to the three core packages—Google did not need to copy the structure, sequence, and organization of the Java API packages to write programs in the Java language. More importantly, however, the Ninth Circuit has not adopted the court’s “method of operation” reasoning in Lotus, and we conclude that it is inconsistent with binding precedent.[11] Specifically, we find that Lotus is inconsistent with Ninth Circuit case law recognizing that the structure, sequence, and organization of a computer program is eligible for copyright protection where it qualifies as an expression of an idea, rather than the idea itself. See Johnson Controls, 886 F.2d at 1175-76. And, while the court in Lotus held “that expression that is part of a ‘method of operation’ cannot be copyrighted,” 49 F.3d at 818, this court—applying Ninth Circuit law—reached the exact opposite conclusion, finding that copyright protects “the expression of [a] process or method,” Atari, 975 F.2d at 839. We find, moreover, that the hard and fast rule set down in Lotus and employed by the district court here— i.e., that elements which perform a function can never be copyrightable—is at odds with the Ninth Circuit’s endorsement of the abstraction-filtration-comparison analysis discussed earlier. As the Tenth Circuit concluded in expressly rejecting the Lotus “method of operation” analysis, in favor of the Second Circuit’s abstraction-filtrationcomparison test, “although an element of a work may be characterized as a method of operation, that element may nevertheless contain expression that is eligible for copyright protection.” Mitel, 124 F.3d at 1372. Specifically, the court found that Section 102(b) “does not extinguish the protection accorded a particular expression of an idea merely because that expression is embodied in a method of operation at a higher level of abstraction.” Id. Other courts agree that components of a program that can be characterized as a “method of operation” may nevertheless be copyrightable. For example, the Third Circuit rejected a defendant’s argument that operating system programs are “per se” uncopyrightable because an operating system is a “method of operation” for a computer. Apple Computer, Inc. v. Franklin Computer Corp., 714 F.2d 1240, 1250-52 (3d Cir. 1983). The court distinguished between the “method which instructs the computer to perform its operating functions” and “the instructions themselves,” and found that the instructions were copyrightable. Id. at 1250-51. In its analysis, the court noted: “[t]hat the words of a program are used ultimately in the implementation of a process should in no way affect their copyrightability.” Id. at 1252 (quoting CONTU Report at 21). The court focused “on whether the idea is capable of various modes of expression” and indicated that, “[i]f other programs can be written or created which perform the same function as [i]n Apple’s operating system program, then that program is an expression of the idea and hence copyrightable.” Id. at 1253. Notably, no other circuit has adopted the First Circuit’s “method of operation” analysis. Courts have likewise found that classifying a work as a “system” does not preclude copyright for the particular expression of that system. See Toro Co. v. R & R Prods. Co., 787 F.2d 1208, 1212 (8th Cir. 1986) (rejecting the district court’s decision that “appellant’s parts numbering system is not copyrightable because it is a ‘system’” and indicating that Section 102(b) does not preclude protection for the “particular expression” of that system); see also Am. Dental Ass’n v. Delta Dental Plans Ass’n, 126 F.3d 977, 980 (7th Cir. 1997) (“A dictionary cannot be called a ‘system’ just because new novels are written using words, all of which appear in the dictionary. Nor is word-processing software a ‘system’ just because it has a command structure for producing paragraphs.”). Here, the district court recognized that the SSO “resembles a taxonomy,” but found that “it is nevertheless a command structure, a system or method of operation—a long hierarchy of over six thousand commands to carry out pre-assigned functions.” Copyrightability Decision, 872 F. Supp. 2d at 999-1000.[12] In other words, the court concluded that, although the SSO is expressive, it is not copyrightable because it is also functional. The problem with the district court’s approach is that computer programs are by definition functional—they are all designed to accomplish some task. Indeed, the statutory definition of “computer program” acknowledges that they function “to bring about a certain result.” See 17 U.S.C. § 101 (defining a “computer program” as “a set of statements or instructions to be used directly or indirectly in a computer in order to bring about a certain result”). If we were to accept the district court’s suggestion that a computer program is uncopyrightable simply because it “carr[ies] out pre-assigned functions,” no computer program is protectable. That result contradicts Congress’s express intent to provide copyright protection to computer programs, as well as binding Ninth Circuit case law finding computer programs copyrightable, despite their utilitarian or functional purpose. Though the trial court did add the caveat that it “does not hold that the structure, sequence and organization of all computer programs may be stolen,” Copyrightability Decision, 872 F. Supp. 2d at 1002, it is hard to see how its method of operation analysis could lead to any other conclusion. While it does not appear that the Ninth Circuit has addressed the precise issue, we conclude that a set of commands to instruct a computer to carry out desired operations may contain expression that is eligible for copyright protection. See Mitel, 124 F.3d at 1372. We agree with Oracle that, under Ninth Circuit law, an original work—even one that serves a function—is entitled to copyright protection as long as the author had multiple ways to express the underlying idea. Section 102(b) does not, as Google seems to suggest, automatically deny copyright protection to elements of a computer program that are functional. Instead, as noted, Section 102(b) codifies the idea/expression dichotomy and the legislative history confirms that, among other things, Section 102(b) was “intended to make clear that the expression adopted by the programmer is the copyrightable element in a computer program.” H.R. Rep. No. 1476, 94th Cong., 2d Sess. 54, reprinted in 1976 U.S.C.C.A.N. 5659, 5670. Therefore, even if an element directs a computer to perform operations, the court must nevertheless determine whether it contains any separable expression entitled to protection. On appeal, Oracle does not—and concedes that it cannot—claim copyright in the idea of organizing functions of a computer program or in the “package-class-method” organizational structure in the abstract. Instead, Oracle claims copyright protection only in its particular way of naming and organizing each of the 37 Java API packages.[13] Oracle recognizes, for example, that it “cannot copyright the idea of programs that open an internet connection,” but “it can copyright the precise strings of code used to do so, at least so long as ‘other language is available’ to achieve the same function.” Appellant Reply Br. 13-14 (citation omitted). Thus, Oracle concedes that Google and others could employ the Java language—much like anyone could employ the English language to write a paragraph without violating the copyrights of other English language writers. And, that Google may employ the “package-class-method” structure much like authors can employ the same rules of grammar chosen by other authors without fear of infringement. What Oracle contends is that, beyond that point, Google, like any author, is not permitted to employ the precise phrasing or precise structure chosen by Oracle to flesh out the substance of its packages—the details and arrangement of the prose. As the district court acknowledged, Google could have structured Android differently and could have chosen different ways to express and implement the functionality that it copied.[14] Specifically, the court found that “the very same functionality could have been offered in Android without duplicating the exact command structure used in Java.” Copyrightability Decision, 872 F. Supp. 2d at 976. The court further explained that Google could have offered the same functions in Android by “rearranging the various methods under different groupings among the various classes and packages.” Id. The evidence showed, moreover, that Google designed many of its own API packages from scratch, and, thus, could have designed its own corresponding 37 API packages if it wanted to do so. Given the court’s findings that the SSO is original and creative, and that the declaring code could have been written and organized in any number of ways and still have achieved the same functions, we conclude that Section 102(b) does not bar the packages from copyright protection just because they also perform functions. [9] The Supreme Court granted certiorari in Lotus, but, shortly after oral argument, the Court announced that it was equally divided and that Justice Stevens took no part in the consideration or decision of the case. The Court therefore left the First Circuit’s decision undisturbed. See Lotus, 516 U.S. at 233-34. [10] The Lotus majority cited no authority for this definition of “method of operation.” [11] As Oracle points out, the Ninth Circuit has cited Lotus only one time, on a procedural issue. See Danjaq LLC v. Sony Corp., 263 F.3d 942, 954 (9th Cir. 2001) (citing Lotus for the proposition that delay “has been held permissible, among other reasons, when it is necessitated by the exhaustion of remedies through the administrative process . . . when it is used to evaluate and prepare a complicated claim”). [12] This analogy by the district court is meaningful because taxonomies, in varying forms, have generally been deemed copyrightable. See, e.g., Practice Mgmt. Info. Corp. v. Am. Med. Ass’n, 121 F.3d 516, 517-20 (9th Cir. 1997); Am. Dental, 126 F.3d at 978-81. [13] At oral argument, counsel for Oracle explained that it “would never claim that anyone who uses a package-class-method manner of classifying violates our copyright. We don’t own every conceivable way of organizing, we own only our specific expression—our specific way of naming each of these 362 methods, putting them into 36 classes, and 20 subclasses.” Oral Argument at 16:44. [14] Amici McNealy and Sutphin explain that “a quick examination of other programming environments shows that creators of other development platforms provide the same functions with wholly different creative choices.” Br. of McNealy and Sutphin 17. For example, in Java, a developer setting the time zone would call the “setTimeZone” method within the “DateFormat” class of the java.text package. Id. Apple’s iOS platform, on the other hand, “devotes an entire class to set the time zone in an application—the ‘NSTimeZone’ class” which is in the “Foundation framework.” Id. at 17-18 (noting that a “framework is Apple’s terminology for a structure conceptually similar to Java’s ‘package’”). Microsoft provides similar functionality with “an entirely different structure, naming scheme, and selection.” Id. at 18 (“In its Windows Phone development platform, Microsoft stores its time zone programs in the ‘TimeZoneInfo’ class in its ‘Systems’ namespace (Microsoft’s version of a ‘package’ or ‘framework’).”). Again, this is consistent with the evidence presented at trial. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 10-CV-3561 retrieved on September 22, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. law.justia.com

Oracle Concedes it Cannot Claim Copyright in the Idea of Organizing Functions of a Computer Program

’s highly anticipated brought forth exciting news as the tech giant unveiled PaLM 2, its latest large language model (LLM). With PaLM 2, Google aims to enhance its AI capabilities across various domains and provide developers with a powerful tool for language processing and understanding. Google I/O developer conference Here are the key takeaways from this groundbreaking announcement: PaLM 2 serves as the core technology behind Google’s updated Bard chat tool, directly competing with OpenAI’s renowned ChatGPT. Moreover, PaLM 2 acts as the foundation model for a range of new AI features that Google plans to introduce. By leveraging this advanced language model, Google seeks to empower developers with cutting-edge natural language processing capabilities. PaLM 2 is Powering Google’s Bard Chat Tool and Beyond: Google diverged from the traditional emphasis on model size and parameter counts, highlighting the importance of capabilities instead. Deepmind VP Zoubin Ghahramani stressed that larger models are not always superior. PaLM 2’s extensive training includes a vast array of math and science texts, enabling it to excel in common sense reasoning, mathematics, and logic. Notably, PaLM 2 showcases remarkable proficiency in solving math problems, providing diagrams, and reasoning through complex issues. Focusing on Capabilities Over Size: Recognizing the significance of programming languages, PaLM 2 received training on 20 different programming languages, encompassing popular choices like JavaScript and Python, as well as lesser-known languages such as Prolog, Verilog, and Fortran. Google introduces Codey, a specialized model built on PaLM 2, designed to aid in code writing and debugging. This advancement promises to streamline the coding process and enhance developers’ productivity. Enhanced Support for Code Writing and Debugging: PaLM 2 sets a new benchmark in multilingual language processing. Trained on a corpus encompassing over 100 languages, PaLM 2 surpasses its predecessors by demonstrating nuanced phrasing and a deep understanding of diverse linguistic nuances. This proficiency makes it invaluable for a wide range of multilingual tasks and applications. Multilingual Mastery: Google envisions PaLM as a versatile family of models. Alongside Codey, there are other variants tailored for specific use cases. Med-PaLM 2 focuses on medical knowledge, while Sec-PaLM addresses security-related scenarios. Google’s exploration of a smaller PaLM 2 model for smartphones could expand its applications to privacy-centric use cases, although specific timelines for its release remain undisclosed. A Family of Models: Google maintains its commitment to responsible development and safety in the AI landscape. The deliberate approach to launching AI features aims to ensure that the tools Google introduces meet rigorous quality standards and ethical considerations. PaLM follows this approach, although its performance and handling of edge cases still require real-world testing to ascertain its effectiveness. Responsible Development and Safety: As Google introduces PaLM 2, the tech community eagerly anticipates the impact of this next-gen language model. With enhanced capabilities in common sense reasoning, mathematics, code writing, and multilingual understanding, PaLM 2 paves the way for groundbreaking advancements in AI-powered applications across industries. Developers now have a powerful tool at their disposal, empowering them to create innovative solutions that leverage the immense potential of natural language processing and understanding. Also published here. The featured image for this piece was generated with Kadinksy v2 Prompt: Illustrate Google-powered AI

Unveiling PaLM 2: Key Insights on Google's Next-Gen Language Model 

What is Decentralized Science (DeSci)?

বিকেন্দ্রীভূত বিজ্ঞান (DeSci) কি?

Was ist dezentrale Wissenschaft (DeSci)?

¿Qué es la Ciencia Descentralizada (DeSci)?

Qu'est-ce que la science décentralisée (DeSci) ?

विकेंद्रीकृत विज्ञान (DeSci) क्या है?

分散型科学 (DeSci) とは?

분산 과학(DeSci)이란 무엇입니까?

O que é Ciência Descentralizada (DeSci)?

Merkezi Olmayan Bilim (DeSci) Nedir?

Khoa học phi tập trung (DeSci) là gì?

什么是去中心化科学 (DeSci)？

E-Petitioning In 2022

Peticiones electrónicas en 2022

Pétition en ligne en 2022

ई-याचिका 2022 में

2022年の電子請願

Petições eletrônicas em 2022

Yêu cầu điện tử vào năm 2022

2022 年的电子请愿书

From the Brink of Suicide to Bliss: How Hormones Destroyed and Saved My Life

Last Monday, a nurse suggested we try a wireless monitor to track my vitals and those of my unborn baby. the nurse told me. “We call this device “Monica, the monitor!” It’s either a dream to work with or a total nightmare,” On that day, “Monica” (actually the Novii Wireless Patch System) performed exceptionally well. I was able to move freely, without the encumbrance of wires, while giving birth to my daughter. This technology harnesses passive signal acquisition to differentiate between fetal and maternal heart signals and to detect uterine contractions. Data is wirelessly transmitted to a monitoring unit for real-time observation. This system enhances accuracy and reduces false alarms, offering so much-needed mobility during labor. I thought: that writing and theorizing about technologies is one thing, but experiencing their remarkable capabilities firsthand is quite another, especially when a device functions flawlessly. A question arose: What can foundation models add to wearables? Right after my experience with “Monica”, a recent paper from Google Research and MIT researchers caught my attention. Titled ‘ ,’ and authored by Kim et al., this paper delves into the application of LLMs in the health sector, focusing on interpreting data from wearable sensors for health prediction. Intriguingly, these models are fed data not from medical records or doctor’s notes, but from wearable devices like Fitbits, which track daily steps, heart rate, sleep patterns, and more — akin to ‘Monica.’ Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data The research evaluated eight cutting-edge LLMs: Med-Alpaca, PMC-Llama, Asclepius, ClinicalCamel, Flan-T5, Palmyra-Med, GPT-3.5, and GPT-4, across six public health datasets. They conducted experiments on thirteen health prediction tasks related to mental health, activity, metabolism, sleep, and cardiac assessments. The team experimented with various methods, including zero-shot and few-shot prompting (teaching the model with minimal or no examples), instructional fine-tuning (tailoring the model to specific tasks), and even some parameter-efficient fine-tuning for computational efficiency. Particularly fascinating is the effectiveness of context enhancement in prompts, which involves adding user context, health knowledge, and temporal information. This approach yielded up to a 23.8% improvement in performance. Healthcare is an exceedingly sensitive field, but the potential benefits of generative AI for humans are immense, especially with the power of foundation models. Health-LLM explores the future where wearables are not just passive trackers but proactive health guardians. Another recent groundbreaking paper in healthcare comes from Stanford and Stability AI researchers, titled . The most fascinating aspect of this paper is the development of CheXagent, an advanced foundation model specifically designed for interpreting chest X-rays. This model uniquely combines a clinical LLM, a specialized vision encoder, and a vision-language bridging network, demonstrating exceptional performance in interpreting complex medical images. Its ability to outperform existing models in accuracy and fairness evaluations marks a significant advancement in medical imaging AI technology. It can save so much time! And possibly lives. CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation (The newborn girl — Reason Leeloo Joy — sends her regards. We took a week off last week but are now back on track, exploring the AI world to understand how she and her four brothers will live in it and navigate it.) News from The Usual Suspects © Sam Altman and OpenAI OpenAI two new embedding models (text-embedding-3-small and text-embedding-3-large) and updated versions of GPT-4 Turbo, GPT-3.5 Turbo, and a text moderation model. The new embedding models represent content as numerical sequences, enhancing machine learning tasks like clustering or retrieval. They are also more efficient and cost-effective. released Meanwhile, Sam Altman is with Middle Eastern backers, including wealthy investors and chip fabricators like TSMC, to launch a new chip venture. This move aims to meet OpenAI’s growing semiconductor needs and reduce dependence on Nvidia. The venture’s structure is unclear, and it might be a separate entity or a subsidiary of OpenAI. in discussions Blackstone steps in Another big player is investing heavily in the AI revolution. Blackstone is building a $25 billion network of power-intensive data centers across America. Following its $10 billion acquisition of QTS, a major data center operator, Blackstone is developing massive facilities to meet tech giants' growing digital and AI demands. These projects, consuming electricity equivalent to millions of homes, are reshaping communities and sparking debates over resource use and local benefits. Despite challenges, including strained power supplies and public backlash, Blackstone views this venture as one of its potentially best investments, illustrating the increasing significance and complexity of data infrastructure in the AI era. Elon Musk, xAI and Tesla Elon Musk has been making headlines recently, seeking a from global investors in the Middle East, Hong Kong, Japan, and Korea. If successful, xAI’s valuation could reach $20 billion, surpassing Anthropic’s $18.4 billion but falling behind OpenAI’s $100 billion. However, Musk’s recent unless he secures 25% control has stirred dissatisfaction among current investors and might affect talks with potential new backers. Meanwhile, in a “Dojo” supercomputer at its Buffalo, New York facility, underscoring the company’s commitment to advancing AI technology. $6 billion investment for xAI threat to remove Tesla AI projects Tesla is planning a $500 million investment Google and Hugging Face The recently announced aims to make AI more accessible. It focuses on shared initiatives in open science and source, leveraging both Hugging Face’s open models and Google Cloud’s technology. The goal is to facilitate the development of AI technologies for a wider range of users and applications. partnership between Hugging Face and Google Cloud Meanwhile, Google Bard has on HuggingFace’s Chatbot Arena Leaderboard, overtaking GPT-4 and now only behind GPT-4 Turbo in the community-driven LLM rankings. ascended to the second position The freshest research papers, categorized for your convenience Model Compression and Efficiency : A technique for efficiently compressing large language models by removing parameters while retaining performance SLICEGPT →read the paper : Focuses on developing high-performing, multi-language code generation models with an extensive parameter range DeepSeek-Coder →read the paper : Introduces an efficient pre-training method for T5 models, reducing computational requirements SPACTOR-T5 →read the paper : A framework for accelerating large language model inference using multiple decoding heads MEDUSA →read the paper LLM Capabilities and Evaluation : Evaluates MLLMs for generalizability, trustworthiness, and causality across multiple modalities From GPT-4 to Gemini and Beyond →read the paper : Develops a multilingual LLM supporting over 500 languages, enhancing language model accessibility MaLA-500 →read the paper : Introduces a method for zero-shot detection of text generated by large language models Spotting LLMs with Binoculars →read the paper Multimodal and Specialized Models : Examines the decoding mechanism in masked autoencoders for improved image processing Rethinking Patch Dependence for Masked Autoencoders →read the paper : A comprehensive survey on the advancements and capabilities of multimodal large language models MM-LLMs →read the paper : Establishes a benchmark for evaluating large multimodal models in the Chinese context CMMMU →read the paper : Enhances vision-language models with advanced spatial reasoning capabilities SpatialVLM →read the paper AI Training and Data Generation Techniques : Explores training neural networks for universal prediction strategies, approaching Solomonoff Induction Learning Universal Predictors →read the paper : A Python library for flexible and reproducible data preparation in generative NLP Unitxt →read the paper : A method for generating high-quality, content-grounded synthetic data using large language models GENIE →read the paper : Investigates a token-free language model that learns directly from raw bytes MambaByte →read the paper : Enhances language models with a task-agnostic scaffolding technique for better performance Meta-Prompting →read the paper : An approach for aligning large language models with human preferences in reinforcement learning WARM →read the paper Language Models and Role-Playing : Presents a compact model integrating enhanced vision vocabulary for efficient visual information encoding Small Language Model Meets with Reinforced Vision Vocabulary →read the paper : Develops a method for role-playing dialogues using large language models Large Language Models are Superpositions of All Characters →read the paper : Introduces a collection of multilingual large language models for conversational applications Orion-14B →read the paper In other newsletters Great dive into Apple’s “Update on apps distributed in the European Union” from Hardcore Software Fun read from about Model Merging “When what seems like pure LLM black magic is supported by the literature” Interconnects Is This The Year Apple Awakens in AI? Madrona investors’ opinion. describes his experience in Davos and World Economic Forum. It’s about AI but in Ng’s signature humanistic style. Andrew Ng

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